Search

It is important to quantitatively analyze the driving mechanisms of vegetation cover changes and explore the relationship between vegetation and climate factors for regional ecological environment evaluation and soil and water conservation. Based on the normalized difference vegetation index(NDVI)in Ningxia from 1998 to 2019, the trend analysis, partial correlation analysis and variance decomposition of RDA were used to investigate the spatial-temporal variation characteristics of NDVI of different vegetation types and their driving factors. The results show that ① the annual growth rate of NDVI is 0.007 1 a-1, with the lowest NDVI growth rate in spring(0.002 3 a-1)and the highest NDVI growth rate in autumn(0.007 5 a-1); the NDVI growth rates of coniferous forests, shrub and desert are the highest in summer, and the NDVI growth rates of broad-leaved forests, farmland, grasslands and meadows are the highest in autumn; ② on the annual scale, 93.4% of the regional NDVI show an improving trend; within different seasons, the vegetation degradation is serious in spring, 16.6% of the vegetation area show a decreasing trend, which mainly occur in the yellow irrigation area of northern Ningxia, and the degraded areas in other seasons are concentrated around Yinchuan city; ③ climate is the main factor affecting the vegetation change, NDVI is positively correlated with temperature throughout the year and in spring, while NDVI and temperature are negatively correlated in summer, and NDVI and precipitation are positively correlated in different seasons. [ABSTRACT FROM AUTHOR]

Based on MODIS-NDVI data, SRTM DEM data and China meteorological data from 2000 to 2018 and using the methods of trend analysis, deviation analysis and correlation analysis, it quantitatively analyzed the spatial-temporal changes and the response relationship with climate factors of the NDVI in the Zijin Mountain area on spatial and temporal scales. the results show that a) from 2000 to 2018, the annual average growth rate is 0.015 / a (significance test p<0.01); b) the degree of the land desertification in the Zijin Mountain area has been improved significantly. The rules of land cover change are generally that the conversion of desertification land to arid grassland is completed in the first 9 years and the conversion of arid grassland to shrub land is completed in the later 10 years. The area of NDVI has been significantly improved by 64.85% in 19 years; c) the climate shows a tendency to warm and humidify, with the temperature increase rate of 0.04 mm / a and the precipitation growth rate of 14.63 mm/ a in 19 years; d) the annual average NDVI is significantly positively correlation with the annual precipitation (correlation coefficient r = 0.734, passed the significance test p<0.01), and 76.61% of the area has a significant positive correlation with the precipitation, which is not significantly correlated with the annual average temperature. Since 2000, the NDVI of vegetation in the Zijin Mountain area has generally been developed towards a good trend and the growth trend of NDVI in low-mid altitude mountain are as is particularly significant. Climate is the main factor affecting the change of NDVI in this area. [ABSTRACT FROM AUTHOR]

Both human activities and climate change have posed great impacts on the vegetation changes in the context of global climate change. It is necessary to identify the areas of man-made vegetation degradation for better environmental protection. However, only limited effort has been focused on distinguishing between the influences of yearly climatic factors and human activities on vegetation growth. Particularly, it is still lacking in the vegetation dynamics under the seasonal climate changes and anthropogenic activities in China. In this study, the spatiotemporal variations in the Normalized Difference Vegetation Index (NDVI) and climate change were investigated using the AVHRR NDVI3g data, land use data and gridded climate data, A multiple linear regression model was then used to determine the driving factors of vegetation change using residual analysis at the regional and national scales from 1982 to 2015. The results show that there was outstanding regional spatial characteristic of vegetation changes with a decreasing trend from the southeast to northwest China, where the annual rate of change ranged from -0.016/a to 0.029/a. An increasing trend was found in the temperature and solar radiation, whereas, the precipitation showed a downward trend in most regions. According to the Hurst index, 80.62% of the vegetation presented a continuous and stable change, while the grasslands and woodland tended to be unstable and vulnerable to climate change and human activities. The NDVIs of Junggar Basin, Qinghai-Tibet Plateau, and Xilin Gol League of Inner Mongolia were negatively correlated with the air temperature and solar radiation, whereas, the NDVIs of Yunnan-Guizhou Plateau, southern Loess Plateau, and Sichuan Basin were positively correlated. The residual analysis showed that the drought resulted in the vegetation degradation in the arid areas, such as Xinjiang Junggar Basin and central Inner Mongolia, while the increase of temperature and radiation was the main driver for the vegetation improvement in the eastern coastal plain, the southern Loess Plateau, Sichuan Basin, and the southwestern Yunnan-Guizhou Plateau. Since the beginning of the 21st century, human activities were gradually strengthened, including soil and water conservation, the conversion of farmland to forest on the Loess Plateau, the desert control in Alxa, as well as the construction of coastal city clusters and northeast industrial bases. Thus, the vegetation improvement was identified in the Alxa Plateau of Inner Mongolia, the central and northern Loess Plateau, and most parts of South China, whereas, the vegetation degradation was detected in the Greater and Lesser Khingan Mountains, the eastern regions of the Changbai Mountains and the Yangtze River Delta. Among them, the national projects of ecological protection were attributed to the significant improvement of vegetation conditions. Consequently, the monitoring of vegetation degradation can provide a theoretical basis for the implementation of ecological restoration and the construction of ecological civilization in the future. The finding can greatly contribute to regional green and sustainable development. [ABSTRACT FROM AUTHOR]

Time-lag effect of vegetation refers to the growth and distribution patterns out of sync with the varying climate. Vegetation cannot adapt immediately the climate change, leading to adjustment time. Previous research has focused mainly on the overall growth of vegetation under climate monitor or changes in a single type of vegetation. It is still lacking in the timelag effect of vegetation types and their response to the climate. Therefore, this study aims to determine the vegetation activity and its response to climate change. The lag time of different vegetation types was also clarified in response to the hydrothermal changes. The Normalized Difference Vegetation Index (NDVI) and meteorological grid data were monthly collected from 1982 to 2015. The GIMMS NDVI dataset was also selected, together with the CRU precipitation and air temperature dataset. Sen + Mann-Kendall trend analysis was then conducted to obtain the interannual change rate of vegetation NDVI in over the past 34 years. At the same time, a significance test was carried out on the NDVI change trend, in order to distinguish the significant improvement and degradation areas. The growth and degradation of different vegetation types were statistically determined after the test. The lagged partial correlation analysis was used to calculate the partial correlation coefficients between vegetation NDVI and precipitation and air temperature in the current month, the previous month, the first previous two months, and the first previous three months. Then, the partial correlation coefficients were also synthesized to obtain the maximum partial correlation coefficient. The lag time was set as the maximum partial correlation coefficient. The dynamic characteristics of the vegetation pattern change over the past 34 years were presented to determine the relationship between vegetation NDVI and climate response. The lag effect was obtained in the different vegetation types on climate response. The results showed that: 1) A spatial distribution pattern of vegetation was achieved in the higher vegetation in northern Xinjiang than that in the southern, and higher in the west than that in the east. There was an overall "greening" trend in the study area. Specifically, the significantly improved and stable unchanged areas accounted for 20.6%, and 65.7%, respectively. The areas insignificant improvement and degradation accounted for 3.3%, and 10.4%, respectively. There was no area with significant degradation. In the spatial distribution, a significant NDVI increase was found in the oasis around the Tarim Basin and the northern section of the Tianshan Mountains, whereas, the Ili region showed a trend of degradation. In vegetation types, the cultivated vegetation shared the largest improvement area, accounting for 75.3%, while, the degraded area of coniferous forest was the largest, accounting for 24.2%; 2) There was a lag response of 72% of vegetation areas to the precipitation at the monthly time scale, with an average lag time of 1.1 months, while the lag response of 70% of vegetation areas to air temperature, with an average lag time of 1.4 months. The higher the lagged correlation coefficient between vegetation and climate elements was, the higher the response speed was. Overall, the vegetation was much more sensitive to the precipitation. 3) Different types of vegetation varied greatly in response to the precipitation and air temperature. Precipitation was the main promoting factor for the grassland, shrubland, and coniferous forest, while air temperature shared the strongest impact on the broad-leaved forest. The lagged partial correlation coefficients between different vegetation types and precipitation were higher than those of air temperature. The response time of different vegetation types to air temperature was longer than that of precipitation. There were lagged effects in the response of different vegetation to both precipitation and air temperature. The correlation between vegetation and precipitation was higher in Xinjiang, indicating a more rapid response of vegetation to precipitation than that to air temperature. [ABSTRACT FROM AUTHOR]

The southwest alpine canyon area is one of the most typical ecologically fragile areas in the world. It is a high demand to explore the spatiotemporal variations of vegetation and its response to climate change and human activities in this region. The relative contributions of climate change and human activities can also be quantified for ecological restoration. Among them, the normalized difference vegetation index (NDVI) can be expected to serve as the potential indicator for the vegetation dynamic growth conditions, due to the high sensitivity to the vegetation. In this study, the NDVI dataset was collected to examine the spatiotemporal variations from 2000 to 2019. The future trends of NDVI were then predicted using the Theil-Sen trend analysis, Mann-Kendall significance test, and Hurst index in conjunction with the meteorological factor datasets. A correlation and residual analysis were finally conducted to explore the response of NDVI to climate change and human activities. The results showed as follows: 1) The overall NDVI from 2000 to 2019 showed a fluctuating upward trend on the temporal scale, with a growth rate of 0.004 6/a. The proportion of areas with an upward trend in the NDVI was 85.59% on the spatial scale, indicating significant vegetation recovery. However, some localized areas with a downward trend of NDVI accounted for 14.41% of the study area. The changing trend of NDVI continued to be predominantly upward in the future, where 82.34% of the study area was an upward trend, while 17.66% was a downward trend. A significant decrease in NDVI was found in the junction area between Changdu and Linzhi City in the Tibet Autonomous Region. There was also a risk of a further decrease in the NDVI in this area in the future. 2) The temperature and solar radiation increased at the rates of 0.02°C/a and 6.92 MJ/(m² ·a), respectively, against the backdrop of regional climate warming and drying. But the precipitation decreased at a rate of −2.97 mm/a. The NDVI showed different responses to the various climatic factors, with a positive correlation with the temperature and solar radiation overall, but a negative correlation with the precipitation. Among them, the temperature posed a much stronger influence on the NDVI change than solar radiation and precipitation, indicating the dominant climatic factor on NDVI change. 3) Both climate change and human activities shared positive and negative effects on vegetation change. Specifically, a positive trend was found in most cases. Vegetation change in 85.10% of the study area was influenced by both climate change and human activities. Human activities were taken as the first driving factor, and climate change was the secondary driving factor, with relative contribution rates of 68.67%, and 31.33%, respectively. The finding can provide a scientific basis for the ecological and environmental construction in regional green and sustainable development. Keywords: remote sensing; plants; climate change; human activities; normalized difference vegetation index; residual analysis; southwestern alpine canyon area [ABSTRACT FROM AUTHOR]

Soil management zoning can normally use weighted environmental variables to obtain the weight of environmental factors. This approach relies mainly on the crop yield to acquire the weights of environmental variables, in order to establish the relationship model between the crop yields and environmental variables. Thus, the current zoning cannot fully meet the requirement of modern agriculture in recent years, particularly when the crop yields are unavailable or there is no relationship between them. Fortunately, the normalized difference vegetation index (NDVI) can be expected to reflect the soil condition, which was significantly influenced by the environmental variables. In this study, an accurate and efficient approach was developed to obtain the weights of environmental variables using NDVI. The relation model between NDVI and environmental variables was then established in this case. Three steps consisted of this approach. Firstly, the key environmental variables were selected to significantly impose the influence on NDVI. The relation model was then established between NDVI and the selected key environmental variables. Finally, the weights of environmental variables were acquired from the established relation model. A case study was applied to test the effectiveness of the approach. Specifically, the weights of environmental variables were acquired for a rubber plantation in Danzhou County, Hainan Province, China. Among them, the candidates were taken as the 10 terrain attributes, 19 bioclimatic variables, and 5 parent materials types. Furthermore, 15 key environmental variables were determined to construct the relationship model with the NDVI using the random forests (RF). The importance index (IncMSE) was extracted from the fitted relation model, and then calculated the weight of each key environmental variable. These acquired weights were also used to weigh the environmental variables, and then to serve as the input variables for the K means clustering. Finally, six groups of soil management zones were generated for the rubber plantation. A total of 1296 top soil (0-20 cm) samples were employed to verify the soil management zones. Results indicated that the management zones better distinguished the abundance and deficiency levels of different soil nutrients (soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), and available potassium (AK)). Zone 1 presented the high AP content, the normal range of pH value and SOM content, as well as the TN and AK in deficiency. Zone 2 shared the high AP content, pH value suitable for the rubber tree growth, but the SOM, TN, and AK in deficiency. Zone 3 demonstrated a very high AP content and a pH value within the normal range. Nevertheless, the contents of SOM, TN, and AK were all lower than the normal range in Zone 3. More importantly, the contents of SOM, TN, and AK in Zone 4 were similar to those in Zone 3. However, the values of pH and AP were significantly lower than that in Zone 3. In Zone 5, the values of pH and AK were the highest in the normal range among the six zones, while the values of SOM and TN were the lowest in deficiency. In Zone 6, the soil TN content was the highest among the six zones within the normal range. The pH value and AP were also within the normal range, but the AP content was close to the lower limit of the normal range, indicating the insufficient content of SOM and AK. At the same time, the mean differences were all significant at P < 0.05 level in 15 selected environmental variables among different management zones. Consequently, the soil management zones further verified the effectiveness of the approach, where the weights of environmental variables were acquired by the NDVI. Anyway, the NDVI acquiring the weights of environmental variables can be applied to much wider ranges, because the NDVI can be much easier to access than the crop yields in practice. [ABSTRACT FROM AUTHOR]

Alfalfa is a key feed variety for the development of herbivorous animal husbandry, which is of great significance to the development of animal husbandry and food safety in China. Timely and accurate acquisition of alfalfa spatial distribution information is helpful to provide scientific data support for the development and management of grass production. This study was based on high-resolution GF-1/WFV and Sentinel-2 remote sensing images in Jinchang City, Gansu Province, a Normalized Difference Vegetation Index (NDVI) dataset was constructed, combined with the change of spectral reflectance of alfalfa with the growth stage, an algorithm of extracting the remote sensing characteristics of alfalfa using the Findpeaks function of MATLAB was proposed. By determining the value of Minimum Peak Prominence (MPP), the limitation of automatic identification and area extraction of alfalfa was solved, and the spatial distribution information of alfalfa in Jinchang City was extracted. Firstly, the time series of NDVI of alfalfa was analyzed, and it found that NDVI of alfalfa increased many times as peak value decreased in one year. The NDVI time series curve had many peaks and troughs, among which the peak represented the high value of NDVI in a growing period, which was the flourishing period of alfalfa growth and development, and the trough reflected the alfalfa from the peak period to the cutting state. According to the field investigation, the number of peaks and troughs of alfalfa was determined, and the number of troughs on the NDVI time series curve was 3-4, and the number of peaks was 3-5. Combined with the verification results of position accuracy found that the classification accuracy increased when MPP was in the range of 0.30-0.40 and reached the maximum value when MPP was 0.40, while the classification accuracy tended to decrease with the increase of MPP. Therefore, 0.40 was the most reasonable value of MPP to extract the information of alfalfa in Jinchang City, and the potential spatial distribution results of alfalfa were obtained by using the Findpeaks function of MATLAB software. The spatial distribution data set of alfalfa planting area in the study area was established by masking the terrain characteristics and the spatial distribution data of cultivated land, removing forests, and other land objects. Finally, the spatial distribution of alfalfa in Jinchang City in 2019 was obtained by using ENVI software to carry out classification post-processing such as multiplicity filtering and fragment elimination. The results show that: 1) The recognition accuracy of Sentinel-2 remote sensing data was better than that of GF-1/WFV remote sensing data. The recognition accuracy and Kappa coefficient of Sentinel-2 data were more than 85% and 0.7, mainly because the density of the NDVI time series curve of Sentinel-2 data was larger than that of GF-1/WFV data, which could better capture and identify the key time points of alfalfa. 2) The find troughs method has more advantages in alfalfa identification in the study area, and the overall accuracy, Kappa coefficient, user accuracy and mapping accuracy of alfalfa identified by remote sensing were higher than those of find peaks method. 3) The find troughs method based on Sentinel-2 image was the best method for alfalfa remote sensing recognition, with an overall accuracy of 92.25%, the Kappa coefficient of 0.81, and the position accuracy of 86.44%. It had a good monitoring effect in terms of spatial location. 4) The spatial distribution of alfalfa in Jinchang City showed a trend of increasing gradually from north to south, most of the continuous areas were mainly concentrated in the south-central and southwest of Jinchang City, and there was only sporadic distribution in the north. The alfalfa planting area of Jinchang City identified by find troughs method based on Sentinel-2 images was 15 449.07 hm² in 2019, of which the alfalfa area of Jinchuan district was 1 353.42 hm², accounting for 8.76% of the total alfalfa area of Jinchang City, and the alfalfa area of Yongchang county was 14 095.65 hm², accounting for 91.24% of the total area. The research results confirmed that the find troughs method based on Sentinel-2 remote sensing data could effectively identify alfalfa, which had important practical significance for the refined and precise management of pastures and the precise monitoring of pasture production information. [ABSTRACT FROM AUTHOR]

[Objectives] Aiming at the problem that the vegetation index in the early stage of wheat growth is easily disturbed by soil background, a wheat canopy vegetation index measurement method based on narrow band spectral image analysis was proposed. [Methods] A narrow-band spectral image acquisition device with multi-lens structure was constructed to obtain 656 and 770 nm narrow-band spectral images of field wheat in real time. Simple linear iterative clustering (SLIC) and visual geometry group network 16 (VGG16) full convolution neural network were used for super-pixel clustering and classification of wheat near-infrared narrow-band spectral images. Using intersection over union Qseg, comprehensive evaluation index (F value) and Precision as segmentation accuracy evaluation indexes, the performance difference between traditional threshold segmentation method and the proposed method to remove soil background interference was analyzed. The sunlight free whiteboard calibration method was used for the narrow-band spectral image after removing the soil background to calculate the vegetation index. The calculated vegetation index took the measured data of GreenSeeker RT200 as a reference to qualitatively and quantitatively evaluate the performance of the proposed method to remove the soil background interference. [Results]The images of 12 wheat varieties, two nitrogen application levels and 24 planting areas were collected. The average value of Qseg, Precision, F were 90.41%, 80.82% and 72.73%, respectively. The segmentation performance was better than the traditional threshold segmentation method. For the same test field, the maximum variation coefficient, average value and standard deviation of normalized difference vegetation index (NDVI) of each area measured by GreenSeeker RT200 were 47.12%, 33.61% and 10.17% respectively, while that of NDVI of each area measured by the proposed method were 18.59%, 9.61% and 3.88%, respectively. Moreover, the vegetation index extracted by this method had a high correlation with the measurement results of GreenSeeker RT200 after the closure stage of wheat in the test plot, and the determination coefficient was 0.895 9. Hence, the calculated NDVI could effectively characterize the nutritional status of wheat varieties under different fertilization levels. [Conclusions] The proposed method can complete the canopy extraction and vegetation index measurement of wheat narrow-band spectral images under complex soil background and field illumination change, and can provide a reference for the optimal design of crop canopy reflectance spectrometer with multi-lens structure. [ABSTRACT FROM AUTHOR]

High-resolution precipitation data can be directly used to characterize the spatial-temporal differentiation features of precipitation after the accurate simulation of hydrological, meteorological, and biological systems. Therefore, it is crucial to implement the spatial downscaling for the precipitation products with the low spatial resolution. However, it is still lacking on the precision and detail features in the downscaling precipitation data, due to fail to consider the scale variations in the spatial distribution of precipitation. In this study, a spatial downscaling approach was proposed to improve the TRMM precipitation data in the Wei River basin (WRB). A Multi-scale Geographically Weighted Regression (MGWR) model was also integrated to enable the conditional relationships between the response and predictor variables that changed at the spatial scales. Therein, the goodness of fit (R 2 ), relative deviation (BIAS), and Root Mean Square Error (RMSE) were employed to verify the TRMM satellite precipitation product, compared with the actual precipitation data from meteorological stations. Normalized Difference Vegetation Index (NDVI), digital elevation model (DEM), slope, aspect, latitude and longitude were induced as the Geographic Environmental Factors (GEFs). The MGWR models with the monthly TRMM precipitation data were constructed to further investigate the scale effects of factors on precipitation distribution. The spatial downscaling of TRMM production data was then implemented using a scale conversion process. Finally, the reliable spatial downscaling was achieved in the TRMM precipitation products. The results illustrated that: 1) The TRMM precipitation data was better suited for use at different scales in the WRB. An acceptable fitness was found in the statistics of R 2 (0.807), BIAS (2.909%), and RMSE (83.477 mm) at the annual scale. Specifically, the maximum R 2 was 0.847 at the seasonal scale, the largest RMSE was 62.393 mm in the summer, and the BIAS values were lower in all four seasons. More importantly, the R 2 varied between 0.456 and 0.815 on the monthly scale, with the smallest value in June and the largest value in September. The BIAS was positive in the most months, indicating that the TRMM product data generally overestimated the precipitation. The RMSE index was fallen in the range of 3.507-39.342 mm, which was lower than those on the annual and seasonal scales. 2) Different scale characteristics were found in the influence of various GEFs on the spatial pattern of precipitation divergence in wet and dry years. Slope was set as the global scale, whereas the DEM, NDVI, aspect, latitude, and longitude were the local effects on the precipitation in wet years, and all GEFs were used the local impacts in dry years. 3) The more precise data was obtained in the downscaled TRMM on the watershed and station scales, compared with the product data, indicating an increase in the R 2 of the entire watershed of 3%, while a decrease in the RMSE of 1 mm. However, the accuracy of station downscaling precipitation data at the annual scale was worse than that at the monthly scale, due to the accumulation of errors on the time scale, as shown by the R 2 range decreasing from 0.8-0.91 (monthly) to 0.4-0.95 (yearly), while the RMSE range increasing from 11–17 (monthly) to 32–150 (yearly). 4) The downscaled TRMM data presented the better detailed characteristics, the greater precision at annual and monthly scales, and a much more delicate geographical distribution than the product data. The finding can provide a strong data support for the hydrological design in the areas with less precipitation data. [ABSTRACT FROM AUTHOR]

An accurate and universal monitoring model of nitrogen nutrition can greatly contribute to the precise management and application of regional rice. In this study, a systematic investigation was carried out to explore the impact of the images on the monitoring models. The consumer-grade multispectral images were captured by unmanned aerial vehicles (UAV) from the different ecological sites and rice varieties. A series of field experiments were conducted at two test sites: Mengzhe Town, Xishuangbanna, Yunnan Province (with trial variety Yunjing 37) and Beibei District, Chongqing City (with trial variety Jiyu 6135) in South China, with the varying nitrogen levels. Four multispectral drones (DJI Phantom) were used to capture the multispectral images of the rice canopy during tillering, jointing, and heading stages. The nitrogen content in rice plant canopies (CNC) was measured to calculate the above-ground nitrogen accumulation (PNA) using the Kjeldahl method. The nitrogen nutrition monitoring models were established using vegetation indices, partial least-squares regression (PLSR), random forest (RF), and backpropagation neural network (BPNN) for the single trial site, single variety, different trial sites, and multiple varieties of rice. The transferability was also explored in the models. The monitoring models were established for the CNC and PNA during each growth stage of rice for the single trial site and single variety with the high accuracy (normalized difference vegetation index NDVI or near-infrared normalized vegetation index NNVI, where the coefficient of determination was 0.68-0.88). However, the inaccurate model was obtained with the vegetation index during the tillering stage (NDVI, the coefficient of determination was 0.53-0.79), indicating the low transferability of all models. It was difficult for the vegetation indices to construct the monitoring models for the canopy nitrogen content throughout the entire growth period of rice. But a monitoring model was established for the above-ground nitrogen accumulation of single-variety rice in the period of the growth using the ratio vegetation index, the high accuracy and the high transferbility of the model. The monitoring models constructed by PLSR, RF, and BPNN were more accurate than those by vegetation indices. Among them, the highest accuracy was achieved in the monitoring model for the canopy nitrogen content and above-ground nitrogen accumulation in the entire growth period of multiple varieties using RF, with the coefficient of determination values were 0.84 and 0.94, respectively, and root-mean-square errors of 0.28% and 10.09 kg/hm², respectively. The finding can provide a theoretical basis and technical support for the application of consumer-grade multispectral images captured by drones in monitoring rice nitrogen nutrition during the entire growth period of different ecological sites and rice varieties. [ABSTRACT FROM AUTHOR]

Global warming and human activities have posed a great threat to the climatic elements in drylands. The very fragile ecosystem is also highly sensitive to natural and anthropogenic disturbances. It is a serious risk to the evolutionary direction and habitability of oases in recent years. Among them, the Land Surface Temperature (LST) is one of the most important parameters to evaluate the interaction and energy exchange between the earth and the atmosphere. The spatial pattern of the surface temperature and the thermal environment in a region can be attributed to the combined effect of the regional small terrain factors and the properties of underlying surfaces. It is a high demand to explore the spatial distribution of the LST and the "source-sink" effect in the dryland oases, particularly for the local climate change patterns and the influencing factors in the arid regions. However, the oases cold island has put a great challenge on the temperature inversion and high-temperature center using remote sensing in the large-scale basins or open terrain regions. This study aims to analyze the spatial distribution of LST and the influencing factors in the Yanqi Basin in Xinjiang of China. The Mono-window algorithm was also utilized in this case. A quantitative analysis was performed to determine the contribution of the “source-sink” effect in the different land use/cover types to the LST change. The results showed that: 1) The land use/cover type was determined by the spatial distribution pattern of surface temperature and the “source-sink” effect. There was the strongest heat source in the unused lands. The strongest heat sink was observed in the water bodies. There was little influence of grasslands and construction lands on the land surface thermal environment. The intensity of the heat sink effect in the croplands depended mainly on the area of agricultural irrigation and crop growth conditions. 2) The Normalized Difference Vegetation Index (NDVI) was negatively correlated with the LST. The surface temperature decreased by about 1.98 to 2.66 for every 0.1 increase in NDVI. ℃ However, more effects of the vegetation were found on the surface temperature in the cultivated, grassland, and unused land. Less effect of the vegetation was observed in the wetlands. There was also a smaller cooling effect on the vegetation in the construction lands, due to the relatively small proportion of green space in the towns and cities. 3) The slope presented the most significant impact on the gentle slope areas less than 13° at the lower elevation in the basin. The LST increased rapidly with the increase of the slope. There was a weaker effect of slope on the LST at altitudes higher than 1 252 m. The LST decreased gently with the increase of slope. 4) The cold island effect also created a unique thermal structure of the near-surface atmosphere over the Yanqi Basin Oasis. Specifically, the surface temperature rose rapidly at first, with a high-temperature center at the height of 100 to 300 m from the ground, and then gradually decreased, as the altitude rose. The atmospheric temperature inversion layer was generated on June 22. The thickness then exceeded 1 000 m on July 1. The redistribution of the slope direction on the solar radiation disappeared within the height range of 100-300 m above the Yanqi Basin Oasis surface. This absence was attributed to the high-temperature center. This finding can fill the gap in the observation data in the Yanqi Basin in Xinjiang of China. [ABSTRACT FROM AUTHOR]

Sediment connectivity can be an indicator to identify the erosion hotspots and sediment migration paths, particularly for the potential linkage of sediment sources and sinks in watersheds. It is a high demand to determine the influencing factors for the spatial and temporal variability of sediment connectivity. In previous studies, the functional factors are simply added to calculate the connectivity index, but it is still lacking the difference between the connectivity indexes rich in functional factors and the original ones. At the same time, the connectivity index is mainly concentrated in the Loess Plateau in China, where the time scale is also dominated by the annual scale. The vegetation, terrain, and climate conditions vary greatly in different regions. For instance, the Loess Plateau belongs to the continental monsoon climate, most of which are semi-arid areas dominated by bare land and gullies. By contrast, the study area of watersheds belongs to the subtropical monsoon climate with an average annual rainfall of 1 291 mm for many years rich in vegetation and surface types. Moreover, the slow change of vegetation can be mainly determined by human factors on the annual scale, while there are four distinct seasons for the vegetation on the seasonal scale. Therefore, it is necessary to clarify the sediment connectivity in the different regions on the various time scales. In this study, the revised index of sediment connectivity (ICr) was adduced to explore the influence of seasonal and inter-annual variation of vegetation coverage and rainfall erosivity on the sediment connectivity considering the functional factors. The study area was selected as the Zhongtianshe River watershed in the Tianmu Lake. The connectivity index was firstly calculated in the summer and winter of 2019, according to the formula of the original sediment connectivity index (IC) model. The data show that the fluctuation range of IC value was from -12.71 to 0.09, with the lowest IC value in summer and the highest in winter. The weighting coefficient (W) in the original IC was directly replaced by the Rainfall Erosivity Factor R to obtain the index IC-R, in order to verify the introduction of rainfall erosivity. There was a strong correlation with the measured sediment volume of the silver carp bridge sub watershed (r = 0.85). Finally, the improved index ICr was obtained to couple the vegetation and rainfall factors. The mean value of ICr and sediment demonstrated the same change trend (r = 0.91) for the inter-annual connectivity index in 2016, 2018, 2019, and 2020, although there was no outstanding change for the seasonal changes within the year. The results show that the vegetation coverage values were 85% and 57% in the summer and winter of 2019, respectively, where the mean values of sediment connectivity index were -9.39 and -6.85, respectively. It infers that there was an important influence of vegetation coverage on the sediment connectivity. The Normalized Difference Vegetation Index (NDVI) was used to obtain the surface integrated coefficient in the model, which dynamically represented the regional and seasonal changes in surface vegetation and land use. There was also a significant influence of rainfall on the functional connectivity of sediment, where the correlation coefficient was 0.91 between the mean connectivity index on an annual scale and the sediment yield in the basin. Especially, a dominant role of rainfall factor was found, when the less vegetation coverage in the watershed or woodland. The vegetation coverage increased by 28%, whereas, the IC mean value decreased by 37%, indicating a similar trend in the IC-R mean value and rainfall. when considering the rainfall factors alone. The weights of the vegetation and rainfall factors in the improved connectivity index ICr can also be expected to adjust appropriately in the application for the actual situation of the watershed. Consequently, the sediment connectivity index can be feasible in the southeastern region of China. The finding can provide a scientific reference for soil and water conservation and water environment management in the small watersheds of lakes in humid areas under the background of climate change. [ABSTRACT FROM AUTHOR]

Monitoring the Crop Water Stress Index (CWSI) is of great significance for the water status and irrigation in crop production. Taking the Brassica chinensis as the test object, this study aims to measure the canopy temperature under different soil moisture conditions. Some meteorological parameters were collected, including the air temperature, relative humidity, wind speed, and photosynthetic active radiation. Meanwhile, the images of spectral reflectance were also collected for the four bands (450, 650, 808, and 940nm). Four vegetation indexes were then calculated by the canopy spectral reflectance, including the Normalized Difference Vegetation Index (NDVI), Difference Vegetation Index (DVI), Re-Difference Vegetation Index (RDVI), and Optimized Soil-Adjusted Vegetation Index (OSAVI). Support Vector Regression (SVR) was selected to construct the inversion models of the CWSI upper/lower baseline using the meteorological parameters, and the inversion models of the canopy temperature using the vegetation index. The results showed that the canopy spectral reflectance at 450 and 650 nm for the Brassica chinensis ranged from 0 to 0.1, while the relatively higher one at 808 and 940 nm ranged from 0.4 to 0.6. The reflectance at 808 and 940 nm increased outstandingly, when the Brassica chinensis was developed gradually from the vegetative to reproductive growth stage. The vegetation index reflected the growth state and vegetation coverage of the Brassica chinensis. There was a different response of vegetation indexes to the canopy temperature. The vegetation NDVI, DVI and RDVI increased, while the vegetation OSAVI decreased with the increase of the canopy temperature of the Brassica chinensis. The vegetation index under the same water treatment was slightly different in the various growth stages. Specifically, the range of the vegetation index in the reproductive growth stage was smaller than that in the vegetative growth stage. The error analysis showed that the inversion models were feasible to monitor the air temperature, relative humidity, wind speed, and photosynthetic radiation, further invert the upper/lower baseline of CWSI with the determination coefficient greater than 0.75. In the light of the error analysis of the inversion models, the vegetation index was inverted the canopy temperature of the Brassica chinensis in the vegetative and reproductive growth stage, indicating an excellent accuracy with the determination coefficient greater than 0.7. The calculated CWSI using the inversion models presented a significant correlation with the using the measurement, while the determination coefficient was equal to 0.70. And the CWSI showed the negative relationship with the stomatal conductance with the determination coefficient equal to 0.53. The meteorological parameters were used to invert the upper/lower baseline of CWSI, where the vegetation indexes were used to invert the canopy temperature. The inverted values using the SVR model shared the better fitting performance. The finding can provide a strong support for the spectral monitoring of the crop water stress index of the Brassica chinensis. [ABSTRACT FROM AUTHOR]

Citrus has been widely planted in the south of China in recent years. However, the multiple crop types and frequent cloud cover locally have posed a great challenge to the direct identification of the large-scale citrus orchard using the spectral information. In this study, a systematic approach was developed to identify the citrus orchard using the phenological characteristics of citrus during the fruit expansion stage, where the threshold value was determined to the key time window. Taking the Wuming District of Nanning Guangxi Zhuang Autonomous Region in southwest China as the study area, an empirical investigation was carried out using the Google Earth Engine platform. 1 751 ground samples were also collected in the field for validation. Meanwhile, the cloud coverage assessment was performed on the Sentinel-2 images over the whole year of 2018. According to the citrus phenology in the study area, the characteristics of flowering were not outstanding in the first half of 2018 (the flowering period of citrus), while the key identification features were found in the second half of 2018 (the peak stage of citrus fruit growth and expansion). As such, the second half of 2018 was determined as the study period, considering the data availability and citrus phenological stage. Then, a multi-temporal image dataset was obtained from August to December. The specific procedure was as follows. Firstly, some indices were calculated using the time series Sentinel-2 data in 2018, including the multiple vegetation indices (e.g. Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Difference Vegetation Index (DVI)), and Sentinel-derived Red-Edge Spectral Indices (RESI). The vegetation information of multi-temporal remote sensing was then obtained for the entire study area. Secondly, the vegetation information of various planting types was compared in different growing periods, according to the measured ground data. The results showed that there was no outstanding spectral difference between the citrus orchards and other vegetation types (e.g. sugarcane, banana, maize, and rice) in the study area. However, the vegetation indices of multi-temporal remote sensing presented that the NDVI of 0.47 for the citrus orchards was distinctly lower than that for the other vegetation types in October. There was also a lower GNDVI of citrus orchards in October and November. But there was no difference in the DVI of citrus orchards from other vegetation. Furthermore, the fruit expansion stage of citrus was located from September to October, indicating weak vegetation information of citrus. Nevertheless, there were significant differences among different vegetation indices, where the NDVI presented the highest. In addition, a renormalization of vegetation indices was further constructed to identify the spatial distribution of citrus in the study area by a threshold, according to the NDVI in October. The overall accuracy of citrus orchards reached 82.75% using the renormalization of NDVI, where the Kappa coefficient was 0.66, indicating a better identification, compared with the rest of renormalized vegetation indices (GNDVI, DVI, and RESI). The NDVI presented more complete identification for the citrus orchards, whereas, the GNDVI performed more fragmented identification. Consequently, the total area of citrus planting was calculated as 3.42×104 hm2 using the phenological parameters of citrus orchards. This finding can provide strong theoretical and practical support to timely mapping the citrus orchards using remote sensing. [ABSTRACT FROM AUTHOR]

Vegetation is one of the critical environmental factors driving the hydrologic cycle. The impact of vegetation change on runoff is a hot issue. As a pronounced greening region, the Huang-Huai-Hai River basin (HHHRB) was selected as the studying area. Based on long- term (1982—2016) hydro- meteorological and Normalized Difference Vegetation Index (NDVI) datasets, the spatial and temporal change in NDVI was detected using Mann-Kendall′s test methodology. The relationship between NDVI and the parameter ω of Budyko-Fu′s model was analyzed using an empirical formula and an elasticity method. Taking the parameter ω as a link, the impact of NDVI change on runoff in HHHRB was investigated with the chain rule for derivatives of complex functions. Several new findings were investigated: ① There was a statistically significant increasing trend in NDVI during the last 35 years over HHHRB. ② An increase in NDVI would increase the model parameter ω, thereby leading to a decrease in the runoff. ③ There might be an average reduction of 8. 3% in runoff as a 10% increase in NDVI in the HHHRB. ④ The runoff was more sensitive to NDVI change under drier climate and sparser vegetation conditions. The results could improve the understanding of the mechanism of the water cycle in a changing environment and might provide scientific and technological support for water resources planning and vegetation management. [ABSTRACT FROM AUTHOR]

The winter crop has been one of the important crop types in China. Accurate and timely spatio-temporal distribution of planting area directly determines the grain output and economy, as well as the national food security. Taking the Huaihe River Basin as an example, this study aims to extract the planting areas of winter crops according to the phenology period using the Google Earth Engine cloud platform and the fusion of Landsat-7/8 and Sentinel-2A/B images. Firstly, a dataset of time-series images was constructed with a spatial resolution of 30m. A CFMask algorithm was selected to preprocess the images, thereby calculating the Normalized Difference Vegetation Index (NDVI). More importantly, the maximum NDVI of all high-quality images within 10 days was used to obtain time-series data with equal time intervals. The linear interpolation was utilized to fill the pixels without high-quality images. Savitzky-Golay (S-G) filtering (a second-order filter with a moving window of 9 observations) was adopted to smooth the NDVI time series for the removal of noise. As such, a smoothed NDVI time series was obtained with a 10-day interval. Secondly, the peak growth, sowing, and harvest periods were determined to select sample points of winter crops with different spatial distributions, according to the NDVI time series. Subsequently, the winter crops were sowed in mid-late October, when the NDVI values were the lowest. The NDVI values gradually increased, after the emergence of seedlings in early November. The crops stopped growing in January during the overwintering period, where the NDVI stayed the same over the whole period. Furthermore, the NDVI resumed growing and gradually reached the peak growth period, when the winter crops turned green in February. After that, the NDVI reached the peak at the heading stage, and then gradually decreased. Correspondingly, the NDVI dropped to the bottom, when the harvest was over from the end of May to June. According to these characteristics in the process of winter crops growth, the peak growth period was determined from March 20, 2018, to April 20, 2018, the sowing period was determined from October 11, 2017, to November 10, 2018, and the harvest period was determined from May 20, 2018, to June 30, 2018. Particularly, the maximum NDVI was achieved in the peak growth period and the minimum and median of NDVI in the sowing and harvest period. Finally, the classification model of a decision tree was constructed, according to the NDVI boxplots of winter crops and non-winter crops at different time periods. The planting area map of winter crops was also generated for the Huaihe River Basin. The results showed that the planting area of winter crops was 8.762×106 hm² in the Huaihe River Basin in 2018. Specifically, the user accuracy was 0.926, the producer accuracy was 0.970, the total accuracy was 0.958, and the Kappa coefficient was 0.912. Consequently, the large-scale planting area of winter crops was extracted accurately for the decision-making in similar areas. [ABSTRACT FROM AUTHOR]

Since 1999, China has implemented the Grain for Green Project(GFGP), which has significantly changed the vegetation coverage status and land use structure of the Loess Plateau. As a result, vegetation coverage and ecological quality have been greatly improved. However, the spatio-temporal trend of the vegetation coverage is very uncertain, while the comprehensive assessment of its ecological effects has been relatively weak. To this end, two vegetation indices, including normalized difference vegetation index(NDVI)and fractional vegetation cover(FVC), were analyzed, and the changes in vegetation coverage and distribution on the Loess Plateau before and after the implementation of GFGP in the past forty years were reviewed. The results show that ① comparing five common data sets that record the dynamic changes of vegetation coverage, it is found that the data sources affect the authenticity of the eco-environmental benefits' evaluation results to a certain extent; ② the overall vegetation coverage of the Loess Plateau has been increasing in a fluctuating manner over the years, whose growth rate has surged due to the implementation of the project; its spatial distribution decreases from southeast to northwest, and is influenced by the ecosystem pattern, land use type, climate change, etc.; ③ with a rise of vegetation coverage, the water and soil conservation, and carbon sequestration capacity of the Loess Plateau ecosystem also gradually make a build-up, both of which are highly coupled with vegetation changes. According to the current situation of eco-environment management and food production level, it is recommended to strengthen the construction of high-standard farmland to alleviate the problem of arable land shortage caused by GFGP. Meanwhile, it is necessary to accelerate the promotion of ecological value transformation and eco-compensation, so that the lucid water and lush mountains can truly become invaluable assets. [ABSTRACT FROM AUTHOR]

Heavy metal pollution has become the focus of environmental biology and crop quality and safety. In order to obtain the cadmium content of tobacco leaves quickly and accurately, four cadmium pollution levels were simulated. The spectral reflectance of tobacco leaves in each treatment was obtained using an American ASD spectrometer, and the cadmium content of tobacco leaves in different periods was measured. The sensitive bands with the best correlation with the cadmium content were selected, and the spectral parameters were used as input factors to establish a back propagation（BP）neural network model of the cadmium content in tobacco leaves. The results showed that as the cadmium content increased, the reflectance in the visible light and near-infrared range（400~910 nm）first decreased and then increased. In the wavelength range of 930~1 000 nm, the leaf reflectivity was positively correlated with the cadmium content in the tobacco leaf. The reflectance in the 1 000~2 500 nm broadband range first increased and then decreased. The selected spectral indexes ratio vegetation index（RVI）and normalized difference vegetation index（NDVI）were RVI（520, 710）and NDVI（530, 710）, respectively. The R² of the BP neural network model was 0.681 and the root mean square error（RMSE）was 8.001, the model was tested, and the test results showed that the R2 was 0.801 and the RMSE was 4.430. The results showed that the BP neural network model could provide a good prediction of the cadmium content in tobacco leaves. [ABSTRACT FROM AUTHOR]

A planting area of fruit trees has accounted for up to 13% of the national planting area in Xinjiang, China. Local specialty fruits (e.g. walnut, jujube, apricot, fragrant pear, and apple) have been produced in four prefectures of Southern Xinjiang around the Tarim River Basin, accounting for more than 80% of the total fruit planting area in Xinjiang. It is very necessary to real-time and accurately acquire the fruit tree type and area information under the pattern of forest and crops interplanting, further to improve the quality and efficiency of the specialty fruit industry. Taking the continuous area of forest and crops interplanting in Hotan oasis of southern Xinjiang in China as an example, this study aims to extract the structure information of fruit trees from the satellite images. The high-resolution remote sensing image data with abundant texture and spectral characteristics was also integrated with the medium-resolution sensing image data with multi-temporal characteristics. Firstly, an object-oriented programming was used to extract the high-precision boundary of fruit trees parcel using GF-2 (PMS) image data. The GF-2 image data was divided into the winter (February) and summer-autumn (July-September). The Normalized Difference Vegetation Index (NDVI) was determined to identify the spectral and texture features between target objects and other ground objects. Four plots were obtained to gradually eliminate the non-target ground categories. Then, the NDVI time series products were constructed using multi-temporal Sentinel-2 image data. A decision tree model was established to extract the interplanting walnut, pure walnut, jujube, and grape orchard, according to the characteristics of phenology. There were many peaks and troughs in the NDVI time series curve of fruit trees in one year. The peak represented the flourishing period of fruit trees’ growth, and the trough reflected the orchard management (such as irrigation and pruning branches). There were some outstanding and different time windows for the NDVI timing series of pure walnut, interplanting walnut, jujubes, and grapes, particularly for the rarely staggered overlapping. These differences were greatly contributed to the classification of fruit trees. Finally, the multi-phase orchard classification was overlaid the high-resolution of fruit trees parcel, further to obtain the distribution of fruit crops in the study area. The research results show that the major area of fruit was 4.28×105 hm2 here, with 3.31×105 hm² of the walnut orchard (including interplanting and pure walnut), 8.29×104 hm² of jujube, and 1.40×104 hm² of grapes. The area of interplanting walnuts accounted for 63.8% of the total fruit area, The user accuracy and overall classification accuracy were both exceed 90%, and the Kappa coefficient was 0.95, fully meeting the accuracy requirements of agroforestry classification at the county and city level. The relative accuracies of walnuts, jujube, and grapes were 62.1%, 97.8%, and 85.2%, respectively, using remote sensing extraction, compared with the forestry survey in 2019. The areas of jujube and grape using remote sensing extraction were close to the forestry survey data. The walnut planting areas in the Hotan area were mainly distributed in the upper reaches of Yurunkax and Karakax River with suitable soil and water conditions. The jujube trees were mostly distributed in the downstream oasis-desert ecotone, and the grapes were mostly distributed in the sandy desert of the lower reaches of the Karakax River. This finding can provide a strong reference for the fruit tree extraction under the pattern of forest and crop intercropping. [ABSTRACT FROM AUTHOR]

Drought has become a global concern, and timely and accurate drought monitoring has an important guiding significance for the regional ecological security, economic development, and agricultural production of countries along the "New Silk Road Economic Belt" in the context of global warming. Since natural disasters have occurred frequently around the world in recent years, the drought has also posed a great threat to the global ecological environment, particularly to the long-term social and economic development of Central Asia and the surrounding countries. In this study, a Temperature Vegetation Dryness Index (TVDI) model was established using the MODIS Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) in Central Asia from 2001 to 2020. Based on trend analysis, MK test, and geographic detectors, the spatial and temporal variations, of drought and drought changing patterns in Central Asia in the last 20 years, the drought evolution pattern and the influence of different factors (temperature, mean annual precipitation, land use type, population density, DEM, slope, aspect) on TVDI were also investigated. The results show that: 1) There was an obvious regional pattern in the spatial distribution of TVDI in the study area, which were mainly characterized by regional and local droughts. The frequency and degree of droughts in the southwestern part of Central Asia are than those in the northeastern part, with a decreasing trend from the southwest to the northeast. For all the study area, 66% of the area showed a significantly increasing trend of drought (P<0.05), whereas, 34% of the area showed a significant decreasing trend of drought (P<0.05), indicating a pattern of "dry in the north and wet in the south". Overall, Central Asia shows an increasing trend of aridity, with the most severest drought in the northwestern part of Kazakhstan. 2) The degree annual of drought slightly increased over the past 20 years, with the severest drought occurred in 2012. There was a significantly different spatial distribution of TVDI, which with a clear seasonal change. The drought degree was ranked as follows: summer>spring>autumn. 3) The single-factor detection results showed that the contribution of the influencing factors to drought was ranked as follows: temperature > DEM > slope > precipitation > land use type, with the controlling factors of temperature and DEM for the drought (all q values were greater than 0.45). The multi-factor analysis showed the two-factor enhancement and non-linear enhancement, with t major contribution from the four combinations, e.g., — temperature and DEM, temperature and slope, temperature and land use, and temperature and precipitation. Under the background of global climate warming and increasing human activities (vegetation deterioration, urbanization and construction, cultivation, overgrazing, industrial pollution, etc.), the air temperature gradually increased and the precipitation decreased in Central Asia over the past 20 years. These changes led to vegetation degradation, which in turn resulted in ecological degradation and increased drought degrees. This finding can provide a scientific reference for the decision-making on the drought prevention and control, particularly for the ecological risks and geo-security impacts. [ABSTRACT FROM AUTHOR]

The red band and the near-infrared band are often used as sensitive bands for remote sensing retrieval of forest stock volume, however the red edge band between the two is often ignored. To investigate whether the red edge band is sensitive to the accuracy of forest stock volume retrieval, the wide imagery data of GF-6 remote sensing satellite (GF-6 WFV), the DEM, the second survey data of forest resources in 2014 of Xining city were used, and the multiple linear regression model (MLR), the random forest regression model (RF) were employed. The predicting variables were collected from spectral characteristic, vegetation index, topographic factor and image texture. These variables were divided into two groups, the one is no red edge band (No Red-Edge), and the other is red edge band added (Red-Edge Added). The results show that: (1) the selected principal components which were derived from the texture variables of two groups by PCA method mainly explained the texture features of red, near-infrared and red-edge 1 band of the image. (2) Regarding the spectral variables, the surface reflectance of red and near-infrared band were selected because of the high correlation with the forest stock volume in No Red-Edge group, and in Red-Edge Added group, the surface reflectance of the red-edge 1 band was selected. Regarding the vegetation index variables, the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were selected in No Red-Edge group, and in Red-Edge Added group, the MERIS Terrestrial Chlorophyll Index (MTCI) was selected. (3) In Red-Edge Added group, the R² of the RF model is better than that of the MLR model, 0.6719 and 0.5487, respectively, and the root mean square error (RMSE) of the RF model is smaller than that of the MLR model, 26.3m³·ha−1 and 20.8m³·ha−1, respectively. (4) After eliminating the effect of the model on the accuracy of forest stock volume retrieval, the R² between the retrieval and the observed values in Red-Edge Added group increased by 11.6%, and the RMSE in Red-Edge Added group decreased by 9.1% compared to No Red-Edge group. Our results suggested that the red-edge band significantly improved the accuracy of the coniferous forest stock volume retrieval in Xining city. This study has high potential value in the remote sensing retrieval of forest stock volume. [ABSTRACT FROM AUTHOR]

A quantitative estimation of nitrogen content in rice leaves has been one of the most critical steps to realize precision fertilization and variety breeding in modern agriculture. Optical remote sensing technology has been emerging in recent years. The multi- and hyper-spectral remote sensing data can be utilized to rapidly obtain the physical and chemical parameters, such as the rice nutritional status, pest stress, and phenotypic information. Therefore, it is very necessary to implement the high-throughput acquisition of breeding phenotypes in the rice digital production using spectral technology. In this study, a rapid and accurate inversion of nitrogen content was realized to detect the spectral characteristics and variation in the rice leaves using the character transfer vegetation index in the spectral technology. The results showed that there were all the same changes in the spectral curves of rice leaves in the key growth periods, indicating the extremely significant characteristics of "two valleys and one peak" in each period. The spectral characteristic curve varied significantly in the visible and near-infrared regions in the period of rice growth. Specifically, the spectral curve changed greatly in the near-infrared region from the tillering to the heading stage, whereas, the hyperspectral reflectance increased gradually in each growth period. The characteristic bands of the rice nitrogen content were extracted from the continuous hyperspectral reflectance with lots of redundant information. The vegetation index of nitrogen content inversion was then constructed for the rice leaves. Since the continuous projection was a forward band selection, the projection of each cycle was calculated in the remaining band starting from a band variable, and then the band corresponding to the maximum value of the projection vector was introduced into the band combination, while ensuring the lowest correlation between the selected and previous band, finally to repeat the above steps until the selected number of bands were fully met the given requirements. Therefore, the continuous projection was used to screen the characteristic bands of rice hyperspectral in the band of 400-1000 nm, where the correction set was utilized to conduct the internal cross validation of the selected bands. Six hyperspectral characteristic bands of nitrogen content in rice leaves were screened, i.e., 500, 555, 662, 690, 729, and 800 nm, according to the Root Mean Square Error of Cross Validation (RMSECV) values in the validation. Once the form of vegetation index was determined, the current vegetation index of rice nitrogen content was mostly constructed to change the bands for a new one. The mature form of vegetation index was normally used to determine the optimal band, according to the different inversion parameters. Nevertheless, there was still lacking of relationship between nitrogen content and spectral characteristics. Therefore, the multiple characteristic bands were first converted into three bands for the nitrogen characteristic transfer index, according to the band characteristic transfer. A linear regression model was established to verify the inversion model of nitrogen content in rice leaves. Taking Nitrogen Characteristic Transfer Index (NCTI) as the input, the inversion model of rice nitrogen content was constructed by linear regression. The determination coefficient of the model was 0.774, and the root mean square error was 0.379 mg/g. The inversion performed better, compared with the traditional vegetation indexes, such as NDVI (Normalized Difference Vegetation Index) and EVI (Ratio Vegetation Index). Consequently, the NCTI can be widely expected to serve as a hyperspectral vegetation index for the rapid inversion of nitrogen content in rice leaves in practical application. The finding can provide promising data support and strong reference for the spectral detection of nitrogen content in rice leaves. [ABSTRACT FROM AUTHOR]

This research aimed to determine the temporal and spatial variations of the hydrometeorological extremes, and their impacts on vegetation across the Yangtze River basin (YRB). First, a meteorological reanalysis dataset with high spatial and temporal resolution from 1982 to 2015 was used to identify and analyze the annual variation trends of the extreme drought (Dry-PRE or Dry-VPD) and extreme wetness (Wet-PRE or Wet-VPD), based on precipitation and vapor pressure deficit (VPD), respectively. Subsequently, the responses and sensitivities of vegetation growth (indicated by normalized difference vegetation index, NDVI) to the hydrometeorological extremes were explored. The results showed that the frequencies of Dry-VPD, Wet-PRE and Dry-PRE generally increased while that of Wet-VPD decreased during the study period. Particularly, Dry-VPD increased significantly in the upstream and downstream of the YRB after 1998. The vegetation across the YRB was more sensitive to extreme drought (mainly with a negative correlation) than extreme wetness. Moreover, the vegetation growth in the upstream was sensitive to Dry-VPD, while that in the midstream and downstream was sensitive to Dry-PRE. To summarize, this study found that extreme drought was on the rise across YRB. Different from the general notion that precipitation limits vegetation growth, our study indicates that vegetation in the upper reach of the YRB is more negatively impacted by atmospheric drought. [ABSTRACT FROM AUTHOR]

Crops biomass has been one of the most important indicators to predict the plant growth status and crop yield. This study aims to estimate the dry biomass of the winter wheat aboveground using optical satellite remote sensing. The winter wheat biomass was acquired at four growth stages (jointing, heading, flowering, and filling stage), and three nitrogen treatments (N1, N2, and N3, application rates of 0, 150 and 300 kg/hm²) in 2011, 2012, and 2014 at the agricultural meteorological experiment station, Nanjing University of Information Science and Technology, Nanjing, China. Simultaneously, the narrow-band spectral reflectance of canopy was also collected from the winter wheat using Analytical Spectral Device (ASD). Afterwards, the hyperspectral remote sensing data was resampled into the broad band reflectance of RapidEye, Sentinel-2, and WorldView-2 satellites with the red edge bands using the satellite spectral response functions. All possible band combinations of Normalized Difference Vegetation Index like (NDVI-like) were validated in the different growth stages and nitrogen treatments. Meanwhile, the satellite broad-band reflectance was used as the inputs for the six machine and deep learning for the biomass estimation, including the Random Forest (RF), Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), Deep Neural Network (DNN), Long- and Short-Term Memory recursive neural network (LSTM), and one-dimensional Convolutional Neural Network (1D-CNN). Finally, the models were developed using Leave-One-Out cross validation under different growth stages and nitrogen treatments. The results showed that the optimal NDVI-like vegetation indices that derived from the two arbitrary bands presented the highest sensitivity to the winter wheat biomass at the flowering stage (coefficient of determination, R²=0.50-0.56). It was also difficult to accurately estimate the biomass of winter wheat using only one vegetation index in the whole growth period. The nitrogen treatments were dominated the correlation between the vegetation indices and winter wheat biomass. Specifically, the high nitrogen treatment was enhanced the sensitivity of vegetation index to the winter wheat biomass. The vegetation index with the Sentinel-2 bands performed better than that with the rest. The R² was over 0.50 between the vegetation index and biomass at the jointing and flowering stages. The best performance was achieved in the estimation model of winter wheat biomass using DNN among the six models. The performance of DNN-based model was also depended on the growth stages and nitrogen treatments. In the single growth stage, the highest estimation accuracy was observed at the jointing stage (R²=0.69-0.78 and the normalized root mean square error (NRMSE)=0.26-0.31), and at the flowering stage (R²=0.69-0.70 and NRMSE =0.24-0.35). The highest estimation accuracy was obtained in the DNN-based model with the Sentinel-2 bands as the inputs, indicating the R² of 0.70 in the whole growth period. The high nitrogen treatment was also enhanced the estimation accuracy of DNN model, where the R² was not lower than 0.71 and RMSE was within 219 g/m² at the N3 condition for all the three satellite bands. Therefore, the optical satellite remote sensing data can be expected to estimate the winter wheat biomass under the different growth stages and nitrogen treatment conditions. [ABSTRACT FROM AUTHOR]

The photosynthetic capacity of a crop plays a decisive role in its yield. The quantum yield (QY) of soybean flag leaf is also very important to assess photosynthetic efficiency. High-throughput QY inversion can rapidly, non-destructively, and efficiently monitor the physicochemical changes in the soybean flag leaf during photosynthesis using a UAV multispectral. The objective of this study was to investigate the correlation between the vegetation indices and QY, and then to invert the QY values using the highly correlated vegetation indices. The inversion models were also constructed for high accuracy with the multiple versus single vegetation indices. Eight vegetation indices were calculated, including the Normalized Difference Vegetation Index (NDVI), green NDVI (GNDVI), Enhanced Vegetation Index (EVI), Leaf Chlorophyll Index (LCI), Soil Adjusted Vegetation Index (SAVI), green SAVI (GSAVI), Optimized SAVI (OSAVI), and Normalized Difference Red Edge (NDRE). The high throughput of the spectral collection was used in the five bands of the soybean canopy. Pearson correlation coefficients were also utilized to determine the correlations between the single vegetation indices and QY values of the soybean flag leaf. Six vegetation indices with high correlations were then selected as NDVI, GNVDI, LCI, SAVI, OSAVI, and NDRE. The single-index inversion models of the six highly correlated vegetation indices were constructed using five models, including the Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), Random Forest (RF), Adaptive Boosting (AdaBoost), and Gradient Boosted Decision Tree (GBDT). The simulation was then evaluated using three evaluation indexes, including the coefficient of determination (R²), root-mean-square deviation (RMSE), and relative percent difference (RPD). Five models were evaluated to select the vegetation indices with the better inversion for the QY of the soybean flag leaf. The single vegetation index modelling showed that the NDVI, GNDVI, LCI, and NDRE performed better inversion for the QY of the soybean flag leaf. A comprehensive analysis was made to verify the evaluation indexes of each modelling. The SVR, AdaBoost, and GBDT modelling were more suitable for this case, compared with the PLSR and RF. The integrated learning-based AdaBoost improved the accuracy and robustness of the model, particularly with the validation set R² of 0.982, RMSE of 0.089, and RPD of 7.29, indicating the standard of a Class A model, compared with the traditional inversion SVR. Among them, the NDVI presented the strongest correlation with the QY values of soybean flag leaf, especially with a Pearson correlation coefficient of 0.956. The R² values were all greater than 0.7 for the fitting between the NDVI, GNDVI, NDRE, LCI, and QY values, indicating being suitable for the QY inversion model. The R² values in the test set were 0.959, 0.954, 0.962, 0.982, and 0.967, respectively, using the traditional inversion SVR, PLSR, integrated learning-based random forest, AdaBoost, and GBDT algorithms. Therefore, the learning algorithm based on integrated learning can be expected to further improve the accuracy and robustness of the inversion model. Compared with the inversion models with the single and multiple vegetation indices, the combination of multiple vegetation indices can be used to improve the prediction accuracy of the inversion model. More importantly, the R² and RPD of the AdaBoost model were improved by 0.149 and 4.645, respectively, while the RMSE was reduced by 0.306. The multiple vegetation indices and the AdaBoost can be used to construct the much more effective multispectral inversion model for the photosynthesis QY of the soybean flag leaf. The finding can also provide an advanced method to assess the high-throughput photosynthetic efficiency using remote sensing. [ABSTRACT FROM AUTHOR]

Plastic mulch has been widely used to increase the soil temperature with the constant moisture for the high crop yields in the arid regions in northwestern China. Particularly, there has also the application in maize, wheat, and cotton cultivation in Hexi Corridor since the last century. Nevertheless, the mulch residues have significantly damaged the soil structure, leading to environmental degradation. Therefore, it is very necessary for the spatial and temporal distribution of plastic mulch, in order to quantify the impact on the local environment. However, the wide region and the complex interlocking surface types have posed a great challenge to the spatial and temporal distribution of plastic mulched cropland (PMC) from the field work. Fortunately, large-scale ground objects can be extracted with the development of remote sensing. However it is still lacking in the acquisition and processing of remote sensing data. This study aims to extract the spatiotemporal PMC variations in Hexi Corridor from 2013 to 2022. The multi-source remote sensing data was first collected using Google Earth Engine, including Sentinel-2, Landsat 8, Landsat 7, and MODIS. A time window of PMC extraction was also proposed using the variance of the Normalized Difference Vegetation Index (NDVI) within one year. Then, the Timely Plastic-mulched cropland Extraction Method (TPEM) was applied to the complex and variable ground objects for the extraction of the PMC area. About 500 sample points were randomly selected and then labelled as the PMC and non-PMC for the accuracy assessment. The Overall Accuracy (OA), User Accuracy (UA), Quantity Disagreement (QD), and Allocation Disagreement (AD) were also used to evaluate the performance of TPEM. Finally, the spatiotemporal pattern of PMC was analyzed to explore the potential controls on the interannual variation in the PMC area using Water Yearbook Bulletin data. The results showed that: 1) The best time window was the first half of April for the PMC extraction, which also coincided with the sowing of major local crops. The half month before and after this period was also beneficial to extracting the PMC information. 2) The TPEM reached 80% overall accuracy with the QD less than 4%. The error was attributed to the disagreement between the sample points and the real PMC area. 3) The average PMC area was about 383 000 hm², of which about 55.6% was in the Heihe Basin. The PMC was located near the inland river systems. Approximately 41.9% of the cropland used plastic mulch, while the PMC area decreased by 32.4% in the last decade. Meanwhile, there was a 46% increase in the water-saving irrigation area, whereas, there was a 14.9% decrease in the irrigation water from 2013 to 2019, indicating the significant correlation with the decrease in the PMC area. In conclusion, the application of plastic mulch was ever decreasing with the development of water-saving irrigation. This finding can be an essential reference for the survey of agricultural resources in the Northwest Arid Region of China. [ABSTRACT FROM AUTHOR]

Understanding the relationship between hydrological and sediment connectivity under extreme rainfall conditions is of great significance for water and sediment regulation and high-quality development of soil and water conservation in the basin under the new situation of vegetation restoration in the Loess Plateau. In this study, the influence of extreme rainfall and vegetation restoration on water-sediment connectivity and the characterization ability of sediment transport potential was analyzed based on the index of connectivity, the structural sediment connectivity (SCst) improved by topographic location index and vegetation cover factor, the functional hydrological connectivity (HCfn) improved by the cumulative surface runoff depth factor. The results showed that the rainfall and vegetation were the main functional and structural factors affecting hydrological and sediment connectivity, respectively. The mean value of normalized difference vegetation index (NDVI) increased by 0.41 from 2000 to 2020, while the mean value of sediment connectivity decreased by 3.54, with a negative correlation coefficient of −0.93. The spatio-temporal distribution of rainfall was in good agreement with the spatio-temporal distribution of hydrological connectivity, both of which were highly correlated on the monthly scale. When extreme rainfall occurred, rainfall runoff factors played a dominant role and determine hydrological and sediment connectivity. The improved water-sediment connectivity index was well applied to the study of large-scale watershed connectivity in the Loess Plateau. Pearson correlation analysis showed that the functional hydrolodical connectivity had a good correlation with runoff and sediment transport capacity in the basin under the inter-annual and seasonal variation, and could better reflect the phenomenon of runoff and sediment increase in the basin under extreme rainfall. Compared with previous studies in which functional connectivity was calculated by using factors such as rainfall erosivity under annual changes, which could not well characterize the actual sediment transport capacity of the basin, the functional connectivity used in this study had obvious advantages, which would provide a more direct and effective tool for water and sediment management in large basins on the Loess Plateau in the future. The interaction between basin structure and functional connectivity changed significantly with seasons. In dry season, vegetation cover was low, rainfall was low, structural sediment connectivity was high and HCfn was increasing. At this time, SCst had a weak influence on HCfn, and the two showed a high negative correlation. In the wet season, with sufficient rainfall and lush vegetation, the correlation between structure and work connectivity changed with the trend of rainfall. At this time, the SCst was low, which limited the function of HCfn to a certain extent. When encountering extreme rainfall, high-intensity rainfall runoff played a dominant role, and SCst couldn’t significantly affect HCfn, while HCfn changed SCst in turn, and both increased at the same time, showing a strong positive correlation. In the normal water season, vegetation gradually withered due to the weakening of rainfall intensity, SCst increased while HCfn decreased, and the negative correlation between the two was enhanced. In conclusion, the improved water-sediment connectivity index has certain advantages and limitations, which can provide guidance for water and sediment management and ecological construction of large watershed in the Loess Plateau under the changing environment in the new era. [ABSTRACT FROM AUTHOR]

Garlic has been one of the most widely planted cash crops in Henan and Shandong provinces, China. The garlic plantation varies greatly in the frequently fluctuated price over the past few years, further dominating the decision-making on the planting for the healthy and sustainable development of the garlic market. However, there is a great challenge on the rapid and accurate extraction of garlic with a high precision, where the garlic crop planting is often mixed with other ground crops. Taking the Four-Huaiqing Chinese medicine base in Kaifeng City, Henan Province of China as a study area, this research aims to improve the identification accuracy and efficiency of fragmented garlic with the complex planting structure using remote sensing data. An object-oriented model was also proposed to integrate the active and passive remote sensing data of Sentinel satellites using the Google Earth Engine (GEE) platform and random forest (RF). The normalized difference vegetation index (NDVI) time-series data was selected to separate the phenological features of garlic and other land cover types. The maximum difference of NDVI and the features of RF were used to screen the monthly mean Sentinel-2 optical data in March 2021. A time series of Sentinel-1 synthetic aperture radar (SAR) backscattering coefficients were selected for the monthly mean data from November 2020 to May 2021. Two steps were then implemented before classification. The first one was to integrate the data segmentation using the simple non-iterative clustering (SNIC), further to select the best segmentation scale with the highest classification accuracy and Kappa coefficient. The second one was to select the best within three neighborhood values (4, 8, and 16) using the gray-level co-occurrence matrix (GLCM), thereby calculating the seven texture features of synthetic optical data, and finally to reduce the data dimensions using the principal components analysis (PCA). The red edge bands of vegetation were integrated with the Sentinel passive and active remote sensing data in the10 or 20 m spatial resolution. As such, the identification accuracy of garlic was improved to combine various groups of spectral features, backscattering coefficients, vegetation indexes, and different principal component groups of texture features. The result showed that the highest overall accuracy of classification and Kappa coefficient reached 94.54%, and 0.93, respectively, using the active and passive Sentinel remote sensing data in the 10 m spatial resolution, where the producer’s and user’s accuracies of garlic were 97.83% and 96.38%, respectively, at the SNIC segmentation scale of 5, and the GLCM neighborhood value of 4, as well as the first and second principal components of the 7 texture features. In the case of active and passive Sentinel remote sensing data with three vegetation red edge bands in the 20 m spatial resolution, the highest overall accuracy of classification and Kappa coefficient reached 94.14%, and 0.92, respectively, where the producer’s and user’s accuracies of garlic were 95.72% and 98.81%, respectively, at the SNIC segmentation scale of 3, and the GLCM neighborhood value of 4, as well as the first and second principal components of the 7 texture features. The producer’s and user’s accuracies in the 20m spatial resolution Sentinel-2 data with three vegetation red edge bands were improved 0.49% and 4.38%, respectively, compared with the 10m spatial resolution. In the time series of Sentinel-1 SAR data, the producer’s and user’s accuracies in 10 m spatial resolution were improved 0.66% and 2.03%, respectively, compared with the 20m. Correspondingly, the Sentinel active and passive remote sensing data can be effectively integrated to fully represent the spectral and structural information. Moreover, the overall accuracy and Kappa coefficient were much higher than those using the optical or time series SAR data alone, particularly the maximum in the 10 m high spatial resolution. Therefore, the SNIC, GLCM, PCA, RF, and GEE platforms can be widely expected to accurately and efficiently extract the garlic planting areas using GF satellites data. [ABSTRACT FROM AUTHOR]

A crop yield estimation can provide the data support for the decision-making in the entire crop growing season. The previous estimation models of winter wheat yield often perform a low accuracy and less generalization, due to the overfitting during machine learning. Meanwhile, the specific feature variables can be expected to reduce the high collinearity between spectral bands of surface reflectance. It is also proper to set the spectral index for the less influence of independent wavelengths on iterative calculation. In this study, an improved estimation model of winter wheat yield was established using Random Forest (RF) in Henan Province of China, using the surface reflectance data and spectral index during the growth period. The specific procedure was listed. 1) The comparisons were conducted for the regional mean values and regional frequency histogram on the spectral bands of surface reflectance. 2) The different spectral bands of surface reflectance were then compared. 3) The generated sample data on the specific feature variable was used to train a yield estimation model. 4) The yield estimation model was applied to evaluate the winter wheat yields during 2013-2015, using the surface reflectance data and spectral index. The estimated yields of winter wheat were also verified by the survey statistics. The results showed that the frequency histogram presented a better performance than the regional mean values, where the Mean Absolute Error (MAE) was 660 kg/hm², Root Mean Square Error (RMSE) was 860 kg/hm², and the coefficient of determination was 0.83. The dimension of input feature variables and computational complexity were greatly reduced to improve the estimation ability of the model. The frequency histogram was suitable for the extraction of feature variables, further to optimize the sample structure. Among the surface reflectance bands, the near-infrared band 1 behaved the best performance, where the MAE was 636 kg/hm², RMSE was 784 kg/hm², and the coefficient of determination was 0.76. The near-infrared band 2 was the second most. The estimation accuracy of the blue band was the lowest, where the coefficient of determination was only 0.58, while the MAE and RMSE were 885 and 1040 kg/hm², respectively. The coefficient of determination of Normalized Difference Water Index (NDWI) was 0.82, whereas, the coefficient of determination of Normalized Difference Vegetation Index (NDVI) was 0.73. Moreover, there was a higher coefficient of determination in the combination of NDVI and NDWI, where the coefficient of determination was 0.89, and the MAE and RMSE were 444 and 527 kg/hm², respectively. There was the crucial influence of the heading stages of NDVI and NDWI on the winter wheat yield. Specifically, the accuracy of the combined NDVI and NDWI reached the highest on April 15th, indicating that the period from April 15th to 22nd presented the greatest impacts. In addition, the inter-year variation patterns from 2013 to 2015 showed well consistent trends between the estimated and observed yields in the spatial distribution, where the relative errors were within ±20%, and gradually decreased from the northwest to the southeast. A comprehensive effect of topography, temperature, and precipitation can be attributed to the surface microclimate and material redistribution. Consequently, the better yield estimation of winter wheat over the large areas can be achieved in the main winter wheat planting regions of Henan Province, using the random forest model combined with the regional frequency histogram samples. The next step can be recommended to maximize the number of samples and introduce the high-resolution satellite images for higher accuracy of the winter wheat yield estimation model. [ABSTRACT FROM AUTHOR]

Dynamic change of Soil Salinity Content (SSC) is widely used to control soil salinization for high production efficiency in modern agriculture. This study aimed to enhance the correlation between SSC and spectral reflectance under vegetation coverage using Sentinel-2 multispectral remote sensing. Soil samples of salinity and moisture were collected at 100 sampling sites with different depth ranges, including <20, 20-40, and >40-60 cm, in Shahaoqu Irrigation Area, Inner Mongolia, China, from June to August in 2019. The multispectral data of Sentinel-2 satellite was acquired synchronously according to the sample time and location. The specific procedure was as follows. Firstly, a depth decision tree was constructed with the normalized difference vegetation index as a branching criterion, where the best one was then selected to determine the optimal depth range for the SSC retrieval of soil samples. Secondly, a classification decision tree was used to divide the soil samples into different categories, according to the normalized vegetation index and Soil Moisture Content (SMC). As such, the category of each soil sample was determined using the classification decision tree. Thirdly, the optimal spectral combination for each category was calculated to serve as an input variable into the SSC inversion model. Several machine learning models were adopted for the SSC inversion models to monitor the SSC at the optimal depth range from the salinity depth decision tree, including Adaptive boosting algorithm (Adaboost), Partial Least Squares Regression (PLSR), Support Vector Machines (SVM), Gradient Boosting Decision Tree (GBDT), and Random Forest (RF). The results showed that the correlation coefficient between SSC and spectral reflectance was above 0.66, considering the decision tree. In terms of soil depth range, the optimal inversion depth for the SSC under vegetation cover was >40-60 cm, followed by <20 cm, but the SSC inversion model presented some limitations in the middle layer (20-40 cm). Furthermore, the inversion accuracy was ranked in the descending order of RF, GBDT, Adaboost, PLSR, and SVM, where the RF, GBDT, and Adaboost presented similar inversion. Correspondingly, the SSC inversion model using ensemble learning demonstrated a strong generalization ability to achieve the ideal and stable inversion under different application scenarios, compared with the other machine learning. Specifically, the SSC inversion model performed the best using RF, where the coefficient of determination, the root mean square error, the residual predictive interquartile range, and the residual predictive deviations were 0.77, 0.27%, 2.65, and 8.99, respectively. The correlation between SSC and spectral reflectance was 0.38 without considering the decision tree, indicating there was no significance in the SSC inversion model. Considering the decision tree, the correlation coefficient between soil salinity and spectral reflectance was above 0.66, and the coefficient of determination of the SSC inversion model was 0.72, indicating that the decision tree effectively enhanced the sensitivity of spectral reflectance to SSC for the high accuracy, particularly when the vegetation on the surface of the soil. Consequently, this finding can provide a promising potential way to monitor the soil salinization in the irrigated areas during crop growth using multi-spectral satellites in modern agriculture. [ABSTRACT FROM AUTHOR]

As an important economic tree species in China, tea trees play a critical role in promoting economic development. However, the expanding area of tea plantations in China over the recent years has reduced the biodiversity of the ecosystem and accelerated ecological problems. Identifying the spatial distribution of the tea plantation accurately is important for the monitoring of the tea plantation as well as the development of the tea industry. In this study, Wangzhai Town, located in the central of Wuyi County in Zhejiang Province, was chosen as the study area, and Gaofen-1 (GF-1) data on December 28, 2017, and all available Sentinel-2 data in 2017 were downloaded to study the tea plantations extraction method. The spectral and texture features of tea plantations are similar with crops on the high spatial resolution remote sensing image which results in the mixture of the tea plantations and crops. The Normalized Difference Vegetation Index (NDVI) time series data can reflect the phenological changes of the tea trees and crops, which can be used to improve the separability of tea plantations and crops. In this study, a phenology-based approach was applied to the NDVI time series to distinguish tea plantations from crops by comparing the similarity of the seasonal changes of NDVI time series. Due to the influence of cloud and fog on the satellite image, the complete Sentinel-2 time series data is difficult to be obtained. Therefore, the high temporal resolution Moderate-resolution Imaging Spectroradiometer (MODIS) reflection product (MCD43A4) in 2017 was used to reconstruct the complete Sentinel-2 NDVI time series data (5 m spatial resolution, 5 d temporal resolution) based on Bayesian Maximum Entropy (BME) spatial-temporal fusion algorithm. Then, the Dynamic Time Wraping (DTW) distance was calculated based on the DTW algorithm to characterize the similarity of NDVI time series between the tea plantation samples and other pixels to be classified. Combining the advantages of GF-1 in spatial details and the high temporal resolution of reconstructed Sentinel-2 NDVI time series in capturing the difference of growth process of tea trees, the tea plantation was identified based on random forest algorithm. Two identification schemes: 1) spectral and texture features based on GF-1; 2) spectral, texture features based on GF-1 and DTW distance were set up to investigate the impact of NDVI time series on the tea plantation identification. The results showed that: 1) the reconstructed NDVI time series data derived from MODIS and Sentinel-2 based on spatio-temporal fusion algorithm is able to capture the seasonal dynamics of different objects, and the DTW distance can capture the difference of NDVI time series data between crops and tea plantations and can be used for tea plantations extraction; 2) the accuracy、 error rate、precision、recall and F1score were 96.91%、3.09%、89.00%、83.09% and 0.86 respectively combining texture and spectral information of GF-1 and Sentinel-2 NDVI time series information, which were 94.72%、5.28%、73.09%、84.61% and 0.78 respectively without Sentinel-2 NDVI time series information. Overall, the results of combining GF-1 and Sentinel-2 time series data performed better than that only based on the spectral and texture features of GF-1. These results confirmed that combining high spatial resolution with high temporal resolution time series remote sensing data is an effective method to improve the identification accuracy of tea plantations. [ABSTRACT FROM AUTHOR]

Driving mechanisms of Fractional Vegetation Cover (FVC) can be a prerequisite for decision-making on vegetation restoration and management. As the intersection of farming and animal husbandry activities in China, the Liaohe River basin is of great significance for the regional ecological construction and environmental protection, in order to clarify the internal vegetation change characteristics and driving mechanisms. Much effort has been made into the characteristics and influencing factors of vegetation change in the region. However, it is still lacking in the spatial heterogeneity of vegetation in the region as an Agro-pastoral intersection zone. Since vegetation change is a complex process, it is very necessary to fully consider the complex influence of the interaction between environmental factors on vegetation change, rather than only the individual factors. In this study, the spatial heterogeneity of FVC was analyzed in the Liaohe River Basin from 2010 to 2019 using the Normalized Difference Vegetation Index derived from the MOD13Q1 product. Furthermore, the factor regression and interaction were combined to jointly explore the effects of natural and socioeconomic factors on the vegetation changes from a regional and overall perspective. The results showed that: 1) There was an overall rising trend of FVC, with a 10-year average FVC of 0.68 and an overall high level of vegetation cover. The most significant was ranked in the order of the pastoral areas > agricultural areas > semi-pastoral areas, and the vegetation improvement trend in the pastoral areas, in terms of the rising trend of vegetation cover. 2) The explanatory power of natural factors on the vegetation changes in the whole basin was greater than that of human factors, among which the explanatory power of precipitation was the most significant. At the same time, there was an outstanding synchronization between the vegetation and precipitation change, where this trend was the most significant in the pastoral areas. 3) The interaction indicated that most factors showed a mutually reinforcing and non-linear enhancement of vegetation change. It infers that the vegetation change was a complex process with multi-factor effects from a system perspective. There were no completely independent factors. The three largest groups of interacting factors across the basin were the precipitation and temperature, precipitation and elevation, and precipitation and wind speed. The natural factors dominated the interaction of vegetation in the pastoral and semi-pastoral areas. By contrast, a combination of natural and human activities significantly affected the vegetation change in the agricultural areas. 4) The precipitation gradient greatly contributed to the explanation degree of FVC spatial heterogeneity. The environmental factors better fitted the FVC with the increase in precipitation. The precipitation was an important catalyst for the vegetation change. 5) human activities also posed an important influence on the vegetation change. The positive effects of human activities were generally dominant in the study area over the past 10 years. Such reasonable human activities can be maintained to increase the FVC in the Liaohe River basin. [ABSTRACT FROM AUTHOR]

Soil Organic Matter (SOM) is the carrier of soil organic carbon in the crop system. This component of soil solid matter is one of the core elements to evaluate soil fertility quality in agriculture and land management. An accurate and efficient acquisition of SOM content can greatly contribute to the quality grading of cultivated land. High-resolution remote sensing and Google Earth Engine (GEE) can serve as the computing platform for the efficient inversion of SOM. Much effort has been made on the SOM prediction model and the spatial distribution map. However, it is still lacking in the appropriate satellite data sources and prediction algorithms to accurately predict the SOM content in specific regions. In this study, the accurate SOC content was predicted in the cultivated land using GEE and machine learning. The Sentinel-2A MSI and the Landsat8 OLI data were collected in the Gaocheng District, Shijiazhuang City, Hebei Province, China. The main data sources were also combined with the Sentinel-1 SAR, ECMWF/ERA5 meteorological, and USGS/SRTMGL1_003 elevation data. The variable feature sets of the spectral band were constructed, including the vegetable index (Normalized Difference Vegetation Index (NDVI)；Red Index (RI)；Enhanced Vegetable Index (EVI)；Soil-Adjusted Total Vegetation Index (SATVI)；Brightness Index (BI)), radar feature (Sentinel-1 VV, and Sentinel-1 VH), terrain feature (slope, aspect, and elevation), and climate feature (annual precipitation, and average annual temperature). Six and five models were constructed using the Sentinel-2 and Landsat8 variable datasets, respectively. Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Support Vector Machine (SVM) were utilized to predict the SOM on the GEE platform. The predictive performances of three machine learning methods were determined for a high-precision spatial distribution map for the SOM inversion. The accuracy of the prediction model was then evaluated using the determination coefficient (R²) and the root mean square error (RMSE). The results show that: 1) the R² and RMSE values of the model using the Sentinel-2A were better than those using the Landsat8. The Sentinel-2A model performed better than the Landsat8 model in the predicting SOM content. The best performance (R2 =0.759, RMSE=2.852 g/kg) was achieved in the omnivariate model of Sentinel-2A under the GBDT. 2) The maximum improvement of 9.752% was obtained in model A-1 with the red edge band, compared with model A-0. This difference was attributed to the inclusion of four red edge bands (B5, B6, B7, and B8A) in model A-1. The addition of red edge bands greatly improved the prediction accuracy of the model, particularly with the effective spectral information for the SOM inversion. 3) The red edge band, vegetable index, Sentinel-1A radar features, terrain factors, and climate variables greatly contributed to the prediction accuracy of SOM from the perspective of different variable feature combinations. 4) The GBDT was better applied to the SOM prediction in the study area. The resultant SOM map was used to accurately characterize the SOM spatial distribution. The test data was verified for high accuracy, each group of which was an excellent consistency in the image, indicating the reliable SOM inversion. Therefore, the Sentinel-2A MSI data presented outstanding advantages over the Landsat8 OLI, due to the higher spectral and spatial resolutions. The combination of GBDT, Sentinel-2A, and GEE can be an effective way to predict the SOM map. Each prediction factor can also provide valuable information for the prediction of SOM content. [ABSTRACT FROM AUTHOR]

Grain crops are of great significance to the national food security in the world in recent years. Among them, the single remote sensing data source can usually be used to monitor the distribution of grain crops at the pixel scale. However, the monitoring data of grain crop can be constantly contaminated with the serious pepper noise. The current accuracy cannot fully meet the harsh requirements of cropland management in smart agriculture. In this study, a modified strategy was proposed to identify the grain crop information at the field parcel scale using the multi-source satellite data (including high-resolution data (HR), multi-temporal data (MT), and hyperspectral data (HS)), and vector data of land-use type. A study area was selected as the 13 km×12 km complex planting region in the Huangdao District of Qingdao City, Shandong Province, China. The data sources were collected from the ZY1-02D Visible Near-Infrared Camera (VNIC), Advanced Hyperspectral Imager (AHSI), and Landsat-8 Operational Land Imager (OLI). The experiment was performed on the following steps. Firstly, the vector data was used to obtain the cultivated and non-cultivated land boundaries, where the non-cultivated land was masked in the study area. Secondly, the watershed algorithm was utilized to segment the HR for the boundaries of homogeneous crop field parcels. Thirdly, the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were calculated to determine the thresholds of vegetated field parcels. After that, the spatiotemporal spectrum feature datasets were constructed at the field parcel scale using multi-source satellite remote sensing data, including the spectral features (SFs), Vegetation Indices (VIs), and Texture Features (TFs). Then, an independent sample t-test method was adopted to calculate the separability between the grain crops and non-grain crops within different feature types. The optimal subset of features within each feature type was determined to train the classifier with the diagonal covariance matrix as the discriminant analysis. Seven sets of features were constructed by different combinations of the optimal subset of each feature type. A Quadratic Polynomial Support Vector Machine (QSVM) identification was then carried out to evaluate the accuracy of the system. Subsequently, the contribution of each feature type to the accuracy was analyzed under the different field parcel sizes. Moreover, the optimal feature set was achieved to compare the identification accuracy. Finally, the better accuracy was determined at the field parcel scale and the traditional pixel scale under the optimal feature set. The results showed that: 1) The proposed strategy performed better to acquire the grain crop distribution of cultivated land, with an identification accuracy of 89.7% using the number of field parcels. 2) The maximum identification accuracy of 97.1% was achieved at the field parcel scale using the number of pixels, in terms of the optimal feature set with the SFs, VIs, and TFs. The accuracy was improved by about 24.2 percent points, compared with the traditional pixel scale, and the identification crop field parcels were more complete. 3) The HR_VI and HS_SF can be expected to significantly improve the identification accuracy of small and large field parcels, respectively. In medium-sized field parcels, both HR_VI and HR_TF were contributed to the high identification accuracy of grain crops. The finding can also provide a strong reference to efficiently utilize the cultivated land. [ABSTRACT FROM AUTHOR]

: An accurate prediction of the spatial distribution of Soil Organic Matter (SOM) is of great importance for precision agriculture, farmland quality construction, ecological environment protection, and soil carbon sequestration. However, the accuracy of prediction dominates by the heterogeneity of SOM spatial distribution and its relationship with auxiliary variables. Taking Hailun City, Heilongjiang Province (126°14′-127°45′ E, 48°58′-47°52′ N) of northeast China as the study area, this study aims to accurately and rapidly predict the SOM spatial distribution using a Two-Point Machine Learning Method (TPML) with the climate, topography, socio-economic, and spatial location as the auxiliary variables. The spatial location and auxiliary variables were also integrated to effectively deal with the heterogeneity of SOM spatial distribution and the heterogeneity of its relationship with auxiliary variables. The performance of TPML was then evaluated using the Random Forest (RF), RF regression kriging, inverse distance weighting, and Ordinary Kriging (OK) models. The performances of the models with samples of different sizes were also evaluated using the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), correlation coefficient between the predict and true value (r), and the coefficient of determination (R2 ). The results reveal that: 1) The SOM was predicted to range from 1.775 to 7.188 g/kg in the study area, with an average value of 3.179 g/kg. The spatial distribution of SOM spatially varied, with a trend of the high in the east and the low in the west. Meanwhile, the SOM content was positively correlated with the normalized difference vegetation index (NDVI), digital elevation, and mean annual precipitation, whereas, negatively correlated with the gross domestic product, mean annual air temperature, and topographic wetness index, particularly significantly related to the land use, landform, vegetation, and soil type. 2) The TPML presented the highest accuracy of prediction under different sample sizes, with the lowest MAE (0.088-0.097 g/kg) and RMSE (0.116-0.139 g/kg), while the highest r (0.992-0.996) and R² (0.971-0.985). The MAE and RMSE of the TPML model were improved much more than 0.7 g/kg, while the r and R² were improved by more than 0.2, and 0.9, respectively, compared with the most frequently-used OK. 3) There is a similar spatial pattern between the standard deviation of prediction errors (theoretical errors) and the actual errors, indicating that the TPML provided reasonable uncertainty estimates for the prediction. Consequently, the TPML can be expected to employ spatial autocorrelation and attribute similarity at the same time for higher spatial prediction accuracy. Anyway, the TPML spatial prediction of variables is feasible for the resource and environment with a certain degree of spatial autocorrelation and available auxiliary data. [ABSTRACT FROM AUTHOR]

The planting distribution of paddy rice has been widely extracted to identify the flooding characteristics of paddy rice during the transplanting period. Optical remote sensing images can be mostly used as the data source at present. However, the transplanting period cannot characterize full spectral characteristics of the paddy rice lifespanthe, spectral characteristics of paddy rice should be considered in multiple phenological periods. The pixel-based extraction of paddy rice planting distribution can also be susceptible to the data source noise. Fortunately, an object-oriented extraction can be selected to effectively reduce the impact of data source noise in the field. Since the remote sensing images are limited by the acquisition and processing costs, only a few studies focused on the application of multiple paddy rice phenological stages in the extraction of paddy rice planting distribution in large areas. Alternatively, the emergence of the Google Earth Engine (GEE) platform in earth science data and analysis can be accessible to large amounts of remote sensing data for free and with high efficiency. Taking Panjin City, Liaoning Province of China as the research area, this study aims to realize the object-oriented extraction of rice planting areas using phenological features from the GEE platform. Four phenological periods of paddy rice were selected, namely the sowing, transplanting, heading, and maturity period. Specifically, the sowing period with the Bare Soil Index (BSI) was selected from March 15 to April 30. The transplanting period was selected as the Green Chlorophyll Vegetation Index (GCVI) and the Modified normalized difference water index (MNDWI) from May 10 to June 20. The heading period was the Normalized Difference Red Edge Index (NDRE) and Normalized difference vegetation index (NDVI) from June 30 to September 10. The maturity period with the Plant Senescence Reflectance Index (PSRI) was selected from September 20 to October 20. The 2020 Sentinel-2 time series images were filtered to construct the datasets in the four paddy rice phenological periods. Then, the spectral indices corresponding to each phenological period were calculated and synthesized by the median. Finally, the six images were synthesized into one multi-band image as the original image. The images were segmented with the Simple Non-Iterative Clustering (SNIC) available in the GEE platform. Texture features were also calculated with the Gray Level Co-occurrence Matrix (GLCM). Six models were also established for the paddy rice distribution using the random forest (RF) and Support Vector Machine (SVM), including the pixel-based RF model (PB_RF), object-oriented RF model (SNIC_RF), object-oriented RF model with texture features (SNIC_GLCM_RF), pixel-based support SVM model (PB_SVM), object-oriented SVM model (SNIC_SVM), and object-oriented SVM model with texture features (SNIC_GLCM_SVM). A full extraction of paddy rice planting distribution was implemented using the six models in the study area in 2020. An optimal model was achieved to verify the model accuracy in the field survey of paddy rice. The results show that the RF performed better than the SVM in the extraction of paddy rice planting distribution. The object-oriented method can be widely expected to improve the extraction accuracy of paddy rice planting distribution. The highest extraction accuracy was also achieved in the SNIC_RF model. Correspondingly, the overall accuracy and Kappa coefficient were 96.83% and 0.934, respectively. The field survey data was used to verify the model, where the field survey accuracy of paddy rice was 95.43%, fully meeting the requirements of regional paddy rice planting distribution and area monitoring. [ABSTRACT FROM AUTHOR]

Breeding for wheat stripe rust-resistance could effectively reduce the harm caused by stripe rust. Accurate and efficient phenotyping is an essential step in crop breeding. Furthermore, UAV multispectral imaging had been used for crops growth monitoring, pathological phenotyping, biochemical index estimation and yield prediction widely for it could overcome the limitation of traditional phenotyping methods which has low efficiency and accuracy. In order to solve the problems of poor means and low efficiency of phenotyping of stripe rust in wheat, a dynamic high-throughput phenotyping framework for field stripe rust in population wheat using the UAV multispectral imaging was proposed. The experiment was carried out in Caoxinzhuang experimental farm, Yangling, Shaanxi Province, China. The breeding wheat population with 516 genotypes was used as experimental materials and planted into 600 plots according to the augmented design. Using UAV low altitude remote sensing and multispectral imaging, canopy multispectral images of experimental population wheat infected with stripe rust naturally were collected. At the same time, the data of disease stage and disease severity of stripe rust were recorded according to the disease time and the traditional manual identification standard of stripe rust. After image mosaic, radiometric correction, geometric correction, segmentation of region of interest and background, image clipping and other operations are carried out on the UAV remote sensing raw data, 22 vegetation indices were calculated to analyze. Using 22 spectral vegetation indices as classification features, we firstly established classification models of disease stage and disease severity in stripe rust, and screen out the vegetation indices sensitive to stripe rust disease stage and disease severity through random forest algorithm. At the same time, the random frog algorithm is also used to filter the original features to reduce the contingency caused by using the random forest algorithm to realize the feature screening merely. Using vegetation index with the selected probability ranking of about the top 1/3 according to the random frog algorithm as the input feature, the Support Vector Machine (SVM) algorithm was used to establish the classification model of disease stage and disease severity level in stripe rust, so as to verify the effectiveness of the features screened by the random frog algorithm. Combining the results of two feature selections, three vegetation indices sensitive to disease stage and disease severity were selected, and the differences of disease dynamics of six reference varieties in the population were analyzed based on the time series of response of these indices. The results showed that 1) for disease stages classification of stripe rust, the F1-score of the random forest classifier based on 22 original features and the SVM classifier based on the features sensitive to disease stage selected by random frog were 0.970 and 0.985 respectively, which proved the sensitive features selected by both random forest and random frog are reasonable and the two machine learning classifiers can be used in the classification of the disease stage of stripe rust in wheat population; 2) for disease severity classification of stripe rust, the F1-score of the random forest classifier based on 22 original features and the SVM classifier based on the sensitive features selected by random frog were 0.741 and 0.780 respectively. Although they are lower than the classifier for the disease stage of stripe rust, they have also achieved relatively satisfactory results, which also proved the features sensitive to disease severity selected by both random forest and random frog are reasonable and the two machine learning classifiers can be used in the classification of the disease severity of stripe rust in wheat population; 3) the response of Difference Vegetation Index RedEdge (DVIRE), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Vegetation Index rededge (NDVIrededge) were more sensitive to both disease stage and disease severity in classification models. Especially, DVIRE was selected as the most sensitive feature to disease stress, and can describe both disease stage and severity of stripe rust in field meanwhile. So, dynamic phenotyping by using multitemporal vegetation indices extracted from UAV multispectral images may accurately and finely quantify stripe rust stress state in wheat population and could be considered as a reliable high-throughput method. This study proposed a new method for the field phenotype investigation for wheat stripe rust-resistance breeding, and also provided a reference for the phenotyping of stress resistance breeding of other crops. [ABSTRACT FROM AUTHOR]

Objectives: Net primary productivity (NPP) not only directly reflects the production of vegetation communities, but also is a major factor in determining ecosystem carbon sources/sinks and regulating ecological processes. Methods: We propose a new approach which couples the CASA (Carnegie Ames Stanford approach) model with the CA -Markov model to simulate and predict NPP at the pixel scale, thus reflecting the spatiotemporal distribution characteristics of NPP and predicting the possible directions of changes in NPP. Using the Weihe River basin as the study area, we estimated NPP in 2000, 2005, 2010 and 2015 by the CASA model based on NDVI (normalized difference vegetation index) MOD13Q1 data, meteorological data and vegetation distribution data, analyzed the spatio temporal distribution characteristics of NPP under the changes pattern of climate fators and topographic factors. The CA Markov model is coupled to simulate the changes of NPP in 2020 and predict the changes of NPP in 2025, and 2030.Results: The results show that the coupling of CASA and CA -Markov model has good applicability. Comparing the estimate and predicted values of NPP in 2015, the Kappa coefficient reaches 0.877 6 which indicates that the coupled model has high accuracy and good applicability for NPP prediction. And in the next 10 years, the NPP classes keep transforming to higher coverage areas which will mainly distribute above Zhangjiashan of Jinghe River and Xianyang to Tongguan of Weihe River. Conclusions: This study have important practical significance for understanding the spatial and temporal evolution characteristics and mechanisms of basin vegetation and promoting the ecological security in basin. [ABSTRACT FROM AUTHOR]