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Vegetation is one of the key components in terrestrial ecosystems, particularly in climate, carbon balance, and water cycling. The healthy growth of vegetation can depend mainly on the context of climate change, such as more frequent occurrences of droughts and heat waves. Moreover, the degradation of vegetation can cause irreversible damage to the structure and functionality of terrestrial ecosystems. Compound hot and dry events can further exacerbate their impact on the vegetation, due to the positive feedback between droughts and heatwaves. Therefore, it is very necessary to evaluate the response relationship between vegetation and compound hot and dry events, in order to clarify the ecosystem response to climate change for effective mitigation and adaptation strategies. The correlation between vegetation and drought or hot indices has been commonly used to determine their relationship in recent years. However, the overall correlation coefficient is often insufficient to capture the tail dependence between extreme events and vegetation conditions. In this study, a Vine Copula-based model was constructed to assess the vulnerability of vegetation using the detrended and standardized normalized vegetation index (SNDVI), standardized precipitation and evapotranspiration index (SPEI), and standardized temperature index (STI). The probability of vegetation loss was determined for the different land use and climatic regions in the Loess Plateau from 1982 to 2015. The results show: 1) There was a positive correlation between SNDVI and SPEI in most areas of the Loess Plateau, whereas, a negative correlation was found between SNDVI and STI. Grasslands shared the highest correlation between SNDVI and SPEI, followed by cropland, and the forests with the lowest correlation. There was no significant difference in the correlation distribution between grassland and cropland SNDVI with SPEI from June to August. However, there was a gradual decrease in the correlation between forest and shrub SNDVI with SPEI. The correlation distribution between grassland and cropland SNDVI with STI remained relatively unchanged, while the correlation between forest and shrub SNDVI with STI gradually increased. 2) The vulnerability values of vegetation to extreme compound dry and hot conditions were 0.51, 0.57, and 0.55 respectively, in June, July, and August, which was significantly higher than those to single drought or high-temperature events. Regions with higher vulnerability were concentrated in the northern part of Shaanxi, Ningxia, eastern Gansu, and Inner Mongolia. 3) The vegetation exhibited a significantly higher vulnerability to extreme compound dry and hot conditions in arid and semi-arid regions, compared with the single extreme drought or high-temperature events. In humid and semi-humid regions, vegetation vulnerability to extreme compound dry and hot conditions was generally comparable or slightly lower than that to single drought or high-temperature events. 4) There was a great variation in the vulnerability of different vegetation types. The vulnerability was ranked in the descending order of grassland, cropland, shrub, forest. The vulnerability of grassland to extreme compound dry and hot conditions increased by 26% to 56% during summer, compared with single drought or high temperature events, while the cropland vulnerability increased by 11% to 24% and 19% to 48%, respectively. Since the SNDVI can only represent the vegetation cover, more vegetation indices (such as gross primary productivity (GPP) and net primary productivity (NPP)) can be expected to assess the vegetation dynamics and their response to extreme events, in order to reduce some uncertainties in the future research. [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]

Rainfall erosivity and vegetation cover are two of the most important factors in regional soil erosion. The greater the rainfall erosivity is, the more favorable the soil erosion is, whereas, the better the vegetation coverage is, the less favorable the soil erosion is. Moreover, the Yangtze River Economic Belt has been a prominent ecological and national strategic area in China. It is of great practical significance to determine the relationship between rainfall erosivity and vegetation cover, in order to protect the ecological environment. In this study, the daily-scale rainfall erosivity model and the Mann-Kendall trend test were applied to analyze the temporal and spatial evolution of monthly-scale rainfall erosivity and NDVI using daily precipitation and monthly NDVI data. The Pettitt test and coupling coordination degree theory were then used to identify the temporal and spatial variation of the coupling coordination relationship between rainfall erosivity and NDVI. Finally, the reasons were proposed for the change of the coupling coordination relationship between them. The results showed that: 1) Multi-year average rainfall erosivity from July to October was evenly distributed in the whole region, while there was a decrease from the southeast to the northwest in the rest months from 1965 to 2019. A significant increasing trend of rainfall erosivity was also found in the eastern region in July and September (P<0.05). The rainfall erosivity in the central region showed a significant decrease in January, whereas, there was a significant increasing in the central and southern regions in March. The rainfall erosivity in the southwest showed a significant decrease trend in May. 2) On a multi-year scale, the NDVI of the whole gradually increased from June to September, and it reached the highest level in August. The ratio of the area where the NDVI increased significantly in different months to the total area of the study area all exceeded 30% (P<0.05) from 1999 to 2019. The regions with the significant NDVI increase were distributed mainly in the southwest, central and southern regions. But, the NDVI in the eastern region decreased significantly, where the soil erosion risk was also high. 3) There was a high multi-year average in the coupling coordination degree of rainfall erosivity and NDVI in the east, but a low in the west. A significant decrease was detected in the coupling coordination degree in the central and southwestern regions, while a significant increase was in the eastern regions (P<0.05). The significant change-points were found in the coupling coordination degree of rainfall erosivity and NDVI, which were distributed mainly in the southwest, central and eastern regions in the occurrence period from 2003 to 2014. 4) The reduction of forest area (such as evergreen coniferous forest, evergreen broad-leaved forest, mixed forest, and closed shrub forest) was the important reason for the significant increase of coupling coordination degree in the east, indicating a high percentage of vegetation cover but a very small percentage of the area. Consequently, it is a high demand to rationally increase the forest land in space pattern for the ecological environment. The finding can greatly contribute to further understanding the risk of soil erosion in the Yangtze River Economic Belt. [ABSTRACT FROM AUTHOR]

An accurate extraction of the crop spatial distribution is of great significance for the decision-making on management measures in modern agriculture. Fortunately, the remote sensing images can be widely used as the important data sources for the spatial distribution of crops at present. It is a high demand to extract the high-quality features from the spatial distribution of crops using the remote sensing images. In this study, the Sentinel-2A images were selected to extract the high-precision spatial distribution of winter wheat, in order to avoid the data scale reduction and feature fusion. Firstly, the red edge resource was utilized to classify the important features of winter wheat. The visible light and red edge bands were also combined to effectively reduce the misclassification of pixels for the high accuracy. A downscale model Red Edge Down Scale (REDS) was then established to balance the spatial scale of the data in the Sentinel-2A images, due to the different band resolution between the red edge (20m) and the visible light (10m). The generative countermeasure network was constructed using the three red edge bands of B5, B6 and B7. More importantly, the spatial resolution of B11 shortwave infrared band was reduced from 20 to 10 m, in order to obtain the better consistence in the spatial resolution of visible light and red edge band. The edge blur of image was also prevented from the interpolation (nearest neighbor interpolation). Secondly, the inputs of REDS consisted of the low- and high-resolution channel, correspondingly to the spectral and texture information, respectively. The spatial structure information was then input from the high- into the low-resolution channel. As such, the improved model was achieved in the image data from the high-resolution red edge and short-wave infrared (SWIR) channel. Secondly, the original data was extracted, and then combined into the basic input data, including the three red edge bands after scaling down, the visible light band with a resolution of 10m, and three remote-sensing index products, namely Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Red-Edge1 (NDRE1). Thirdly, the semantic feature extraction model was constructed as the Red Edge and Vegetation Index Feature Network (REVINet) using convolutional neural network. The coding and decoding units were constructed in the REVINet model using residual network. The linear model was used to fuse the multi-scale features for the output by the decoding units. SoftMax function was used as a classifier for the pixel-by-pixel classification. Finally, the segmentation, and the spatial distribution of winter wheat were generated to verify the REVINet model, compared with the ERFNet, U-Net, and RefineNet models. The experimental results show that the smoother contour edge was extracted from the planting area of winter wheat, particularly with the less misclassification. Meanwhile, the recall (92.15%), precision (93.74%), accuracy (93.09%), and F1 score (92.94%) were better than the rest models, indicating the ideal performance. The spatial distribution of the whole research area demonstrated that the winter wheat in China was mainly distributed in the south of the Great Wall in 2022. The relatively high accuracy of extracted areas was achieved with the better coincidence degree, compared with the standard released by the National Statistical Department in 2021. Therefore, the data organization and feature extraction can be expected to serve as the spatial distribution of winter wheat using the Sentinel-2A. The finding can also provide the technical reference for the Sentinel-2A data in the agricultural field. [ABSTRACT FROM AUTHOR]

Net Primary Productivity (NPP) of vegetation is considered one of the main indicators for the carbon fixation capacity of ecosystems in the carbon cycle, particularly for the adaptability of ecosystems to climate change. Among them, the typical phenological factors are the key components of the ecosystem functions in many biological processes, including the Start of the Growing Season (SOS), End of the Growing Season (EOS), and Length of the Growing Season (LOS). However, it is still lacking in the relative importance of phenological and climatic factors to the NPP. The contribution of phenological factors (SOS, EOS, and LOS) to the interannual change of NPP has not been well quantified, due to the complex ecosystem. Therefore, this study aims to extract the phenological information of vegetation using a Cardiovascular-Ames-Stanford Approach (CASA) model, in order to examine the characteristics of spatial and temporal changes of NPP. The Normalized Vegetation Index (NDVI) was used from the MODIS data in Beijing from 2001 to 2020. The interaction between meteorological factors, phenological changes, and NPP was then explored using linear regression, trend analysis, and structural equation modeling. The results show that the SOS was gradually advanced by 0.57 each year on average from 2001 to 2020 over more than 70% of the regions, whereas, the EOS was gradually postponed by an average of 0.51 days per year over more than 90% of the regions. The NPP vegetation increased significantly from 2001 to 2020 (P < 0.05), where the growth rate was greater in the last 10 years than that in the first 10 years. The average annual NPP value was greater in the northern region than that in the southern. There was a strong correlation between the phenological factors (SOS, and LOS) and NPP (P<0.05). The pixel-by-pixel analysis also found that the SOS, LOS, and NPP were significantly correlated in the areas with more than 75% vegetation coverage. The NPP was also significantly affected by the advance of SOS and extension of LOS (P<0.05). The NPP increased by 3.74 g/m² for every single day advance of SOS, while by 2.65 g/m² for every single day extension of LOS. There was no significant effect of the EOS delay in autumn on the NPP. A coupling effect of phenology and climatic factors varied with the season. There was a greater indirect effect of climate through changing phenology (SOS and EOS) on the NPP in spring and autumn, compared with the direct. By contrast, the NPP was more directly affected by climatic factors, temperature, and rainfall in summer. In conclusion, the spring phenological change was an important driving factor for the interannual variation in the NPP. Furthermore, the annual NPP increased to the phenological advance. The findings can also provide an important supplement to determine the vegetation productivity response to the climate change in urban areas. [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]

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]

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]

Crop-type classification and spatio-temporal change detection support rational agricultural management; perennial crop maps reflect changes in crop planting patterns and are useful for economic and social analysis. The satellite images used for mapping crops, however, are not at high spatial and high temporal resolution. They do not provide sufficient data for all stages of growth when mapping spatial distributions of crops in areas with a great variety of agricultural practices and products. A cost-effective solution using short repeat cycle Huan Jing (HJ)-1 Charge-Coupled Device (CCD) imagery and the freely available Landsat-8 imagery was proposed to produce annual crop maps reflecting spatio-temporal changes in planting areas in Jianli County, China. Phenological metrics such as maximum Normalized Difference Vegetation Index (NDVI) values, dates, and the number of days in the growth stages of the different crops were defined from time-series NDVI curves and used for crop classification. Typical planting areas were extracted from the 15 m pan-sharpened Landsat-8 images. The NDVI and time thresholds for phenological metrics were obtained from the NDVI time series curves during the crop growth stage of typical planting areas. Classification rules were established to create crop maps from 2009 to 2016 and applied land-use changes for different crops based on multi-year crop classification maps reflecting the distribution of dominant crops. The high-spatial-resolution China satellite images and crop area data from Jianli statistical yearbook were used to perform an accurate assessment. The average classification accuracy rate was 84% when compared with the high-spatial-resolution imagery, and the classification area matched up to 81.60% with the statistical crop area data. These results indicated that this method provided a possible means for classification permitting regular mapping of crop distributions in complex areas like Jianli County. By the spatio-temporal analysis of summer-harvest crops, it could be found that fewer farmers were willing to plant oilseed rape because of the high labor cost caused by this crop’s low agricultural mechanization level. For autumn-harvest crops, the government set the standard of the lowest purchase price for the middle-season rice, which greatly reduced the risk of planting the middle-season rice for farmers. This might guide farmers’ decisions and lead to small changes in the middle-season rice area. Meteorological data indicated that it had continuous rainfall and waterlogging in the summer of 2016, which led to the reduction of the middle-season rice area, and especially the big reduction of cotton area. [ABSTRACT FROM AUTHOR]

Division of accurate management area can make a great contribution to the ecological conservation in precision agriculture, particularly on preventing excessive fertilization and pesticides. There is a regularly uniform distribution of management zoning in different years in the same field. It is necessary to quantitatively analyze the spatial-temporal changes of zoning for better field accurate management according to local conditions. Most previous studies focused on the accurate partition, with emphasis on the selection of input quantity for high accuracy of zoning. The innovation of this study lies in the quantitative expression of a multiyear pattern after the division, together with the influencing factors of zoning. A corn field was selected as the research area at the Youyi Farm in Heilongjiang Province of northeastern China. The remote sensing images were captured from the Sentinel-2A satellite under the European Space Agency (ESA) during the corn seedling stage in the first ten days of June 2017-2020. The normalized difference vegetation index (NDVI) was extracted from the preprocessed images on the ArcGIS software. The object-oriented segmentation was used to segment NDVI images, where the coefficient of variation was selected to evaluate each segmentation. The coefficients of variation in the NDVI were reduced by more than 70% after segmentation. In the partition, a natural breakpoint was used to classify the NDVI. A superposition analysis was utilized to calculate the spatial transfer matrix. The results showed that the patterns of maize in the seedling stage were similar in the study area. The coefficients of variation for the elevation and slope after segmentation were reduced by 42.857%-57.143% and30.723%-34.940%, respectively, indicating that the growth of crops was affected by terrain factors. There was also a significant correlation between the NDVI and terrain factors, such as the elevation and slope. The sensors of soil temperature and humidity were embedded in four different positions on the slope along the ridge line with obvious topographic relief. A line chart or histogram was obtained for the four NDVI, the average values of soil moisture and temperature on the same ridge line according to the elevation changes in June. The highest NDVI values in the four stages were observed on the sunny slope, while gradually decreased from the top to the bottom of a slope. The reason was that the soil temperature of the sunny slope was the highest, while the soil moisture was sufficient, suitable for the emergence conditions of crops, where the emergence was faster with the high NDVI. On the shady slope, the soil temperature was relatively low, unsuitable for the emergence conditions with a low NDVI. In a specific field, the precise management pattern was similar to the same crop in the growth period over many years. The growth of crops at the early stage and the zonings of precise management depended mainly on the distribution of soil moisture and temperature under the different topographies. The findings can provide a spatiotemporal framework to integrate accurate management with variable fertilization and pesticides in precision agriculture. [ABSTRACT FROM AUTHOR]

Topographical complexity of landscapes and diversity of terrain features have caused to interweave with the scattering mechanism and intensity of full polarization Synthetic Aperture Radar (SAR) data. This makes it difficult for the traditional Wishart-H/α (polarization entropy/scattering angle) classification to effectively classify land types in Karst areas. In this study, a complex Wishart distance measure was used for the class clustering of land type in research areas. A super-box clustering was carried out using the H/α plane. The semi-supervised classification was also carried out, according to the complex Wishart distance between the average coherence matrix of super-box and sample clustering. The obtained data of land classification were further processed using the Polarimetric-Total-Power (SPAN) and the Normalized Differential Vegetation Index (NDVI), where the SPAN was sensitive to buildings and bare rock land, whereas, the NDVI was sensitive to wood land, grass land and cultivated land. An effective classification of land types was finally realized in the Karst areas. Specifically, the complex Wishart distance between two types of sample was calculated to determine the similarity of scattering characteristics in the samples of different land types, and the clustering was also performed during this time. Eight kinds of super-box clustering were divided into using the H/α plane. Three kinds of preliminary clustering were obtained, including the first type of water body, the second type of construction and bare rock land, as well as the third type of wood land, grass land and cultivated land. The SPAN was then used to classify the building and bare rock land, using a threshold in the way of super-box segmentation. The wood land, the grass land, and the cultivated land were classified by introducing a combination of the NDVI and Digital Elevation Matrix (DEM). The improved method can be used to effectively classify the water body, woodland, grassland, farmland, construction land, and bare rock land, with the overall accuracy of 81.45%. To verify the improved method, the another full-polarization SAR data was selected from the typical Karst areas, where the terrain was relatively flat, while the topography was relatively single. This case study demonstrated that the land classification was successfully implemented, where the overall classification accuracy reached 85.66%. The finding can provide a novel way to accurately classify various land types, and thereby serve as an ideal supplementary means to monitor rocky desertification evolution in Karst areas. [ABSTRACT FROM AUTHOR]

Natural wetland is one of the most productive ecosystems, providing with the wide arrays of ecological services to the local communities in particular and global communities in general. The Xiliao River Basin in the eastern edge of agro-pastoral ecotone in northern China, has become an important agricultural and animal husbandry production base, with obviously regional transition and vulnerability of ecosystem. It is necessary to accurate assesse the driving forces of wetland ecosystem evolution, thereby to understanding the long-term evolution of wetland ecosystem, and the contribution rate of climate and human factors to wetland change. In this study, taking the Xiliao River Basin as a research area, a quantitative analysis was made to investigate the evolution and hydrologic attribution of wetlands using the soil and water assessment tool (SWAT Model), Geo-detector, landscape conversion methods. The digital elevation model (DEM), land use, meteorological observation, and vegetation index data were firstly utilized to define the geographical region in the Xiliao River Basin, and then to explore the lake and marsh wetland areas, landscape conversion characteristics, change characteristics of marsh wetland vegetation, and river runoff depth, finally to quantify the types of natural wetland for a long time series and the transformation process. An emphasis was placed on the impact factors of climate or land use on wetland change and their interaction, as well as the contribution rate of driving factors to wetland change. The results showed that the average areas of lake and swamp were 607.14 km2, and 2 657.00 km2, respectively, both of which showed a decreasing trend. The average depth of rive runoff was 48.46 ± 36.82 mm, indicating no obvious change trend. Most types of land use had invaded the swamps and lakes, with the largest area occupied by cultivated lands. Since 1980, there was a dry–wet–dry transformation process occurred in the Xiliao River Basin, except for the wet periods in 1995-2000. From 2000-2015, the mean NDVI and the average annual GPP were 0.30 and 257.73 g/(m2·a) in the growing season of swamp wetlands, both of which showed an extremely significant upward trends and regional differences. There was a highly significant linear positive correlation between the changes of lake area, the runoff depth, precipitation and evapotranspiration, a highly significant conic relationship between the change of swamp area and the above two factors, and a highly significant linear positive correlation between NDVI, GPP, and accumulated temperature in the growing season. The influence of natural factors on wetland changes was greater than that of human factors. The precipitation was the main control factor of wetland changes in the Xiliao River Basin. A single factor driving effect showed that the precipitation was the main controlling factor of wetland change in Xiliao River Basin, where the order of driving capacity of precipitation and evapotranspiration on wetland change from large to small was the marsh area, river runoff, and lake area. A synergistic effect of precipitation and evapotranspiration on lake, swamp, and runoff depth was higher than 70%. This finding can offer the possible mechanisms to control the changes of wetland ecosystem in the Xiliao River Basin. [ABSTRACT FROM AUTHOR]

Cropping intensity (CI) is essentially related to the utilization condition of arable land. A classification method based on high spatial resolution data (TM), and a peak-counting method based on high temporal resolution NDVI data (MODIS NDVI) are mainly used to extract CI in remotely sensed field. However, it is difficult to acquire the high spatial resolution data with high quality characteristics in the most classification method, as three requirements cannot be satisfied concurrently. The first condition is the time requirement, where the high spatial resolution data can be observed in the growing season. The second one is the frequency requirements, where at least one period of data can be obtained in the growing season. The third one is the quality requirement, where the data cannot be polluted by the cloud, mist and other environmental surroundings. Furthermore, the performance was relatively low, when the peak-counting method was applied in some complex surface regions, since the high temporal resolution data has the low spatial resolution (>250 m). A feasible strategy can be made to obtain a high extraction accuracy of CI, in order to improve the spatial resolution of high temporal resolution data in the use of spatiotemporal data fusion (Spatial and Temporal Adaptive Reflectance Fusion Model, STARFM). Taking the hilly region in the eastern Sichuan as a research area, the 2010-2011 time series Landsat-like NDVI dataset with high temporal and spatial resolution was collected to extract the spatial distribution of cropping area for the winter-season in2010 and the CI for summer- and winter-seasons in 2011 in Yanting County, using the STARFM algorithm, Landsat TM and MODIS data. Precision validation between different data in various methods was done at a series of spatial scales (30-4 000 m), referring to the visual interpretation of 1509 random samples, and the spatial distribution of cropping area for winter-season in2010 using Landsat data. The results showed that: 1) Compared to the peak-counting method using Landsat-like time-series NDVI dataset, the overall accuracy of verification was higher for the classification method using Landsat data (89.73% vs. 54.94%), at the scale of 30 m. 2) Compared to the peak-counting method using MODIS time-series NDVI dataset, the overall accuracy of verification was higher 3%-7% for the peak-counting method using Landsat-like time-series NDVI dataset, at the scales from 250 m to 4 000 m. 3) The overall accuracy of verification was higher 0.17, when the data from the new method was spatially aggregated from 30 m to 4 000 m. In the CI result of 2011 in Yanting County, the data in the Mianyang Statistical Yearbook of 2011, and 73 samples from the field survey data in August 2013 were used to validate the practicability of new method. There were very close CI values from the statistical data (1.69) and new method (1.67), indicating that the overall accuracy of verification was 73.97% in the field survey. A high accuracy of extraction and fine spatial details of CI can be acquired, when using the fusion of spatiotemporal data in some complex surface regions. The findings can contribute to the evaluation of food production and carbon sequestration potential in complex surface regions. [ABSTRACT FROM AUTHOR]

Drought is a natural hazard caused by long-term precipitation deficiency and high evaporation resulted from high temperature, which is among the most common disasters around the world. In recent decades, with the intensification of climate change and human activities, drought occurrence is becoming more frequent in China, causing negative impacts on agricultural production, ecological environment and social economy. Therefore, it is significant to explore the impact of drought on vegetation for agricultural production and healthy development of ecological systems. In this paper, the response and sensitivity of vegetation to drought in different climatic zones in China were analyzed, and the effects of different climatic factors, including water balance (precipitation-potential evapotranspiration), annual precipitation, mean temperature and sunshine hours, on the relationship between different vegetation types including forestland, grassland and cultivated land and drought were explored, so as to reveal the response mechanism of ecological vegetation to drought from a macro perspective. Using the self-calibrating Palmer drought severity index (scPDSI) and normalized difference vegetation index (NDVI) data for the period of 1982-2015, the response rule and sensitivity of different types of vegetation to drought were analyzed by applying time-delay correlation method, and the influence of various climate factors on the relationship between vegetation and drought was analyzed by applying linear regression method. The results of the research showed that in northwest China, Inner Mongolia, north China plain and southern Qinghai-Tibet plateau, the correlation of shorter lag time scPDSI (1-3 months) and NDVI was close (Rmax>0.4), which indicated that vegetation growth was more sensitive to drought in these regions and that vegetation biomass were influenced mainly by the spatiotemporal characteristics of the water availability. In the humid areas of south China, including the middle and lower reaches of the Yangtze River and the Pearl River basin, the precipitation is abundant so that the correlation between NDVI and scPDSI was negative, and the response of vegetation was not sensitive to drought. The growth of vegetation is mainly controlled by energy factors such as temperature and sunshine hours. The relationship between vegetation and drought was significantly affected by the average annual water balance, precipitation, sunshine hours and temperature, among which the annual water balance was the main water controlling factor (R²=0.45). It can be concluded that the change of water availability was the key factor behind the change of vegetation vigor. Sunshine hours were the main energy controlling factor. When average annual temperature was less than 5, the effect of ℃ temperature on the correlation between scPDSI and NDVI was also significant. The sensitive areas of drought impact on vegetation were mainly in the northern arid regions, while the southern humid areas had strong resilience and resilience to drought. The sensitivity of different types of vegetation to drought varied greatly, and the order of sensitivity is grassland >forestland>cultivated land. The results can provide a scientific basis for the evaluation of the impact of drought on ecological vegetation in different regions of China, and thus help to guide agricultural and environmental production, protect vulnerable ecosystems and provide implications for sustainable socioeconomic development. [ABSTRACT FROM AUTHOR]

Normalized difference vegetation index (NDVI) is one of the vegetation indices (VI) most widely used. It is related to quite a few indices indicating crop growth, such as leaf area index (LAI), leaf nitrogen accumulation (LNA), chlorophyll content and so on. Several researchers have developed various field crop NDVI measuring instruments to acquire NDVIs, which contains passive sensors and active sensors. Greenseeker, Crop Circle, CropSpec, RapidScan are in common use, and numerous researches are based on them. However, few domestic devices have been developed and corresponding researches haven’t, either. This study assessed the reliability of a novel field crop NDVI measuring instrument based on CropSense crop growth parameter meter (CropSense) together with spectral reflectance sensor (SRS) in varied illumination condition. Field crop NDVI measuring instruments are susceptible to illumination conditions, the height above the canopy and other factors, and it was supposed to detect the performance of sensors through radiometric calibration, which contains laboratory calibration and on-orbit calibration. Integrating sphere was mostly employed for traditional radiometric calibration on field crop NDVI measuring instruments. What was used as the calibration model was the functional relationship between the radiance of integrating sphere and the DN digital number of each channel fitted into the field crop NDVI measuring instruments. Nevertheless, it was found that deviations tended to increase after calibrated by the model built via indoor calibration in the outdoor measurement, with a decline of R2 from 0.997 1 at least of all the calibration model to 0.698 0 for CropSense and 0.877 3 for SRS. In order to deal with such problems, cross calibration method was proposed in this study for the field crop NDVI measuring instruments in the outdoors, with CropSense together with SRS to be calibrated and ASD FieldSpec 4 hyperspectral radiometer (ASD) as reference which can all gather NDVIs. NDVIs were obtained on several objects, referring to diffuse white reference panel, gray cloth, green cloth, blue cloth, cement pavement, naked soil and wheat in varied test periods from 5:30 to 18:30 in sunny days and from 5:30 to 15:00 in cloudy days. The functional relationship between NDVIs acquired through the sensor to be calibrated and ones measured by the reference sensor was taken as an outdoor calibration model to adjust the testing data of CropSense and SRS. Comparing the R2, RMSE and MAE got between field crop NDVI instruments and reference sensor before calibrated, the accuracy and the most suitable conditions for the field crop NDVI measuring instruments were determined. The results showed that RMSEs of NDVIs between CropSense and ASD were 0.074 5-0.104 9 and that between SRS and ASD were 0.026 8-0.054 3 during 6:00-18:00 in sunny days with light intensity from 6.4 to 92.3 klx, and 0.094 2-0.117 9 for CropSense and 0.029 9- 0.070 3 for SRS during 6:00-15:00 in cloudy days with light intensity from 5.2 to 10.8 klx. After calibrated by the model built through outdoor calibration, RMSEs were decreased by 37.1%-41.4% for CropSense and by 10.7%-31.5% for SRS in sunny days; RMSEs were decreased by 33.8%-48.3% for CropSense and by 48.7%-62.6% for SRS in cloudy days. At the same time, the light intensity and solar elevation angle have certain impacts on the accuracy of field crop NDVI measuring instruments. Within the conditions allowed for the instruments to be employed, the performance of the field crop NDVI measuring instruments improves with the increasement of the light intensity; and there is a slight fluctuation with the change of the solar elevation angle. However, the both factors, generally speaking, have no significant influence on the accuracy of the instruments, as long as they are utilized in the illumination conditions more than 5.2 klx. This research results provide a scientific calibration method and momentous reference for passive instruments of vegetation indices measurement. [ABSTRACT FROM AUTHOR]

In order to achieve fast monitoring of regional winter wheat area change, reduce monitoring difficulty, and improve monitoring efficiency and accuracy, the paper proposes a monitoring method based on the relationship analysis of normal difference vegetation index (rNDVI). By selecting 3 counties i.e. Huanghua, Mengcun, and Haixing County, Hebei Province as the study area, and by taking GF-1/WFV data of 2 dates i.e. April 14th, 2014 and April 26th, 2017, the paper conducted the monitoring in the study area by extracting the increased and decreased areas of winter wheat planting areas. Based on rNDVI, the paper built a two-dimensional space with two-year NDVI values of sample points, and thus obtained the monitoring threshold values of the changing areas of the winter wheat by employing the least squares fit method to obtain the upper and lower envelope equations of unchanged ground objects. The result shows that, the overall accuracy of rNDVI algorithm is 90.60%, with the Kappa coefficient of 0.84. Compared with the traditional method of making maximum likelihood classification and then extracting changed winter wheat planting areas, the overall accuracy of this method and its Kappa coefficient are improved by 6.6 percentages and 16.7% respectively. Analysis of the monitoring results on the change of the winter wheat increased area and decreased area shows that, the monitoring method based on rNDVI can effectively improve the identification ability on the change of the land areas such as bare land, linear roads, and fragmented winter wheat areas, and improve monitoring accuracy. The monitoring on the winter wheat changed area was conducted based on 2 pairs of GF-1/WFV data of March 1st, 2014 and March 12th, 2017, as well as May 17th, 2014 and May 20th, 2017. The result shows that the monitoring accuracy of March is relatively low, and the overall accuracies of May and April are close. The above study results show that, fast monitoring method of winter wheat change based on rNDVI can effectively monitor the change of the regional winter wheat planting area. The algorithm used in this method is simple and effective, and it can maintain relatively high accuracy for the sample planting structure of winter wheat planting area, and it can also meet the demand for fast acquisition of crop remote sensing monitoring information. [ABSTRACT FROM AUTHOR]

Geospatial information of irrigated cropland is necessary for the formulation of food policy, water management and climate change studies. In addition to those methods based on pure image classification or non-remote sensing data, spatial reconstruction of statistics by using remote sensing features, a branch of multi-data fusion, with the advantages of less relying on the sampling points with a good consistency with the statistical data, has played an important role in land cover mapping. However, it gains less attention in regional irrigated cropland extraction, which makes it unclear about its applicability in different regions. In this paper, we firstly tested a fusion method based on NDVI data and statistical data of spatial distribution of irrigated cropland in China. Then, quantitative and spatial accuracy assessment and comparisons with other datasets were also earned out for the sake of discussing the availability' of the map. Finally, the possible factors reducing the accuracy of classification were discussed. The results showed that the ratio of irrigation farming decreased and the fragmentation of irrigated croplands increased gradually from east to west. Huang-Huai-Hai and Yangtze River plant regions were the places with the most concentrated irrigation. While in the locations with low precipitation such as northeastern and northwestern areas, irrigation farming was distributed along local water resources. Those irrigation areas were all consistent with the recognized irrigation areas. Quantitatively, the relative errors of more than 90% counties were within 5%, and most of the counties with high relative error (>30%) belonged to Shanxi while the rest were shared by several other provinces. From the view of absolute error, the number of negative ones was much less than positive ones, and this rule was also appropriate on province scale. The total spatial accuracy of the new map was 64.20%, but the values ranged from 31.21% to 90.64% on province scale. Provinces with the accuracies higher than average level were mostly distributed in the eastern areas of the country, and the precision level went lower from north to south. Meanwhile, there was no apparent geographical rule in west. Referring to the comparisons with similar datasets, this fusion method of statistic and remote sensing data could not only perform better quantitatively, but also provide more spatial details than data fusion method without satellite images. In addition, it maintained a same spatial accuracy level with the image classification but accelerated the operating process. These indicated that, the output of the method was both quantitatively and qualitatively comparable to that of similar method in China, yet there was a certain distance with its first application m America. Analysis suggested that, the cropland mask, the method hypothesis and the selected features are the major factors which largely influence the mapping accuracy, so the improvement of the method relies on better cropland maps, and optimization of geographical and spectral features. [ABSTRACT FROM AUTHOR]

The natural mowing grassland plays an important role in grassland animal husbandry, which is not only the forage reserve resource for ensuring the seasonal balance of grassland and livestock, but also can ensure the safe wintering of livestock as forage grassland and post-disaster emergency rescue of forage grass resources reserves in China. However, the information about the resource distribution and biomass estimate of mowing pasture in natural grassland in China is lacked, which limits the effective utilization of regional forage reserve resources. Therefore, a systematic study on forage harvesting in natural grassland is necessary and important. In this study, the estimation of resource distribution and biomass of Chen Barag Banner in the Hulunber meadow steppe in north-eastern China was carried out by using remote sensing technology and field investigation. The field investigation was conducted in 2015 and the investigation time was from the middle of July to the end of July. The results showed that the area of forage harvesting pasture in the Chen Barag Banner regions was 80.28×104 hm2, which accounted for 90.37% of fixed harvesting pasture and 9.63% of temporary harvesting pasture. Among them, the largest area of forage harvesting pasture appeared in Ewenke village, reaching 2.28×105 hm2, followed by Bayanhada village, east Wuzhuer village, Hohnuor village and west Wuzhuer village; the pasture area of each village was larger than 9.00×104 hm2, and the pasture area of Baorixile village was the smallest, which was 0.64×104 hm2. Combining the simultaneous MODIS-NDVI (normalized differential vegetation index) datum with biomass investigation of cutting pasture, a relation model between MODIS-NDVI and biomass was developed, and the relative error of the production estimation model was 20.56%. It was feasible for the power function model to be applied in remote sensing monitoring. The average biomass of Chen Barag Banner natural cutting grassland was 1.24×103 kg/hm2, and the total biomass was 992.90×106 kg, among which the largest village biomass was Ewenke village, reaching 304.21×106 kg, Bayanhada village was 221.01×106 kg, and the other villages accounted for 47.10% of cutting pasture biomass. Temperate steppe (55.94%) and temperate meadow steppe (31.25%) were the main vegetation types for those pastures, and the lowland meadow steppe (4.89%), mountain meadow (5.94%) and desert steppe (1.99%) were distributed with a small area. The area of temperate grassland and temperate meadow grassland was 44.91×104 and 25.09×104 hm2, and the biomass was 532.04×106 and 330.87×106 kg, respectively. The proportions of grassland area and biomass of other types of grasslands were only 12.81% and 13.09%, respectively. The results have made up for the deficiency for the area data of the region forage harvesting pasture and provided data support for analysis of forage harvesting pasture changes of Chen Barag Banner in the Hulunber meadow steppe of north-eastern China in the future. [ABSTRACT FROM AUTHOR]

As the first geostationary satellite of China’s National High-resolution Observation Program (GaoFen Project), GaoFen-4 (GF-4) which launched in December 2015 enjoys the features of high temporal resolution and wide observation with a spatial resolution of 50 m. Meanwhile, GaoFen-4 together with GaoFen-1 (GF-1) polar-orbiting satellite forms a multi-spatial-temporal-resolution ground observation system, which provides more choices for agricultural remote sensing applications. Since the spectral responses of the Panchromatic Multispectral Sensor (PMS) boarded on GF-4 and Wide Field View (WFV) on GF-1 are slight different, there might be difference in reflectance measurements between them. To take fully advantage of their features, the imaging and vegetation detecting capability between two kinds of sensor are of great importance for us to explore in detail using image pairs from the areas of interest. In this study, we aimed at analysis of the continuity of surface reflectance and Normalized Difference Vegetation Index (NDVI) based on GF-4/PMS and GF-1/WFV sensors images. Two image pairs of Northern China Plain and its nearby mountain areas of China, acquired in May 2016, were employed for the comparison. Radiometric calibration, atmospheric correction and geometric correction were operated at first on the Level 1A images. Since there is discrepancy in spatial resolution between GF-4/PMS (50 m) and GF-1/WFV (15 m) images, two pixel-resampling methods were adopted not only to get a comparable surface reflectance image pair, but also to explore the scale effect on their continuity. Statistic indicators such as R2 and RMSE were used to measure the continuity of their surface reflectance and NDVI, and linear regression model was adopted for surface reflectance rectification of GF-4/PMS images. Our results demonstrated that with spatial scale of the pixels increasing, the correlation of surface reflectance between GF-4 and GF-1 is also in increment. However, the uptrend of correlation speeds down or even reverses in blue band from 50 to 80 m. So 50 m is considered as an optimal spatial scale for the continuity study of GF-4 and GF-1 surface reflectance. The surface reflectance of GF-4 and GF-1 has a high positive correlation with R greater than 0.7 in all bands, but there is also bias. The bias includes systematic bias and random bias caused by sensors differences and outside factors, respectively. The major influence factor is the sensors' spectral response function, and it affects red band least. Other outside influence factors may include acquiring time, variation in atmospheric conditions and solar azimuths. Besides, BRDF effects were not under consideration in this paper either. The systematic bias can be reliably modified by a linear regression model, thus to predict GF-4/PMS reflectance from GF-1/WFV reflectance with an elimination of RMSE on surface reflectance between GF-4 and GF-1 remarkably. Meanwhile, the correlation coefficient R of NDVI increased slightly from 0.74 to 0.77 by the process of surface reflectance rectification, which proves that NDVI could weaken the effect of the surface reflectance bias. As a result, NDVI represents a much better continuity between GF-4 and GF-1 both before and after GF-4 surface reflectance rectification. Based on our experiment results, we suggest that GF-4 has a good continuity of China’s GaoFen series satellites that enable its potential applications in the monitoring of agriculture and vegetation. [ABSTRACT FROM AUTHOR]

The timely and accurate crop condition monitoring can provide government policy makers and farmers with information of crop growth, so that they can take promptly field management measures to achieve scientific management and crop yield-increasing. With the development of remote sensing technology, crop condition monitoring by remote sensing has become a research hotspot. Former studies have shown that normalized difference vegetation index (NDVI) is highly correlated with leaf area index (LAI) and leaf chlorophyll content, and can be used to indicate the growth condition of crops. However, it is hard to eliminate the influence of objective factors on crop condition monitoring due to the lack of evaluation criteria. Beside the crop growth condition difference itself, the phenophase difference between fields also has a great influence on crop condition monitoring. To address the problems above, a method of assessing soybean condition by using the historical NDVI time series was designed. In this study, the research target is soybean in Hongxing Farm, which is located in Heilongjiang province. Based on the available multi-spectral HJ-1 CCD data, the historical NDVI dataset from the year 2010 to 2014 was collected. The NDVI variation trend in soybean growth season was analyzed and an inter-annual comparison during soybean growth period was implemented which was integrated with ground data. The high-quality images can't be acquired at day frequency due to the temporal resolution and the cloud influence. A linear interpolation was thus applied to the original data to obtain everyday NDVI dataset. Then a profile, which reflected the soybean growth process was built according to the reconstructed NDVI data from 2010 to 2014. The profile can provide four threshold values every day to categorize soybean condition into five grades which is worst, poor, fair, good and excellent respectively. On the basis of that, the criterion of soybean condition classification was established to assess the growth condition of soybean accurately. A preliminary soybean growth condition map can be produced according to the criterion. Then the critical phenological stages of soybean was extracted based on NDVI time series to reduce the phenophase impact on crop condition monitoring. First, an everyday NDVI dataset at pixel scale in the year 2014 was built using linear interpolation. A simple procedure based on Savitzky-Golay filter and Gaussian function was then implemented to remove the noise in the contaminated NDVI data and fit the growth curve. The result of soybean growth condition monitoring can be calibrated on the basis of the correlation of NDVI and the podding date which can be estimated by analyzing the characteristics of the curve. The yield investigation at field scale was carried out to validate the accuracy of the soybean growth condition assessment method. The results showed that the consistency between soybean condition level and yield level reached 58.5% and increased to 75.6% with the phenological calibration. The pre- and post-calibration consistency were 87.8% and 95.1% respectively when the tolerance was defined as one grade. The proposed method which based on the historical NDVI dataset and the result of key phenophase mapping indicates the ability of reducing the impact of objective factors and phenophase when assessing soybean growth condition. [ABSTRACT FROM AUTHOR]

Remote sensing technology is a major method to obtain spatial distribution and quantity of winter wheat area, and classification method suitable for business operation is a key technology target of annual winter wheat remote sensing monitoring. Aimed at the conditions and demands of winter wheat background survey business operation in agriculture information service, this paper has proposed a weighted NDVI index (WNDVI) based on normal difference vegetation index (NDVI) time sequence. By taking the extraction of 2013-2014 winter wheat area of Anping County, Hebei Province as an example, the algorithm is realized by using GF-1/WFV (wide field view) data. The main idea of the algorithm is to amplify the difference between winter wheat land type and other ground object types by establishing a winter wheat area index based on time sequence images, and to differentiate winter wheat land type from the others and thus to obtain the crop area of winter wheat by automated threshold value setting method. The algorithm includes the following 5 parts: acquisition of winter wheat time sequence images, sample points setting based on grid, establishment of winter wheat area index, identifying winter wheat area index estimation threshold value by iteration, and accuracy validation. Acquisition of images is based on the identification of growth time of winter wheat, and the principle is to ensure to get one GF-1/WFV cloudless image each month. Growth period of winter wheat in Anping County is from October 1st to June 30th of the next year, including 9 growing stages, i.e. seeding, germinating, tillering, overwintering, reviving, jointing, head sprouting, milking maturity and maturity. One GF-1/WFV cloudless image is selected in the middle 10 days of each month, and a total of 9 images are selected for pre-processing and NDVI calculation. Meanwhile, the study area is divided into a certain number of grids, and each grid is further divided into 2×2 sub-grids. The ground object types of central points in upper left and lower right grid are identified by visual interpretation, expert knowledge and field investigation. In this paper, a total of 10×10 equal interval grids with the average grid size of 4.1 km × 4.0 km, as well as 400 sub-grids with the size of 2.05 km × 2.0 km are obtained. The average NDVI values of winter wheat and other ground objects on all upper left centers of this period are calculated. If the winter wheat NDVI is higher than that of other ground objects, the weight of the images of the period is set to 1, and otherwise, set to -1. The winter wheat area index images can be obtained by using the weighted average of NDVI images of all time phases. After obtaining winter wheat area index, it is also necessary to set appropriate threshold value for winter wheat area extraction. The paper takes the visual interpretation classification results of lower right grid points as the basis for threshold value extraction. The specific method is to divide winter wheat area index from small to large with certain intervals, and then to make dimidiate extraction of winter wheat area indices of the lower right centers by taking each divided value as the extraction threshold value. By comparing with the visual interpretation result, the result with the highest accuracy is taken as the optimal winter wheat area index extraction threshold value, which is identified to be approximately 1 600 with self-adaptation approach finally. In all grids, accuracy validation is conducted by taking the 10 plots with equal probability. Accuracy validation results show that the overall classification accuracy has reached 94.4%, with Kappa coefficient of 0.88. The area extraction accuracy of this method is about 1.7% higher than that of conventional method based on NDVI time sequence images. By establishing winter wheat area index, this paper turns a complicated multiple-parameter problem into a single-parameter problem with clearly defined agricultural significance. This method is featured with high automatic degree and stable classification results, and it has been widely applied in the crop area remote sensing monitoring practices in China. [ABSTRACT FROM AUTHOR]

Drought is a natural disaster caused by long-time water shortage and uneven water distribution, which is one of the most common disasters around the world. Under the background of global warming, droughts have become more and more frequent and devastating, causing negative impacts on agricultural production, ecological environment and social economy. For the protection of vegetation and crops, it would be of practical significance to investigate how vegetation develops under the influence of drought which varies in time and space. This research applied the standard precipitation evaporation index (SPEI) derived from high spatial resolution dataset of precipitation and evaporation, as well as the newly published normalized difference vegetation index (NDVI) which represents the growth condition of vegetation on land. Mann-Kendall (MK) test and correlation analysis were taken to study the spatial and temporal evolution of SPEI and NDVI at catchment scale with the help of ArcGIS 10.1 software. The results of the research illustrated that: 1) The MK value of SPEI at catchment scale showed great coincidence with that of the 4189 grid points both in time and space, indicating the feasibility to investigate the response of vegetation growth under the influence of meteorological drought at catchment scale. SPEI was getting increasing slightly at different time scales during 1962-2012, indicating that China as a whole was being more humid. However, taking the time series of 1982-2012 as the research object, China was getting drier slightly at different time scales. In space, both at annual and at seasonal scale, drought trends were mainly detected in catchments in the Loess Plateau area, Northeast and Southwest China, while catchments in Northwest China presented significant humidifying tendency. 2) The NDVI value of the whole China was upgrading with time during 1982-2012, among which the NDVI in spring, growing season and whole year had a significant increasing trend. In space, some catchments of northern Xinjiang and Northeast China demonstrated a significant downgrading tendency, while the upgrading tendency was different owing to different time scales. 3) At annual scale, 56 of the 209 catchments showed significant positive correlation (P<0.05) between SPEI and NDVI, and most of them were located in north Xinjiang, and northern and northeastern China, which indicated that vegetation in arid and semiarid area was significantly affected by droughts. At the same time, negative correlation was mainly distributed in southern China (few of them passed the significant level of 0.95), indicating that droughts in southern China exerted little influence on vegetation growth. 4) Correlation analysis between seasonal NDVI and SPEI showed that in summer and autumn, catchments with positive relation mainly occurred in north area, while catchments with negative correlation mainly occurred in Southeast China and the middle and lower reaches of the Yangtze River plain. In spring and winter, negative relation came to dominate, which mainly occurred in Northeast China, the Yangtze River Basin and most catchments in Southeast China. Lag-relation analysis between seasonal NDVI and SPEI one or two seasons early showed similar correlation distribution as the result of relation analysis without lag. 5) For most of the catchments, air temperature, precipitation and evaporation exerted significant influence on NDVI of vegetation; among them, temperature played a leading role on the vegetation growth in catchments in southern China, while precipitation played a leading role on the vegetation growth in catchments in northern China. Our study on the relationship between meteorological drought and vegetation growth can provide a scientific basis for drought predication and precaution, and thus help to guide agricultural production and protect vulnerable ecosystems. [ABSTRACT FROM AUTHOR]

In recent years, as spectral resolution is being accurate to nanometer level, hyperspectral remote sensing has a unique advantage in the detection of plant life information and the analysis of vegetation growth status. Grasping bidirectional reflectance characteristics of the canopy reflectance spectrum is significant to improve the accuracy of remote sensing applications. Many scholars at home and abroad measured and analyzed the vegetation reflectance rate with the change of view angle, variation of solar zenith angle and season, but they mainly focused on the meadows and forests, and the systematical sensitivity analysis on crop reflectance spectrum was less involved. This paper got a lot of multi-angle hyperspectral remote sensing data based on surface automatic remote sensing platforms. Different from the traditional field spectrometer(ASD Fieldspec), Jaz-Combo 2 spectrometer(Ocean Optics, America) uses the field automatic observation system and has some advantages of low cost, continuous observation all day and real-time automatic remote sensing. This paper continuously observed multi-angel canopy reflectance spectra for winter wheat, and analyzed how various factors influenced the canopy reflectance spectra and 2 commonly used vegetation indices NDVI(normalized difference vegetation index) and EVI(enhanced vegetation index). The results showed that the fluctuation form for the measured canopy reflectance spectra from the self-designed ground automatic remote sensing platforms was found to be consistent with that simulated by the PROSAIL(PROPECT+SAIL) model and also similar to the results studied by Hiker T and Coops N C, et al. The above results demonstrate the measured canopy reflectance spectral data are effective and available, and the calculation method of data is also proved to be reasonable and feasible. With the solar radiation increasing, the canopy reflectance spectra showed an increasing trend and were more obvious in response to weather conditions on sunny days than cloudy days. “Hot spot effect” and “specular reflection” can be used to explain the phenomenon that with the changes of view angle, the canopy reflectance spectra show different distributions. When the direction of observation and the incidence direction of the sun were on the same side(backscatter), the field of view(direct radiating portion of solar rays) coinciding with the incidence direction of the sun had the strongest point of reflection; when the direction of observation and the incidence direction of the sun were on the opposite side(forward scatter), the field of view(direct radiating portion of solar rays) coinciding with the incidence direction of the sun had the weakest point of reflection. Under the above described conditions, vegetation index NDVI and EVI also showed similar variation with canopy reflectance spectra. Influenced by the red band and near infrared band, the change trend of NDVI and EVI was very consistent. The multi-angle relevant analysis of vegetation index NDVI and EVI showed they had a highly significant linear correlation(R2>0.72, P<0.01). So there were no obvious advantages or disadvantages between NDVI and EVI for uniform growth status and full coverage of vegetation, and they would provide the theoretical reference for the choice and design of remote sensing''s view angle and the application of vegetation indices. In this paper, the observations only occurred in the field under uniform growth conditions which was fully covered by the late growth winter wheat, so that the data analysis was not sufficient. In subsequent studies, the data need to be further analyzed with different methods, and crop varieties, crop species and growth period are also needed to verify the findings. [ABSTRACT FROM AUTHOR]

Dynamic monitoring of soil moisture by remote sensing can play a significant role in agricultural production.In recent years, continual attention has been focused on the thought that soil moisture information can be extracted by remote sensing indices.However, the majority of the studies on soil moisture estimation are applied without considering different depths and the time lag that vegetation index(VI) responds to soil moisture.This study investigated the potential of using the moderate resolution imaging spectroradiometer(MODIS) products, including normalized difference vegetation index (NDVI) and enhanced vegetation index(EVI), to estimate soil moisture at distant in situ measured sites.In this study, 30 sites were sampled under the same climatic setting, with the same soil type and the same vegetation type.The MOD13Q1 series data were selected to receive both NDVI and EVI products, which were 16 day composites with 250 meter spatial resolution.We alsoobtainedthe in situ soil moisturedata that were measured once every 30 min from Soil Moisture/Temperature Monitoring Network (SMTMN) in the pasture of northern Tibet.Daily soil moisture was the average of soil moisturesthat were measured once every 30 min.To study the correlation between VIs and soil moisture, the daily time series of soil moisture data had to be processed to match the 16 day VIs.In order to move autocorrelation of most time series data, a simple moving averagemethod was used to identify the seasonal components: 47 point moving average for the daily soil moisture and 3 point moving average for the 16 day VIs.Deseasonalized time series was then produced by subtracting seasonal time series from raw time series.Collocatethe deseasonalized time series of soil moisture at 4 depths(0~5, 10, 20and 40 cm) and the NDVI, EVI in 2012 were used for correlation analysis.Similar analysis was also conducted for the comparison.Pearson Product Moment correlation coefficients were calculated during the growing season (from May to October)for 4 depths.Our hypothesis was that the soil moisture VI regression model developed at one site could be used to estimate soil moisture using VIs at a distant site, providing that other sites had similar soil type, vegetation, and climate regime.Wetested the hypothesis by developing a regression model(at No.2 site) using the deseasonalized NDVI with a 5 day time lag as the independent variable and the deseasonalized soil moisture as the dependent variable at 4 native sites(No.1, No.3, No.4 and No.5) within the growing season.Results showed that the deseasonalized time series and the raw time series had the consistent results between NDVI, EVI and soil moisture at the 30 sites.Both NDVI and EVI needed longer time to respond to soil moisture change.Correlation based on raw time series of VIs and soil moisture was consistent with that based on deseasonalized time series at every depth.The maximum correlation value between deseasonalized NDVI,EVI and soil moisture was from 0.50 to 0.95, and the correlation was significant at the 99% level.Most correlation reached the maximum value whenVIslaged soil moisture by 5-10 days.Regression analysis was conducted using the deseasonalizedsoil moisture time series and the deseasonalizedNDVI time series with a 5 day time lag at No.2 site.Regression models developed at one site and applied to a similar distant site could estimate soil moistures.The correlation coefficient values between estimated and in situ measured soil moisture at different depths varied from 0.64 to 0.68 at No.1 site, from 0.42 to 0.55 at No.3 site, from 0.88 to 0.94 at No.4 site, and from 0.67 to 0.92 at No.5 site, and higher elevationhad smaller correlation coefficient.Thus, elevation is the main factor that affects the accuracy of the regression model.This research can provide valuable information for method selection in pasture soil moisture estimation at different depths by remote sensingindices. [ABSTRACT FROM AUTHOR]

Remote sensing images with the medium spatial resolution can provide long-time series data of the same area, thus are suitable for remote sensing monitoring of major crops in a large scale. Based on the analysis of the time-series spectrum character curve, crop type identification and acreage extraction can be effectively achieved. The time series curve of normalized difference vegetation index (NDVI) can provide the information of crop growth dynamic change, thus is suitable for remote sensing extracting of major crops planting area. We used Jiangsu province as a research area and employed NDVI (normalized difference vegetation index) time-series data from 46 scenes of MODIS images with spatial resolution of 250 m collected from January 1st 2013 to December 31st 2014, reflectance image data of MODIS collected on April 23rd and image data of Landsat to carry out the remote sensing study for winter wheat planting area. First, a time-series curve of NDVI was built from the MODIS data, which was smoothed by an improved Savitzky-Golay filter. The improved Savitzky-Golay filter reserved the authenticity of data at both ends of the NDVI time series while further improving the smoothness of the curve. Based on the reconstruction of NDVI time series analysis, phenology, plant structure and the samples of ground survey, we extracted the key value of typical objects in their phenological growth period emphatically. At the same time, we analyzed the variation trend of winter wheat, woodland and rice (starting time, range, extent and maximum of NDVI) during the growth period. Through comparing and analyzing the characteristics of NDVI time series curves of different objects after smoothing, we defined the different crops, determined the training rules and build the construction of decision tree so that we can extract the distribution of winter wheat preliminarily. The decision tree classification method can be done quickly and efficiently using multi-threshold, however whose threshold is difficult to select accurately as a result of mixed pixel problem. The range of threshold would affect the accuracy of winter wheat planting area extraction. Therefore, in order to solve the problem of mixed pixels, we used surface reflectance image data to extract the endmember spectral curve of winter wheat. With linear spectral mixture model, according to the abundance ratio of winter wheat, we further extracted winter wheat planting area accurately. The distribution of winter wheat in Jiangsu Province was obtained. At last, the experimental results agreed well with the statistical data and high precision of Landsat8 TM image. According to the results of Landsat TM8 supervised classification, the area of winter wheat was extracted and the results were compared to the frontal results. The precision of decomposing the endmember purified was better than ever. Moreover, it accurately reflected the real situation of winter wheat distribution in Jiangsu province. It was concluded that the method in this paper was simple and easy. Accuracy evaluation results showed that the study area of winter wheat planting area extraction accuracy reached 90%, which can accurately reflect the distribution of winter wheat in the study area, The method also indicated that the application in high resolution remote sensing time-series image data with medium resolution image can be accurately extracted crop acreage, for crop planting area of information extraction to provide reference. [ABSTRACT FROM AUTHOR]

Vegetation plays an important role in regulating the terrestrial carbon balance and the climate system, and also overwhelmingly dominants the provisioning of ecosystem services. However, changes occurred on small temporal scales and the persistency or robustness of the changes was often not fully understood. Documentation of changes in vegetation over the most recent years is limited. These documented changes are especially important for policy development and ecosystem conservation and recovery. In this study, with GIMMS NDVI (1982-2006) and MODIS NDVI (2000-2012) datasets in Xinjiang, the spatio-temporal patterns of changes in monthly NDVI and their linkage with change in temperature, precipitation, evapotranspiration, humidity index and human activity were analyzed from March to November at regional and pixel scales. To detect the trend of NDVI during a given period, a least -squares linear regression was applied. To further explore the climatic factors driving NDVI change, interannual correlations between NDVI and climatic variables were calculated using Pearson correlation analysis. To analyze the temporal patterns and dynamic process, we estimated trends of NDVI and the correlation between NDVI and climatic factors over progressively longer periods of 25 to 31 years since 1982 and calculated the percentage of the area that showed a positive or negative trend in the seven nested time series. The monthly NDVI was significantly increased in all months besides November, but there were two distinct periods with opposite trends in most of these months, especially in June to September, from which it increased before 1998 or 1997 or decreased after 1998 or 1997. The trend of reversal of NDVI change also led to that the rate of NDVI increase was notably slowed or stopped as the NDVI record grew in an incremental length from 1982-2006, 1982-2007, ..., to 1982-2012 for March-October. At pixel scale, the areas with significantly change of NDVI in size highly significantly (P<0.01) increased during seven periods in most months, especially for those with a significant (P<0.05) decrease trend. The rate of increase in size of areas with significant decreasing NDVI was larger than that with significant increasing NDVI in all nine months. Consequently, this caused stall or slow increase of regional scale NDVI. The response of vegetation NDVI to climate change for different months was different. The response of NDVI in March-June and September-November was more sensitive to thermal indicator, such as temperature and evapotranspiration. The correlation between NDVI and precipitation and humidity index was stronger in July-August, and areas with significant (P<0.05) correlation were larger. Moreover, the effects of spring temperature on vegetation growth were more substantial at high elevations, such as Altai Mountains, Tianshan Mountains, than that at low elevation. In addition, the impact of climate on vegetation became more significant over a longer time scale. Meanwhile, change in NDVI was significantly (P<0.05) affected by human activities. The change of planting structure and farming methods were also the driving factors. The promotion of agricultural production such as the rapid increase in the proportion of cotton cultivation and the change from flood to drip irrigation may reduce March-May NDVI in some farmland areas. The difference in study time spanning generates different results. Trend analysis during the multiple nested time series may contribute to a better and thorough understanding of NDVI dynamic. Extending the time series as much as possible and focusing on the course of change are particularly important in studies that monitor vegetation dynamics and its relationship with climate change. [ABSTRACT FROM AUTHOR]

HJ satellites with the characteristics of high temporal resolution, high spatial resolution and large coverage, can provide the regional land use/cover classification with high accuracy. Pingluo county is in the arid and semi-arid area of northwest China, the climate and human irrigation activities caused complicated land use/cover type and serious soil salinization in the study area. In order to achieve the dynamic monitoring of land surface information with low cost and high precision, a regional land use supervised classification based on the classification and regression tree (CART) algorithm was developed and discussed in Pingluo county using the multi-temporal and multi-spectral information of HJ satellite CCD data. Firstly, high quality HJ-1 CCD data (the interval was about 20 d) were selected, and preprocessed including geometric correction, radiometric calibration and atmospheric correction. The normalized difference vegetation index (NDVI) were calculated and overlapped together. Secondly, the land use types including double crops irrigated land, one crop irrigated land, paddy, sand, saline-alkali soil, forest land, construction land and water were adopted for the two-level classification system, and the training samples were selected to obtain the typical NDVI time-series curve of each land type. Then, the characteristic parameters (including maximum, minimum, range, the difference between the value of the July 29 and the May 10 phases, the difference between the value of the October 10 and the July 29 phases, the mean value of the October 4 to the November 8 phases) which could reflect the phonological pattern in the area were extracted through the analysis of the NDVI time-series curves. Thirdly, the principle component transform of a multi-spectral image in March with ample soil information was performed for improving the separation between the construction land and saline-alkali land when the first principal component (PC1) was chosen for a parameter band for classification. Finally, a CART decision tree classification was implemented by combining the multi-temporal and multi-spectral parameter bands in the area. The decision tree had a total of 102 leaf nodes and could be expressed as “If…Then…” forms. The results showed that the overall precision of this classification method was 92.26%. The Kappa coefficient was 0.91. The accuracy of the paddy field was the highest which reached 98.23%. The accuracies of sand, one crop irrigated land and water were all greater than 90%. Double crops irrigated land, forest land, saline-alkali land, construction land were all greater than 80%. The participation of PC1 had made great contributions in improving the classification accuracy, especially for construction land and saline-alkali land, their accuracy increased 26.34% and 12.14%, respectively. The overall accuracy of CART decision tree classification was increased 2.58% than maximum likelihood classification. The classification accuracy of vegetation improved the most. The results of CART decision tree classification were more accurate and meticulous than maximum likelihood classification (MLC), and it effectively correct the obvious wrong classification results in MLC. The study indicated that the established typical NDVI time series curves based on HJ-CCD data had strong representativeness for each land use type in this region. The extracted time and spectrum dimensional parameters could distinguish between most of the land categories well. The results of CART decision tree classification were more clear and accurate than MLC. The proposed methods in this study had certain feasibility and applicability, and could provide empirical basis for the further application of HJ-1 CCD data in land use/cover and environment monitoring in different scale area, and also give informational and technical supports for the multilevel and comprehensive land resources and environment management by using HJ satellite as the main data source. [ABSTRACT FROM AUTHOR]

NDVI (Normalized Difference Vegetation Index) time series has been widely used in collecting vegetation information, while most of the present researches about NDVI time series are limited to moderate or low resolution remote sensing images, which affect the accuracy of vegetation information extraction. With the successful launch of the first satellite GF-1 of China High-resolution Earth Observation System, more opportunities have emerged for the construction of NDVI time series with high temporal and high spatial resolution. In this paper, we attempted to build 16 m resolution NDVI time series using images with wide field of view of GF-1 satellite. Different crops have different NDVI time sequence curves during the whole growth period. However, it should be noted that the same crop has a relatively stable growth process and pattern in the same area, which is the basis for the crop classification by using the time series data. While crop phenological characteristics vary largely during a growing cycle, they vary relatively smaller in the different growing cycles. Adopting data containing a complete crop growth cycle can contribute to the extraction of crop phenological information in the construction of NDVI time series. Furthermore, it can avoid the shortage of using data in a calendar year (January to December) to build NDVI time series. In order to carry out studies on crop classification based on GF-1/WFV NDVI time series data, Tangshan, which is located in Hebei Province, China, was chosen as the study area. Through the analysis of NDVI time series curves of samples, we can draw that NDVI time series was able to clearly distinguish crop phenological differences, capture the growth of crop specific features, and identify crop planting patterns in the study area. Irrigation period had the salient features different from that of upland crops before planting paddy rice, which formed the obvious differences compared with other crops. As far as winter wheat was concerned, its NDVI peak was the unique features different from others in overwintering stage. In addition, by analyzing NDVI time series curves in the study area, crop planting patterns can be summarized as follows: winter wheat and summer corn belonged to the planting patterns of two seasons a year, while the rice or peanuts was in a year planting patterns. Based on GF-1/WFV NDVI time series, maximum likelihood method, Mahalanobis distance, minimum distance, neural network, SVM classification methods were used to classify crop in the study area. The results demonstrated that SVM had the best classification accuracies compared to other classification methods, and its overall classification accuracy reached 96.33%. This research showed that GF-1/WFV NDVI with high resolution can be used for crop classification, and can be applied to large area crop classification of remote sensing due to the characteristics of wide coverage. Furthermore, the Harmonic Analysis of Time Series (HANTS) method was used for NDVI time series smoothing, and the results indicated that the processed NDVI time series can better represent different crop phenological characteristics. Then SVM method was used for classifying crop based on smoothed NDVI time series, and the overall classification accuracy was up to 97.57%, which was superior to the one based on the unsmoothed data. The study opens a new era for the domestic high resolution data on agricultural monitoring, and provides insightful reference for the study on the time series of remote sensing classification research. [ABSTRACT FROM AUTHOR]

At present, based on the research of crop yield, estimation of time series of MODIS_NDVI is widely used, but the MODIS data by the spatial resolution of the sensor and features of complex types of limitations has a great influence on the monitoring effect. In this paper, we used Landsat_5_TM and Landsat_7_ETM images of 30 m spatial resolution, constructed a multi day synthetic vegetation index time series data. Two cotton plots in San Joaquin Valley of California were studied to predict cotton yield with the vegetation index. San Joaquin Valley is the Mediterranean climate that belongs to the high yield region of cotton. Landsat_5 and Landsat_7 images combined with the field measured data were used to establish cotton yield prediction model. The tendency of the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) time series and the correlation between vegetation index and cotton yield were analyzed to acquire the optimal cotton yield estimation phase. Then the optimal yield prediction model of cotton was built. Through comparison of the two vegetation indexes respectively as independent variable, the precision of the model can be found. With NDVI as input variable, the decision coefficient and root mean square error were slightly better than EVI. So in this paper, we selected NDVI as yield estimation factor. The results showed that the time series of NDVI based on pure pixels of Landsat images can reveal the period of cotton growing exactly. NDVI of different stages of cotton growth showed differences with time changing, especially in cotton flowering and boll-setting period with 30 m spatial resolution. The correlation coefficient between the flowering and boll-setting period of NDVI and cotton yield was larger than 0.80, and the maximum correlation coefficient was 0.90. During flowering and boll-setting period, NDVI average value determination coefficient of model was 0.82, RMSE was 463.69, thus using flowering and boll-setting stages was more suitable for cotton yield prediction than other growth stages. The NDVI on July 25th was the optimal model input variable for single period, and the average of three growth stage NDVI value before the maximum NDVI was the optimal model input variable for multiple growth stages, and we used the average of NDVI value in the whole flowering and boll-setting stages as input variable for the model. The determination coefficient (R2) reached 0.82; and the R2 was 0.81, RMSE was 477.82 with the model of taking the maximum NDVI value as input variable. The correlation coefficient was large between NDVI of any time in the flowering and boll-setting growth stage and the cotton yield. Thus any growth stage during the flowering and boll-setting period can be used to predict cotton yield. In addition, in the study area, we chose the model of the plot A to verify the plot B model. We concluded that RMSE was 387.53, compared with the common modeling RMSE value, and the test result was ideal. The research results of this paper provided the theoretical basis for the research on the maximum synthesis of MODIS_NDVI. It provided a reference for other crops to establish the yield estimation model. [ABSTRACT FROM AUTHOR]

Normalized difference vegetation index (NDVI) can be used as an ideal indicator of how the crops grow, with which the crops in different growing stages, harmful insects and diseases, water and fertilizer, and the yields can be well predicted. However, the accuracy of NDVI does not remain unchanged due to the ever-changing environmental factors, apart from the impact from the crops growing factors in different stages. The article investigates, using GreenSeeker, the soybean NDVI of its three growing stages of seeding, flowering & podding and maturing in successive hours as the testing unit, for a more accurate monitoring of NDVI affected by air temperature, humidity, solar radiation, and wind speed, etc. in different periods of a day. The results show that the soybean NDVI values, being dynamic in different periods of a day, become smaller from 08:00 or 09:00 am in the morning, reach the valley in 14:00 pm in the afternoon, then give a gradual rise, the whole dynamic process is similar to a quasi-parabola. Moreover, the soybean NDVI values demonstrate different daily variation ranges in different soybean growing stages. From 08:00 am to 18:00pm, the biggest daily variation ranges are respectively among 0.13-0.23, 0.08-0.17 and 0.09-0.19, the biggest relative daily variation ranges are respectively among 20%-26%, 9%-19% and 11%-24%. The correlation study of the soybean NDVI values and the environment meteorological factors show that the changes in soybean NDVI values in its three growing stages are upon great influence by the solar radiation, air temperature, humidity and the wind speed. In four meteorological factors, the solar radiation and air temperature have a negative correlation with the soybean NDVI values at three stages (R2 =0.424, 0.503, 0.631 and 0.602, 0.743, 0.757), humidity shows a positive correlation with the soybean NDVI values(R2 =0.281, 0.435 and 0.654), and the wind speed exerts different influences in different soybean growing stages, specifically, a negative correlation in seeding, flowering & podding (R2 = 0.432, 0.218), and in maturing stage(R2 = 0.127). The regression models were set up to test the impact of four meteorological factors on soybean NDVI and analyze the quantitative relations among them, namely Partial Least Squares (PLS), Stepwise Regression and Ridge Regression. It was found that among four meteorological factors affecting soybean NDVI values, the major factors are the solar radiation and the air temperature, while the minor ones are the wind speed and humidity and their influence on soybean NDVI values can be neglected. In significance tests, predictive accuracy of the three regression models for soybean NDVI in all three growing stages are all statistically significant (P<0.01). By contrast, Ridge Regression has a slightly higher coefficient than partial least squares (PLS) and Stepwise Regression, while the latter two models have almost the same correlation. In contrast of the predictive values and the real test results of the three regression models for soybean NDVI, the Ridge Regression ranks the highest on predictive accuracy, with the Root Mean Square Error (RMSE) of 0.034˛0.018 and 0.016 and R2of 0.820, 0.908 and 0.934 in three stages of seeding, flowering & podding and maturating, followed by a less accurate predictive level of Stepwise Regression and the least accuracy of PLS. Regression model can have a better prediction of daily variation trend of soybean NDVI values and a better accuracy of NDVI monitoring. [ABSTRACT FROM AUTHOR]