Search

As a link for energy transfer between the land and atmosphere in the terrestrial ecosystem, karst vegetation plays an important role. Karst vegetation is not only affected by environmental factors but also by intense human activities. The nonlinear characteristics of vegetation growth are induced by the interaction mechanism of these factors. Previous studies of this relationship were not comprehensive, and it is necessary to further explore it using a suitable method. In this study, we selected climate, human activities, topography, and soil texture as the response factors; a nonlinear relationship model between the karst normalized difference vegetation index (NDVI) and these factors was established by applying a back propagation neural network (BPNN), a radial basis function neural network (RBFNN), the random forest (RF) algorithm, and support vector regression (SVR); and then, the karst NDVI was predicted. The coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the obtained results were calculated, and the mean R2 values of the BPNN, RBFNN, RF, and SVR models were determined to be 0.77, 0.86, 0.89, and 0.91, respectively. Compared with the BPNN, RBFNN, and RF models, the SVR model had the lowest errors, with mean MSE, RMSE, and MAPE values of 0.001, 0.02, and 2.77, respectively. The results show that the BPNN, RBFNN, RF, and SVR models are within acceptable ranges for karst NDVI prediction, but the overall performance of the SVR model is the best, and it is more suitable for karst vegetation prediction. [ABSTRACT FROM AUTHOR]

• Recent forest changes in relation to climate change were investigated for the first time in Romania. • Countrywide NDVI and climate data were processed within forest boundaries between 1987 and 2018. • General greening (increasing NDVI in 65% of all ecological changes) of forests were detected nationally. • Browning (35% decreasing NDVI) trends were found regionally, mainly in the lowland areas of Romania. • National/regional greening/browning trends are mainly explained by climate warming/evapotranspiration increases. Forests have become increasingly stressed by climate change, including in Romania over recent decades, but their response to climate dynamics has not yet been analysed in this country. This study aims to investigate, for the first time, recent ecological changes in forests across Romania, in relation to climate dynamics that affected the country from 1987 to 2018. To this end, countrywide remote sensing (Landsat) data were processed for forest boundaries over the 32 years, in order to compute annual (summer season data) Normalized Difference Vegetation Index (NDVI) datasets, which were subsequently investigated as trends using the Mann-Kendall test and Sen's slope estimator. Simultaneously, various climatic data (temperature, precipitation and reference evapotranspiration) were processed through interpolation techniques and via the same two statistical tools, and were subsequently used for exploring the impact of climate change on Romanian forestlands after 1987. The results highlighted general greening (increasing NDVI) trends of forests nationally (65% of all NDVI changes, which total over 30.000 km2 across Romania), which were dominated by widespread positive NDVI trends detected in the Carpathians region of Romania. This general ecological dynamic suggests a possible enhancement in vegetation productivity in the country's high-altitude areas. Contrasting browning (decreasing NDVI) trends were found for 35% of Romanian forestlands marked by NDVI changes, especially apparent in the Extra-Carpathians (lowland) region, which indicates that in these cases forests were degraded or devitalized. However, the statistical significance of both greening and browning trends is limited across the country. The analysis of climatic trends and of correlations between annual NDVI and climate data indicated that recent warming throughout Carpathians may be an important driving force of forest greening in temperature-limited mountain regions. This finding is supported by an at least moderate intensity of air temperature – NDVI relationships (r correlation coefficient values of ∼50%, the highest of all eco-climatic relationships analysed), generally detected throughout mountain environments. At the same time, it seems that evapotranspiration increase accounted at least in part for forest browning in lowland areas, while the impact of precipitation in forest ecological dynamics remains unclear. All these findings can be useful for a better forest management under the future climate change conditions in Romania. [ABSTRACT FROM AUTHOR]

The current study evaluates consistency among three Normalized Difference Vegetation Index (NDVI) datasets, namely GIMMS, MODIS and SPOT, to characterize alpine vegetation dynamics (greening and browning) across High Mountain Asia (HMA) in 2001–2015. The utility of these datasets is explored to evaluate the vegetation's variability at different spatial-temporal scales and, elevation, and to compare their spatial trends and distribution patterns. In addition to the Pearson correlation coefficients performed to quantitatively analyze the consistency and inconsistency of each dataset, an NDVI quality control (QC) layer and Landsat NDVI are also used to evaluate the findings. The results indicate that the GIMMS has the highest NDVI mean, while SPOT has the lowest. However, GIMMS also showed a browning trend for both Tianshan (TS) and the Qinghai Tibet Plateau (TP) at a rate of −0.3 × 10−3 per year, whereas MODIS and SPOT exhibit a greening trend (TS MODIS = 0.5 × 10−3 per year, TS SPOT = 0.6 × 10−3 per year, TP MODIS = 0.9 × 10−3 per year, TP SPOT = 1.6 × 10−3 per year). Furthermore, MODIS-SPOT shows the highest correlation (R GREEN = 0.73; R BROWN = 0.47), followed by MODIS-GIMMS, and GIMMS-SPOT. The overall, NDVI trend consistency appears to be higher in TS. Finally, the consistent greening pixels mainly distributed in central TP stretching to the northeastern part, and in western stretching to eastern TS, account for 32.14%, while 8.32% of consistent browning pixels are concentrated in southwestern TP and central TS. The inconsistent pixels account for 59.54%, with 39.21% of inconsistent greening pixels being widely distributed across HMA, and 20.58% of inconsistent browning pixels being relatively pronounced in central TS and southern TP. This study provides baseline inferences for the selection and reconstruction of data in follow-up studies on vegetation dynamics. • Three different vegetation indices products were explored in High Mountain Asia. • Consistency evaluation of NDVI products at different elevations, time and spaces. • Consistent greening pixels accounted for 32.14% and 8.32% of browning pixels. • Inconsistent pixels were of 59.54% with 39.21% of greening and 20.58% of browning. • NDVI value of GIMMS is large, but the trend is less than that of MODIS and SPOT. [ABSTRACT FROM AUTHOR]

• Significant greening took up 76.7% of Mu Us Sandy Land. • Temperatures were the main climatic driving factor at the monthly time scale. • Precipitations dominated the vegetation growth at the annual scale. • Human activities accounted for 56.44% of the NDVI change. Critical ecological restoration and reconstruction information can be presented by distinguishing the vegetation dynamics due to human activities and climate changes and by determining the main driving factors. In this study, the long-term (1982–2020) satellite-derived Normalized Difference Vegetation Index (NDVI) data sets and climatic factors were employed to analyze the spatiotemporal patterns of the vegetation dynamics in Mu Us Sandy Land (MUSL). In addition, this study investigated their links to variations of temperatures (T) and precipitations (P), and the contribution of human activities and climate change to the vegetation dynamics using the residual analysis were investigated. The results revealed a statistically significant overall greening trend in NDVI changes in 1982–2020 at the MUSL regional scale. To be more specific, this greening trend underwent two distinct periods separated by the breakpoint in 2006. The NDVI first slowly increased before 2006, and then the greening trend rose dramatically after 2006. At the pixel scale, pixels exhibiting greening tendencies exceeded browning trends observably. The correlation between the NDVI and P and T was largely positive, indicating that the P and the T could well explain the dynamic vegetation changes. However, the climatic driving factors for the vegetation growth in the MUSL varied at different time scales. On the monthly scale, T was the main climatic driving factor for the vegetation growth, while P dominated at the annual scale. In addition, the residual analysis indicated that human activities accounted for 56.44% of the NDVI change, while climate change accounted for 43.56%, demonstrating that human activities acted as the dominant drive for vegetation changes. As opposed to the above, degradation was attributed to human activities (52.23%) and climate change (47.77%), respectively. These results are expected to deepen the insights into the driving factors of vegetation change and provide theoretical guidance for ecological management and sustainable development in the MUSL. [ABSTRACT FROM AUTHOR]

With the impact of climate change and the implementation of the "Grain-for-Green" ecological restoration project, the dynamics of water and heat transport and storage in the Loess Plateau (LP) have changed substantially. However, studies on the vulnerability of the LP ecosystem in response to ecological restoration are less concerned. Based on the normalized difference vegetation index (NDVI) and the gross primary production (GPP), this study assessed the spatial change of ecological vulnerability (EV) in the LP during 2001–2019. Results showed that (1) the annual mean NDVI and GPP was high in the southeast of LP and low in the northwest. The trend of NDVI and GPP presented a significant upward trend over approximately 85% of the regions; (2) the ecological vulnerability of LP displayed a low-to-high gradient from southeast to the northwest. Medium (0.3–0.45) and heavy (≥0.45) vulnerability levels occurred in the northwest of LP, which was attributed to less vegetation and serious land degradation; (3) forestland has the lowest vulnerability whereas grassland, sparse vegetation and urban land exhibited higher vulnerability, with the respective percentages of vulnerable areas of 63.8%, 90.4%, 70% in terms of NDVI and 87.1%, 49.8%, 38.4% in terms of GPP; and (4) the EV of LP was impacted by climate and terrain. The magnitude of vulnerability was negatively correlated with precipitation, temperature and slope, and positively correlated with drought. It is suggested that precipitation, slope and drought stress were the dominant factors of EV. The findings provide guidance for appropriate planning of ecological restoration in the LP. • Achieving a rapid assessment of ecological vulnerability in terms of NDVI and GPP. • Ecological vulnerability showed an increasing trend from southeast to northwest in the LP. • Climate and topography had noticeable effects on ecological vulnerability. • Providing guidance for appropriate planning of ecological restoration on the LP. [ABSTRACT FROM AUTHOR]

The applications of Normalized Difference Vegetation Index (NDVI) time-series data are inevitably hampered by cloud-induced gaps and noise. Although numerous reconstruction methods have been developed, they have not effectively addressed the issues associated with large gaps in the time series over cloudy and rainy regions, due to the insufficient utilization of the spatial, temporal and periodical correlations. In this paper, an adaptive Spatio-Temporal Tensor Completion method (termed ST-Tensor) method is proposed to reconstruct long-term NDVI time series in cloud-prone regions, by making full use of the multi-dimensional spatio-temporal information simultaneously. For this purpose, a highly-correlated tensor is built by considering the correlations among the spatial neighbors, inter-annual variations, and periodic characteristics, in order to reconstruct the missing information via an adaptive-weighted low-rank tensor completion model. An iterative ℓ 1 trend filtering method is then implemented to eliminate the residual temporal noise. This new method was tested using MODIS 16-day composite NDVI products from 2001 to 2018 obtained in Mainland Southeast Asia, where the rainy climate commonly induces large gaps and noise in the data. The qualitative and quantitative results indicate that the ST-Tensor method is more effective than the five previous methods in addressing the different missing data problems, especially the temporally continuous gaps and spatio-temporally continuous gaps. It is also shown that the ST-Tensor method performs better than the other methods in tracking NDVI seasonal trajectories, and is therefore a superior option for generating high-quality long-term NDVI time series for cloud-prone regions. • A new method was proposed to reconstruct long-term NDVI series in cloudy regions. • It is the first time to introduce low-rank tensor completion in NDVI reconstruction. • Information among spatiotemporal and periodic neighbors is synthesized simultaneously. • Robustness of iterative trend filtering method in keeping feature points is proved. • Continuous gaps are well addressed and NDVI seasonal trajectories are well tracked. [ABSTRACT FROM AUTHOR]

Vegetation is one of the most important indicators of climate change, as it can show regional change in the environment. Vegetation health is affected by various factors, including drought, which has cumulative and time-lag effects on vegetation response. However, the cumulative and time-lag effects of drought on different terrestrial vegetation in China are still unclear. To address this issue, this study examined the cumulative and time-lag effects of drought on vegetation from 2001 to 2020 using the Standardized Precipitation Evapotranspiration Index (SPEI) in the Global SPEI database and the Normalized Difference Vegetation Index (NDVI) in MOD13A3. Based on Sen-Median trend analysis and the Mann–Kendall test, the change trend and significance of the NDVI from 2001 to 2020 were explored. The Pearson correlation coefficient was used to analyze the correlation between the SPEI and NDVI at each cumulative scale and time-lag scale and to further analyze the cumulative and time-lag effects of drought on vegetation. The results show the following: (1) The NDVI value increased at a rate of 0.019/10 years, and the increased area of the NDVI accounted for 80.53% of mainland China, with a spatial trend of low values in the west and high values in the east. (2) The average SPEI cumulative time scale most relevant to the NDVI was 7.3 months, and the cumulative effect demonstrated a high correlation at the scale of 9–12 months and revealed different distributions in different areas. The cumulative effect was widely distributed at the 9-month scale, followed by the 12-month scale. The correlation coefficients of cumulative effects between the SPEI and NDVI for cropland, woodland and grassland peaked at 9 months. (3) The average SPEI time-lag scale for the NDVI was 6.9 months, and the time-lag effect had the highest correlation coefficient at the 7-month scale. The strongest time-lag effect for cropland and grassland was seen at 7 months, while the strongest time-lag effect for woodland was seen at 6 months. Woodland had a lower time-lag effect than grassland at different scales. The research results are significant for their use in aiding the scientific response to drought disasters and making decisions for climate change precautions. [ABSTRACT FROM AUTHOR]

Given rapid global urban development, increases to impervious surfaces, urban population growth, building construction, and energy consumption result in the urban heat island (UHI) phenomenon. However, the spatial extent of UHIs is not clearly mapped in many UHI studies based on a remote sensing approach. Therefore, we developed a method to extract the spatial extent of the UHI during the period from 2000 to 2021 in Nanjing, China, and explored the impact of urban two- and three-dimensional expansion on UHI spatial extent and UHI intensity. After cropland effects (i.e., bare soil) were eliminated, our proposed method combines the Getis-Ord-Gi* and the standard deviation of the normalized difference vegetation index (NDVI STD) to extract the UHI area from Landsat 5 and Landsat 8 images using land surface temperature (LST) spatial autocorrelation characteristics and the seasonal variation of vegetation. Our results show the following: (1) Bare farmland has a large influence on the extraction results of UHI—combined with the seasonal variation characteristics of NDVI STD, the impact of bare soil on UHI extraction was highly reduced, strongly improving the accuracy of UHI extraction. (2) The dynamics of the UHI area are consistent with the changes in the built-up area in Nanjing at both spatial and temporal scales, but with the increase of the urban green ratio, the UHI area of mature urban areas trends to decrease due to the cooling effect of green space. (3) The accumulation of population and GDP promote the vertical expansion of urban buildings. When the two-dimensional expansion of the city reaches saturation, the UHI intensity is primarily affected by three-dimensional urban expansion. [ABSTRACT FROM AUTHOR]

Trends for increased vegetation greenness based on satellite-derived data have been repeatedly published for the temperate grassland biome (including forest steppes) of eastern Inner Asia since 1982. Although this greening trend has been attenuated or partially reversed by drought in the early twenty-first century, linear increases in the Normalized Difference Vegetation Index (NDVI) or other parameters of vegetation greenness are nevertheless evident when the period since 1982 is regarded. However, the question arises whether these trends are part of a long-term trend driven by climate change, as simultaneously forests in the region show widespread drought-induced growth reductions and mortality outbreaks. Therefore, we hypothesized that the post-1982 greening trend was neither part of a long-term trend nor unprecedented. To test this hypothesis, we analyzed monthly maximum NDVI data from AVHRR time series and correlated these data with standardized tree-ring data of Larix sibirica from two regions of western Mongolia. We used linear regression to model the NDVI from tree-ring anomalies and to reconstruct the NDVI since 1940. These reconstructions show that the availability of satellite-based NDVI data coincidentally began during a dry period of low vegetation greenness in the early 1980s and was followed by a wet phase in the 1990s, producing the linear greening trend. No positive long-term trend in the reconstructed NDVI was observed from 1940 to 2010. This result rules out a recent climate change-driven greening trend for the grasslands and forest steppes of western Mongolia and calls into question its existence for all of eastern Inner Asia. [ABSTRACT FROM AUTHOR]

Rapid and accurate monitoring of spatial distribution patterns of winter wheat over a long period is of great significance for crop yield prediction and farmland water consumption estimation. However, weather conditions and relatively long revisit cycles often result in an insufficient number of continuous medium-high resolution images over large areas for many years. In addition, the cropland pattern changes frequently in the fallow rotation area. A novel rapid mapping model for winter wheat based on the normalized difference vegetation index (NDVI) time-series coefficient of variation (NDVI_COVfp) and peak-slope difference index (PSDI) is proposed in this study. NDVI_COVfp uses the time-series index volatility to distinguish cultivated land from background land-cover types. PSDI combines the key growth stages of winter wheat phenology and special bimodal characteristics, substantially reducing the impact of abandoned land and other crops. Taking the Heilonggang as an example, this study carried out a rapid mapping of winter wheat for four consecutive years (2014–2017), and compared the proposed COV_PSDI with two state-of-the-art methods and traditional methods (the Spectral Angle Mapping (SAM) and the Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA)). The verification results revealed that the COV_PSDI model improved the overall accuracy (94.10%) by 4% compared with the two state-of-art methods (90.80%, 89.00%) and two traditional methods (90.70%, 87.70%). User accuracy was the highest, which was 93.74%. Compared with the other four methods, the percentage error (PE) of COV_PSDI for four years was the lowest in the same year, with the minimum variation range of PE being 1.6–3.6%. The other methods resulted in serious overestimation. This demonstrated the effectiveness and stability of the method proposed in the rapid and accurate extraction of winter wheat in a large area of fallow crop rotation region. Our study provides insight for remote sensing monitoring of spatiotemporal patterns of winter wheat and evaluation of "fallow rotation" policy implementation. [ABSTRACT FROM AUTHOR]

[Display omitted] • Analyzed spatiotemporal characteristic of NDVI variation affected by climatic factors. • Predicted future vegetation conditions in China combined with two carbon scenarios. • Insignificant impact of carbon concentration on NDVI trend in vegetation types zone, but large impact in partial areas. • Different influence of carbon concentration on NDVI cycle with each month. • No impact of carbon concentration on NDVI amplitude. Currently, studies analysing the relationship between climatic factors and vegetation may present some problems, including considering insufficient spatiotemporal characteristics, contradictory results and deficiencies in reflecting vegetation changes in the future. To address this, we first analysed the spatiotemporal characteristics of the impact of climatic factors on the normalized difference vegetation index (NDVI) using a multiple regression model constructed from the perspective of water deficit. Second, the two carbon emission scenarios provided by Coupled Model Inter-comparison Project phase 5 (CMIP5) were combined to predict the future vegetation conditions in China. Finally, trend analysis, Sen's slope and cycle analysis methods were employed to explore the trend of the NDVI and its spatiotemporal characteristics in the arid and semiarid regions of China. The results show that the impact of climate change on vegetation possesses distinct spatiotemporal characteristics, and the influences are different depending on the month and location. Vegetation has greened in most arid and semiarid areas of China during historical periods. In the future, NDVI variation will show a fluctuating tendency, and increasing and declining trends for the NDVI will alternately occur monthly. Although both precipitation and temperature will increase in the future, spring vegetation will be threatened by stress in some areas due to severe drought caused by the increase in evaporation, which is caused by increasing temperature. The impact of climatic factors on the amplitude and cycle of vegetation type zones varies monthly. The impact of carbon concentration on the NDVI in vegetation type zones was not significant but was relatively large in some areas. The influence of the carbon concentration on the NDVI cycle is different each month. Carbon concentration basically has no effect on amplitude. [ABSTRACT FROM AUTHOR]

The term flash drought describes a special category of drought with rapid onset and strong intensity over the course of days or weeks. To characterize the impact of flash droughts on vegetation coverage, this study assessed the influence of soil water deficits on vegetation dynamics in the northeastern South America region by combining time series of vegetation index, rainfall, and soil moisture based on satellite products at a daily time scale. An 18-year analysis, from 2004 to 2022, of the Normalized Difference Vegetation Index (NDVI), Standard Precipitation Index (SPI), and surface soil moisture (SSM) was performed based on three different satellite remote sensing estimates: the spinning enhanced visible and infrared imager (SEVIRI) and the integrated multi-satellite retrievals algorithm (IMERG), and the soil moisture and ocean salinity (SMOS). The results revealed that flash drought events exerted dramatic impacts on terrestrial ecosystems in the study region during the first two decades of the 2000s, with changes in seasonal and regional vegetation dynamics. Further, the fixed-threshold values to characterize flash drought events were suggested as the timing when the water deficit was less than −1.0 units and vegetation index reached the value equal to +0.3 during five consecutive weeks or more, coupled with soil moisture rates below 40% percentile, leading to a strong region-wide drought throughout the entire region. Additionally, the results of linear least squares trend analyses revealed a negative trend in the pentad-SEVIRI radiance for the solar channel 1 within the semiarid ecosystems of the study region (i.e., the Caatinga biome) that was suggested as a reduction in clouds in the 18 years of the study. Developing combined threshold measures of flash drought based on satellite remote sensing may lead to an accurate assessment of flash drought mitigation. [ABSTRACT FROM AUTHOR]

• Satellite data were combined with transfer learning method to identify cotton area. • Two frameworks were formed for cotton mapping by integrating LSTM and RF with TrAdaBoost. • The improved approaches performed well in cotton detection with few target samples. • The cotton distribution map in Uzbekistan was generated. Cotton is an important cash crop and strategic material in the world, as the main source of natural and renewable fiber for textiles. Accurate and timely cotton distribution maps are crucial for monitoring and managing cotton cultivation system. Remotely sensed data have been widely used in cropland mapping, whereas relatively less attention has been paid specifically to cotton mapping partly due to the difficulty in obtaining the training samples over large regions. To resolve this issue, this study introduced the instance-based transfer learning to identify the cotton cultivated areas using the remotely sensed images. The annual time series of normalized difference vegetation index (NDVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) images was adopted as input features. The random forest (RF) and long short-term memory (LSTM) algorithms integrated with the Transfer AdaBoost (TrAdaBoost) algorithm were adopted to generate the RF-based and LSTM-based TrAdaBoost approaches, respectively. The experiments were conducted in Arkansas State of the United States, where the cropland data layer (CDL) was available and utilized as a source of auxiliary data. The results showed that both the RF-based and LSTM-based TrAdaBoost performed better than the original RF and LSTM under the condition that the training samples in the target domain were limited. The advantages of transfer learning were much greater when the percentage of training samples from the source domain equals 80%. The two transfer learning approaches were then applied to identify the cotton cultivated areas in Uzbekistan. The cotton areas detected by MODIS images in 2018 were agreed well with the statistics at the sub-national level, with the R2 values of 0.57 and 0.64, respectively. These results demonstrate the potential of the RF-based and LSTM-based TrAdaBoost approaches in generating the cotton distribution maps when there are few samples in the target domain. [ABSTRACT FROM AUTHOR]

Accurate estimation of the spatial-temporal distribution of phreatic evapotranspiration (ET) is critical for managing water resources and preventing soil salinization in arid and semiarid agricultural areas where substantial water-saving efforts are needed. Traditional phreatic ET estimation approaches either are for small scales or cannot consider spatial and/or temporal variations in phreatic ET. This study developed a new approach for estimating the spatial-temporal phreatic ET based on the normalized difference vegetation index (NDVI) and measured water table depths. The NDVI was used to calculate the actual evapotranspiration (ET c act) by scaling it with the reference crop evapotranspiration (ET o). The water table depths measured during the periods of no other factors (i.e., precipitation, irrigation and groundwater extraction) were used to establish an equation used to estimate the phreatic ET contribution coefficient (defined as the ratio of phreatic ET to the corresponding ET c act). To improve estimation accuracy, a time-related correction factor was considered in the equation for estimating the phreatic ET. The new approach was used to estimate monthly phreatic ET with a spatial resolution of 250 m in the Hetao irrigation district, located in arid Northwest China. The estimated phreatic ET at different spatial and temporal scales matched well with the groundwater balance model results. The results show that the spatial distribution of phreatic ET is affected by both natural factors (e.g., land cover types) and human activities (e.g., groundwater extraction, planted crops). In the Hetao irrigation district, phreatic ET contributes an average of 24.4% to ET c act during the non-freezing-thawing period (from June to November), demonstrating the essential role of phreatic ET in supporting crop growth and the general ecological environment in arid areas with shallow water table depths. The new approach of estimating spatial-temporal phreatic ET may be used for designing effective and efficient water resource management policies at the regional scale. • A new approach based on NDVI is developed for estimating spatial phreatic ET. • The vital parameters of the approach are obtained from long time-series observations. • The approach can yield reasonable phreatic ET at different spatial-temporal scales. [ABSTRACT FROM AUTHOR]

• NIRv and SIF were more effective at estimating phenological information than NDVI and EVI. • SOS and EOS played important roles in determining seasonal variabilities of GPP. • The variation of annual GPP could be captured by GSL GPP × GPP max , GSL NIRv × NIRv max , and GSL SIF × SIF max effectively. Phenology plays an important role in affecting carbon sequestration in terrestrial ecosystems in the context of climate change. Remote sensing techniques have been widely used to investigate land surface phenology and the effects of phenology on ecosystem production at regional and global scales. Recently, the near-infrared reflectance of vegetation (NIRv) and solar-induced chlorophyll fluorescence (SIF) have been shown to be more promising metrics of gross primary production (GPP) than the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI). However, there is a lack of comparison in the performance of these techniques for deriving phenological metrics. In this study, we explored the consistency in phenological metrics derived from both remote sensing approaches (NDVI, EVI, NIRv, and SIF) and flux tower GPP at six plantations (two broadleaf forests (BF) and four coniferous forests (CF)) in eastern China over the period 2006–2020. The vegetation indices (NDVI, EVI, NIRv) were derived from MODIS data, and SIF was based on the global, OCO-2 based SIF product (GOSIF). We further evaluated the effects of spring and autumn phenology on GPP. The results showed that the flux tower GPP was effectively tracked by NDVI, EVI, NIRv, and SIF (P < 0.001). Meanwhile, the phenological metrics derived from EVI, NIRv, and SIF, including the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (GSL), had significant relationships with those derived from GPP at the six plantations (P < 0.05). NIRv and SIF were more effective at estimating phenological information than NDVI and EVI. In addition, the root mean squared deviation (RMSD) values between the GPP- and NIRv-retrieved phenological dates were less than those derived from NDVI, EVI, and SIF at the BF sites. However, the differences among RMSD values of NDVI, EVI, NIRv, and SIF were not significant at the CF sites. The linear regression analysis showed that the advance of SOS GPP (i.e., SOS derived from GPP) significantly increased GPP (R2=0.29, P < 0.05) over the period from March to April, and the delay of EOS GPP (i.e., EOS derived from GPP) remarkably enhanced GPP (R2=0.61, P < 0.001) over the period from September to October at the BF sites. The relationship of EOS GPP with GPP (R2=0.90, P < 0.05) over the period September-October was strong at the CF sites. In addition, the variations of annual GPP could be captured by GSL GPP × GPP max , GSL NIRv × NIRv max , and GSL SIF × SIF max effectively across the BF and CF sites. These findings can help us understand the potential ability of NIRv and SIF in estimating phenological metrics and in revealing the effects of vegetation phenology on the carbon cycle. [ABSTRACT FROM AUTHOR]

Site-specific crop management demands maps which present the content of the main macronutrients. Such maps are prepared based on optimized soil sampling within management zones, which should be quite homogenous according to nutrient content, especially the content of potassium and phosphorus. Delineation of management zones is very often conducted using soil apparent electrical conductivity (EC) or other variables related to soil condition, including satellite-derived vegetation indices. In this study conducted in North-Western Poland, relationships between soil electrical conductivity and the satellite-derived normalized difference vegetation index (NDVI) of various crops (wheat, barley, and rapeseed) with soil pH and content of P, K, and Mg were evaluated. Strong relationships were observed between NDVI of cereals with potassium content in soil. Correlation coefficients for wheat ranged from 0.37 to 0.60 for average potassium content for three years and from 0.05 to 0.63 for barley. Stronger relationships were observed for the years 2018 and 2019 when NDVI was based on Sentinel-2 data, while weaker for year 2017 when Landsat 8 NDVI was used. Relationships between EC and macronutrients content were similar to those observed with NDVI. Satellite-derived NDVI of cereals can be used as a variable for the delineation of within-field management zones. The same relationships were much weaker and not consistent for winter rapeseed. [ABSTRACT FROM AUTHOR]

• The responses of the NDVI to climatic factors were spatiotemporal heterogeneous. • Karst NDVI in southwest China was predicted based on multiple factors. • The model performance regarding NDVI at the spatial scales was relatively good. • The 1 km resolution NDVI data were suitable for predicting karst vegetation. Karst vegetation (KV) is one of the most important indicators for maintaining the surface energy balance in southwestern China. The spatial pattern of KV is mainly affected by climate, human activities, and environmental factors. The relationships between the KV and these factors are complex and nonlinear. Most previous studies on the nonlinear relationship characteristics and regional regularity were not comprehensive. In this study, the correlation and the time-lagged response of KV to climatic factors were investigated using Pearson correlation analysis, and a nonlinear model of the relationships between the karst normalized difference vegetation index (NDVI) and multiple factors was built based on a back propagation neural network optimized using the beetle antennae search algorithm (BAS-BP), and then, the karst NDVI was predicted. The results showed that (1) in most karst regions, the seasonal NDVI was mainly influenced by temperature, but the autumn NDVI was mainly affected by precipitation. The correlation between the interannual variation in the NDVI and the interannual variation in the precipitation was higher than the correlation between the interannual variation in the NDVI and the interannual variation in temperature. The NDVI and the other climatic factors were not strongly correlated. The NDVI responded to the climatic factors with different time-lagged intervals in different spatiotemporal scales. (2) At multiple spatial scales, the mean correlation coefficient (R) and means squared error (MSE) values of the vegetation prediction in the different geomorphic areas were 0.6565 and 0.0072, respectively. The maximum R was up to 0.9059. In different lithology areas, the mean values of R and MSE were 0.6898 and 0.0072, respectively. The maximum R value was 0.9142. (3) The prediction model of the interannual variation in the NDVI was trained, and it was then tested for the validation period. The R values ranged from 0.5299 to 0.7744, with an average of 0.6606. In contrast to the prediction results on different spatial scales, the model's performance regarding the interannual variation in the NDVI was relatively poor. (4) The mean R values were 0.6708, 0.5575, and 0.5468 and the mean MSE values were 0.0067, 0.0084, and 0.0114 for the 1 km, 250 m, and 8 km NDVI resolution predictions, respectively. The obtained results showed that the model's performance regarding the NDVI prediction was better at a spatial resolution of 1 km than at spatial resolutions of 250 m and 8 km. [ABSTRACT FROM AUTHOR]

This study explored the long-term trends and breakpoints of vegetation, rainfall, and temperature in Taiwan from overall and regional perspectives in terms of vertical differences from 1982 to 2012. With time-series Advanced Very-High-Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data and Taiwan Climate Change Estimate and Information Platform (TCCIP) gridded monthly climatic data, their vertical dynamics were investigated by employing the Breaks for Additive Seasonal and Trend (BFAST) algorithm, Pearson's correlation analysis, and the Durbin–Watson test. The vertical differences in NDVI values presented three breakpoints and a consistent trend from positive (1982 to 1989) to negative at varied rates, and then gradually increased after 2000. In addition, a positive rainfall trend was discovered. Average and maximum temperature had similar increasing trends, while minimum temperature showed variations, especially at higher altitudes. In terms of regional variations, the vegetation growth was stable in the north but worse in the central region. Higher elevations revealed larger variations in the NDVI and temperature datasets. NDVI, along with average and minimum temperature, showed their largest changes earlier in higher altitude areas. Specifically, the increasing minimum temperature direction was more prominent in the mid-to-high-altitude areas in the eastern and central regions. Seasonal variations were observed for each region. The difference between the dry and wet seasons is becoming larger, with the smallest difference in the northern region and the largest difference in the southern region. Taiwan's NDVI and climatic factors have a significant negative correlation (p < 0.05), but the maximum and minimum temperatures have significant positive effects at low altitudes below 500 m. The northern and central regions reveal similar responses, while the south and east display different feedbacks. The results illuminate climate change evidence from assessment of the long-term dynamics of vegetation and climatic factors, providing valuable references for establishing correspondent climate-adaptive strategies in Taiwan. [ABSTRACT FROM AUTHOR]

The normalized difference vegetation index (NDVI) is an important agricultural parameter that is closely correlated with crop growth. In this study, a novel method combining the dynamic time warping (DTW) model and the long short-term memory (LSTM) deep recurrent neural network model was developed to predict the short and medium-term winter wheat NDVI. LSTM is well-suited for modelling long-term dependencies, but this method may be susceptible to overfitting. In contrast, DTW possesses good predictive ability and is less susceptible to overfitting. Therefore, by utilizing the combination of these two models, the prediction error caused by overfitting is reduced, thus improving the final prediction accuracy. The combined method proposed here utilizes the historical MODIS time series data with an 8-day time resolution from 2015 to 2020. First, fast Fourier transform (FFT) is used to decompose the time series into two parts. The first part reflects the inter-annual and seasonal variation characteristics of winter wheat NDVI, and the DTW model is applied for prediction. The second part reflects the short-term change characteristics of winter wheat NDVI, and the LSTM model is applied for prediction. Next, the results from both models are combined to produce a final prediction. A case study in Hebei Province that predicts the NDVI of winter wheat at five prediction horizons in the future indicates that the DTW–LSTM model proposed here outperforms the LSTM model according to multiple evaluation indicators. The results of this study suggest that the DTW–LSTM model is highly promising for short and medium-term NDVI prediction. [ABSTRACT FROM AUTHOR]

Crop yield forecasting is performed monthly during the growing season by the United States Department of Agriculture's National Agricultural Statistics Service. The underpinnings are long-established probability surveys reliant on farmers' feedback in parallel with biophysical measurements. Over the last decade though, satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been used to corroborate the survey information. This is facilitated through the Global Inventory Modeling and Mapping Studies/Global Agricultural Monitoring system, which provides open access to pertinent real-time normalized difference vegetation index (NDVI) data. Hence, two relatively straightforward MODIS-based modeling methods are employed operationally. The first model constitutes mid-season timing based on the maximum peak NDVI value, while the second is reflective of late-season timing by integrating accumulated NDVI over a threshold value. Corn model results nationally show the peak NDVI method provides a R2 of 0.88 and a coefficient of variation (CV) of 3.5%. The accumulated method, using an optimally derived 0.58 NDVI threshold, improves the performance to 0.93 and 2.7%, respectively. Both these models outperform simple trend analysis, which is 0.48 and 7.4%, correspondingly. For soybeans the R2 results of the peak NDVI model are 0.62, and 0.73 for the accumulated using a 0.56 threshold. CVs are 6.8% and 5.7%, respectively. Spring wheat's R2 performance with the accumulated NDVI model is 0.60 but just 0.40 with peak NDVI. The soybean and spring wheat models perform similarly to trend analysis. Winter wheat and upland cotton show poor model performance, regardless of method. Ultimately, corn yield forecasting derived from MODIS imagery is robust, and there are circumstances when forecasts for soybeans and spring wheat have merit too. [ABSTRACT FROM AUTHOR]

This study presents a new method that mitigates biases between the normalized difference vegetation index (NDVI) from geostationary (GEO) and low Earth orbit (LEO) satellites for Earth observation. The method geometrically and spectrally transforms GEO NDVI into LEO-compatible GEO NDVI, in which GEO's off-nadir view is adjusted to a near-nadir view. First, a GEO-to-LEO NDVI transformation equation is derived using a linear mixture model of anisotropic vegetation and nonvegetation endmember spectra. The coefficients of the derived equation are a function of the endmember spectra of two sensors. The resultant equation is used to develop an NDVI transformation method in which endmember spectra are automatically computed from each sensor's data independently and are combined to compute the coefficients. Importantly, this method does not require regression analysis using two-sensor NDVI data. The method is demonstrated using Himawari 8 Advanced Himawari Imager (AHI) data at off-nadir view and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at near-nadir view in middle latitude. The results show that the magnitudes of the averaged NDVI biases between AHI and MODIS for five test sites (0.016–0.026) were reduced after the transformation (<0.01). These findings indicate that the proposed method facilitates the combination of GEO and LEO NDVIs to provide NDVIs with smaller differences, except for cases in which the fraction of vegetation cover (FVC) depends on the view angle. Further investigations should be conducted to reduce the remaining errors in the transformation and to explore the feasibility of using the proposed method to predict near-real-time and near-nadir LEO vegetation index time series using GEO data. [ABSTRACT FROM AUTHOR]

• The proposed linear mixed canopy model based on NDVI information extracted from sequential UAV imagery, provides an affordable alternative to fit the plant specific coverage in relation to time. Therefore, the proposed models are a powerful tool to perform non-destructive dynamic data estimations of coverage change in time. • All the statistical tests performed show that linear mixed models obtained are particularly suited to analyze plant-specific variance-covariance structures as well as repeated measures. • In the paper we also show that the collection of the measurements Unmanned aerial vehicles (UAV) combined with consumer-grade multi-spectral sensors are an affordable system to perform the necessary non-destructive observations, instead of the traditional methods which are in general, time demanding and expensive. This study presents the use of a linear mixed model to accurately estimate the dynamics of canopy cover (CC) during an irrigation experiment on a tomato crop, performed in the Peruvian coastal desert. Initially, the normalized difference vegetation index (NDVI)-based CC was computed using multispectral sensors over consecutive weekly UAV flights, during the growing season of the crop. The drone high-resolution sequential imagery, combined with image segmentation on plant level, results in repeated measures growth curves of the coverage per plant. To analyze these repeated measures data per plant, cubic polynomials without intercept showed the best goodness-of-fit of the CC dynamics per plant. As a consequence, this polynomial was incorporated in a linear mixed model (LMM) as a random coefficient model to fit the plant-specific time evolution of CC as deviations from the mean time effects per irrigation treatment. The mixed model approach is capable to estimate the coverage curves per plant with high accuracy and a very limited number of model parameters. The results and statistical analysis demonstrate the potential benefits of the linear mixed model for incorporating plant-specific random components in addition to classical fixed effects models. Thus, the proposed linear mixed canopy model is a very efficient way to model plant-specific growth curves together with the treatment and design structure of the experiment. [ABSTRACT FROM AUTHOR]

Dust events in Northeast Asia have several adverse effects on human health, agricultural land, infrastructure, and transport. Wind speed is the most important factor in determining the total dust emission at the land surface; however, various land-surface conditions must be considered as well. Recently, the Korea Meteorological Administration updated the dust emission reduction factor (RF) in the Asian Dust Aerosol Model 3 (ADAM3) using data from the normalized difference vegetation index (NDVI) of the Moderate Resolution Imaging Spectroradiometer (MODIS). We evaluated the improvements of ADAM3 according to soil types. We incorporated new RF formulations in the evaluation based on real-time MODIS NDVI data obtained over the Asian dust source regions in northern China during spring 2017. This incorporation improved the simulation performance of ADAM3 for the PM10 mass concentration in Inner Mongolia and Manchuria for all soil types, except Gobi. The ADAM3 skill scores for sand, loess, and mixed types in a 24 h forecast increased by 6.6%, 20.4%, and 13.3%, respectively, compared with those in forecasts employing the monthly RF based on the NDVI data. As surface conditions in the dust source regions continually change, incorporating real-time vegetation data is critical to improving performance of dust forecast models such as ADAM3. [ABSTRACT FROM AUTHOR]

Wide mode SAR images have an apparent incidence angle effect. The existing incident angle normalization methods assume that the relationship between the incident angle (θ) and the backscattering coefficient (σPQ) does not change with the growth stage of crops, which is in conflict with the real-life situation. Therefore, the normalization results of σPQ based on these existing methods will affect the accuracy of object classification, target recognition, and land surface parameter inversion. Here, the change in θ-σPQ relationship was investigated based on time-series (April to October) σPQ of maize canopies in northeast China, and a dynamic method based on normalized difference vegetation index (NDVI) was developed to normalize the effect of θ on σPQ. Through the accuracy evaluation, the following conclusions are obtained: (1) the dependence (referring to N) of Sentinel 1 C-band σPQ on θ varies with maize NDVI. In addition, the value of N changed from 9.35 to 0.66 at VV polarization from bare soil to biomass peak, and from 6.26 to 0.99 at VH polarization; (2) a dynamic method was proposed to quantify the change of N based on its strong correlation with NDVI, indicated by R2 of 0.82 and 0.80 for VV and VH polarization, respectively; and (3) the overall root mean square error of normalized σPQ based on the newly-developed dynamic method is 0.51 dB, and this accuracy outperforms the original first-order cosine method (1.37 dB) and cosine square law method (1.08 dB) by about 63% and 53% on the whole. This study provides a dynamic framework for normalizing radar backscatter coefficient, improving the retrieval accuracy of land surface parameters from radar remote sensing. [ABSTRACT FROM AUTHOR]

• Four critical degradation regions were found. • Effects of environment on NDVI were different under different land uses. • Lag effects between NDVI and climate were studied. The response between NDVI and climate has been a hot topic in recent research. Based on the data of GIMMS NDVI 3g and five climatic factors (precipitation, humidity, atmospheric pressure, air temperature, and sunshine hours), we evaluate spatial-temporal variation characteristics of the Normalized Difference Vegetation Index (NDVI) on the Loess Plateau and its response to climate by Sen+Mann-Kendall, redundancy analysis (RDA) variance decomposition and correlation analysis methods. The results showed that: (1) At the annual scale, the change of vegetation manifested an overall upward trend. However, there was severe degradation (2.49%) in the region, which was mainly distributed in Hohhot City, eastern Yan'an City, eastern Qinghai Province, and along Baoji-Xianyang and Xi'an-Weinan linear district. (2) The correlation between NDVI and atmosphere pressure was negative, while the correlation between NDVI and other four climatic factors was positive. (3) The independent impact and the combined impact of climatic elements, topographical elements and geographical elements were different under different land uses. The uneven spatial distribution of NDVI under different land uses was driven by climatic elements groups. (4) The bi-direction lag effects between NDVI and climatic factors showed the characteristics of short (1–3 months) or long (3–6 months) term under different land uses. Except for atmospheric pressure, the effects were positive in the short-term and negative in the long-term, while atmospheric pressure exhibited the opposite. This study can be conducive to forecast and evaluate the vegetation dynamics under the background of global climate change and provide a theoretical basis for the protection of the regional ecological environment. [ABSTRACT FROM AUTHOR]

Little is known about how much continuous landscape transformation might affect the most vulnerable elements of biodiversity. In this study, we quantified changes in the normalized difference vegetation index (NDVI) over the past 35 years across locations with threatened plants and in Natura 2000 (N2000) protected areas, in an environmentally heterogeneous region of Southern Europe. First, we estimated the intensity and duration of NDVI gains and losses based on Landsat time series using the LandTrendr algorithm in Google Earth Engine. Then, we tested: 1) whether populations of threatened plants were located in more stable sites than non-threatened plants (i.e. , lower NDVI changes); 2) whether NDVI changes around populations of threatened plants differed across habitats and inside/outside N2000 areas, and 3) whether lower NDVI changes occurred in N2000 areas than unprotected areas, thereby indicating their effectiveness at preserving biodiversity. Threatened plants tended to be concentrated in sites with less change irrespective of the habitat where they occurred and their location within protected areas. Occurrence in stable sites also reduced the risk associated with small-sized populations. N2000 areas were in line with the overall greening trend but they experienced less loss events than the unprotected areas, thereby supporting their role in slowing down human-induced land cover changes. Our approach demonstrates how long-term remote sensing monitoring can help to assess the effects of both slow processes and drastic landscape transformation events on priority plants in a comprehensive and rapid manner. This method can identify hidden patterns in extensive regions and guide effective conservation management. [ABSTRACT FROM AUTHOR]

The bidirectional reflectance distribution function (BRDF) is crucial in determining the quantity of reflected light on the earth's surface as a function of solar and view angles (i.e., azimuth and zenith angles). The Vegetation and ENvironment monitoring Micro-Satellite (VENµS) provides a unique opportunity to acquire data from the same site, with the same sensor, with almost constant solar and view zenith angles from two (or more) view azimuth angles. The present study was aimed at exploring the view angles' effect on the stability of the values of albedo and of two vegetation indices (VIs): the normalized difference vegetation index (NDVI) and the red-edge inflection point (REIP). These products were calculated over three polygons representing urban and cultivated areas in April, June, and September 2018, under a minimal time difference of less than two minutes. Arithmetic differences of VIs and a change vector analysis (CVA) were performed. The results show that in urban areas, there was no difference between the VIs, whereas in the well-developed field crop canopy, the REIP was less affected by the view azimuth angle than the NDVI. Results suggest that REIP is a more appropriate index than NDVI for field crop studies and monitoring. This conclusion can be applied in a constellation of satellites that monitor ground features simultaneously but from different view azimuth angles. [ABSTRACT FROM AUTHOR]

• The main factor that affects NDVI in most areas of China is surface soil moisture (SSM), followed by temperature, while in arid areas root zone soil moisture (RZSM) and profile soil moisture (PSM) are the main influencing factors. • The climate zones of China can be roughly divided into northern, southern and northwestern inland areas, and there are transitional zones between different zones. • Deep learning can simulate NDVI well, and the Nash efficiency coefficients during training and verification periods are 0.893 and 0.833, respectively. It shows that these selected influencing factors can simulate and predict NDVI changes well. • The environmental drift areas discovered by concept drift detection are mainly in the Heilongjiang River Basin in Northeastern China, Yunnan, Sichuan, and Southern Tibet in Southwestern China, and Gansu, Qinghai and Xinjiang in Northwestern China. Normalized Difference Vegetation Index (NDVI) is an important indicator reflecting the state of regional climate and environment, which is affected by precipitation, temperature, soil water content, and so on. This study analyzed the influence of different factors on NDVI through the spearman correlation coefficient (SCC) and standard regression coefficient (SRC), carried out climate zoning in China through principal component analysis (PCA) and cluster analysis, simulated the NDVI value through deep learning algorithm, and analyzed the spatial mutation positions of different factors through concept drift detection. The research results show that the main factor that affects NDVI in most areas of China is surface soil moisture (SSM), followed by temperature, while in arid areas root zone soil moisture (RZSM) and profile soil moisture (PSM) are the main influencing factors. The climate zones of China can be roughly divided into northern, southern and northwestern inland areas, while there are transitional zones between different main zones. The deep learning algorithm can simulate the NDVI value very well, and the Nash efficiency coefficients (NSE) during training and verification periods are 0.893 and 0.833, respectively. However, the details of some areas are rough. This shows that the selected impact factors can basically determine the status of NDVI, and these impact factors can be used to predict the changes in NDVI. The environmental drift areas discovered by concept drift detection are mainly in the Heilongjiang River Basin in Northeastern China, Yunnan, Sichuan, and Southern Tibet in Southwestern China, and Gansu, Qinghai and Xinjiang in Northwestern China. This study provides an important reference for the analysis of environmental regional zoning, climate change detection and simulation. [ABSTRACT FROM AUTHOR]

Timely and accurate extraction of urban built-up areas is crucial to addressing environmental problems related to fast changes in urban land cover, which is fundamental for optimizing land use patterns and supporting global sustainable development. Nighttime light (NTL) from the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) offer a new data source for extracting urban information. However, this kind of data suffer from drawbacks of blooming effects. To address this problem, in this study, the Enhanced Nighttime Light Urban Index (ENUI) approach, which involves the combination of NPP-VIIRS NTL with the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI), is proposed and tested. This approach was used to rapidly monitor the urban built-up areas in the Guangdong-Hong Kong-Macao Greater Bay Area in 2012, 2015, and 2018. The average overall accuracy and Map-level Image Classification Efficacy (MICE) for the extraction results are 93.56% and 0.77, respectively, while those of the Local-Optimized Thresholding (LOT) are 86.48% and 0.54, respectively; meanwhile, the average F-score values, user's accuracy and producer's accuracy for urban areas using the proposed approach increased by 9.98%, 10.90% and 8.67%, respectively, compared with the LOT. These findings suggest that this approach has a higher extraction accuracy than the LOT; this is primarily ascribed to the integration of NTL data with the NDVI, NDWI, and NDBI, which increases the variability of nighttime light in the urban core area and adequately alleviates the blooming effects of nighttime light brightness in water bodies and vegetated areas. The proposed approach shows great potentials to accurately and effectively monitor multi-temporal urban information and address environmental issues using NPP-VIIRS NTL data in global urban agglomerations. [Display omitted] • NPP-VIIRS NTL, NDVI, NDWI and NDBI are utilized to improve built-up-areas monitor. • Our approach reduces blooming effects of NTL brightness in water and vegetated areas. • The resulting average overall accuracy and MICE are 93.56% and 0.77, respectively. • The resulting average urban F-score value increased by 9.98% compared to LOT. [ABSTRACT FROM AUTHOR]

Spectral unmixing remains the most popular method for estimating the composition of mixed pixels. However, the spectral-based unmixing method cannot easily distinguish vegetation with similar spectral characteristics (e.g., different forest tree species). Furthermore, in large areas with significant heterogeneity, extracting a large number of pure endmember samples is challenging. Here, we implement a fractional evergreen forest cover-self-adaptive parameter (FEVC-SAP) approach to measure FEVC at the regional scale from continuous intra-year time-series normalized difference vegetation index (NDVI) values derived from moderate resolution imaging spectroradiometer (MODIS) imagery acquired over southern China, an area with a complex mixture of temperate, subtropical, and tropical climates containing evergreen and deciduous forests. Considering the cover of evergreen forest as a fraction of total forest (evergreen forest plus non-evergreen forest), the dimidiate pixel model combined with an index of evergreen forest phenological characteristics (NDVIann-min: intra-annual minimum NDVI value) was used to distinguish between evergreen and non-evergreen forests within a pixel. Due to spatial heterogeneity, the optimal model parameters differ among regions. By dividing the study area into grids, our method converts image spectral information into gray level information and uses the Otsu threshold segmentation method to simulate the appropriate parameters for each grid for adaptive acquisition of FEVC parameters. Mapping accuracy was assessed at the pixel and sub-pixel scales. At the pixel scale, a confusion matrix was constructed with higher overall accuracy (87.5%) of evergreen forest classification than existing land cover products, including GLC 30 and MOD12. At the sub-pixel scale, a strong linear correlation was found between the cover fraction predicted by our method and the reference cover fraction obtained from GF-1 images (R2 = 0.86). Compared to other methods, the FEVC-SAP had a lower estimation deviation (root mean square error = 8.6%). Moreover, the proposed method had greater estimation accuracy in densely than sparsely forested areas. Our results highlight the utility of the adaptive-parameter linear unmixing model for quantitative evaluation of the coverage of evergreen forest and other vegetation types at large scales. [ABSTRACT FROM AUTHOR]

The spatiotemporal evolution of vegetation and its influencing factors can be used to explore the relationships among vegetation, climate change, and human activities, which are of great importance for guiding scientific management of regional ecological environments. In recent years, remote sensing technology has been widely used in dynamic monitoring of vegetation. In this study, the normalized difference vegetation index (NDVI) and standardized precipitation–evapotranspiration index (SPEI) from 1998 to 2017 were used to study the spatiotemporal variation of NDVI in China. The influences of climate change and human activities on NDVI variation were investigated based on the Mann–Kendall test, correlation analysis, and other methods. The results show that the growth rate of NDVI in China was 0.003 year−1. Regions with improved and degraded vegetation accounted for 71.02% and 22.97% of the national territorial area, respectively. The SPEI decreased in 60.08% of the area and exhibited an insignificant drought trend overall. Human activities affected the vegetation cover in the directions of both destruction and restoration. As the elevation and slope increased, the correlation between NDVI and SPEI gradually increased, whereas the impact of human activities on vegetation decreased. Further studies should focus on vegetation changes in the Continental Basin, Southwest Rivers, and Liaohe River Basin. [ABSTRACT FROM AUTHOR]

High-throughput phenotyping (HTP) enables breeders and researchers to have massive data sets accurately and objectively. It could be applied to plant breeding for screening stress tolerance and biodiversity among wild species in the gene bank, which can be a breakthrough in the phenotyping bottleneck. However, there are many factors to be considered. Thus, this study is designed to show an example of phenotyping traits using yield and image data in citrus using the Normalized Difference Vegetation Index (NDVI) and Red, Green, and Blue (RGB) images. The results using image analysis showed that R2 in linear regression ranged from 0.79 to 0.91, depending on the methods which were used in the current study. However, the results from NDVI were proven to be false, unlike those of RGB images. This means that researchers and breeders must be very cautious when dealing with new technologies to avoid being misled to the wrong conclusion when they try to associate this data with genomic data. [ABSTRACT FROM AUTHOR]

This study determined whether Canopeo and GreenSeeker measurements in Megathyrsus maximus can estimate plant height, dry matter mass, morphological components, and content of crude protein and neutral detergent fiber at different days of growth. Five plots of 5 × 25m M. maximus grass were defined: subplots of 1×1m were evaluated every three days, in which the pasture shows 3, 6, 9, 12, 15, 18, 21, 24, 27, and 30 days of regrowth. The subplot was evaluated for canopy height and mass accumulation. The canopeo index (CI) obtained at a higher height was lower than those obtained at a smaller height. Higher measurement height increased the normalized difference vegetation index (NDVI) relative to 0.10 m. The highest indexes were observed since 18 d of regrowth. Except for the CI evaluated at 0.10 m of height, the indexes were not correlated to the chemical composition of the forage. The CI and NDVI were positively correlated to plant height, dry matter mass, and leaf index, whereas both were negatively correlated with stalk index. Thus, lower evaluation heights for CI and NDVI can be a good predictor of forage height. Values of 0.83 and 85.8 for NDVI and CI, respectively, indicated an appropriate time to start the grazing of M. maximus. [ABSTRACT FROM AUTHOR]

Frequent droughts may have negative influences on the ecosystem (i.e., terrestrial vegetation) under a warming climate condition. In this study, the linear regression method was first used to analyze trends in vegetation change (normalized difference vegetation index (NDVI)) and drought indices (Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI)). The Pearson Correlation analysis was then used to quantify drought impacts on terrestrial vegetation in the Weihe River Basin (WRB); in particular, the response time of vegetation to multiple time scales of drought (RTVD) in the WRB was also investigated. The trend analysis results indicated that 89.77% of the area of the basin showed a significant increasing trend in NDVI from 2000 to 2019. There were also significant variations in NDVI during the year, with the highest rate in June (0.01) and the lowest rate in January (0.002). From 2000 to 2019, SPI and SPEI at different time scales in the WRB showed an overall increasing trend, which indicated that the drought was alleviated. The results of correlation analysis showed that the response time of vegetation to drought in the WRB from 2000 to 2019 was significantly spatially heterogeneous. For NDVI to SPEI, the response time of 12 months was widely distributed in the north; however, the response time of 24 months was mainly distributed in the middle basin. The response time of NDVI to SPI was short and was mainly concentrated at 3 and 6 months; in detail, the response time of 3 months was mainly distributed in the east, while a response time of 6 months was widely distributed in the west. In autumn and winter, the response time of NDVI to SPEI was longer (12 and 24 months), while the response time of NDVI to SPI was shorter (3 months). From the maximum correlation coefficient, the response of grassland to drought (SPEI and SPI) at different time scales (i.e., 6, 12, and 24 months) was higher than that of cultivated land, forestland, and artificial surface. The results may help improve our understanding of the impacts of climatic changes on vegetation cover. [ABSTRACT FROM AUTHOR]

Purpose: In the past three decades, remote sensing-based models for estimating crop yield have addressed critical problems of general food security, as the unavailability of grains such as rice creates serious worldwide food insecurity problems. The main purpose of this study was to compare the potential of time-series Landsat-8 and Sentinel-2 data to predict rice yield several weeks before harvest on a regional scale. Design/methodology/approach: To this end, the sum of normalized difference vegetation index (NDVI)-based models created the best agreement with actual yield data at the golden time window of six weeks before harvest when rice grains were in milky and mature growth stages. The application of nine other vegetation indicators was also investigated in the golden time window in comparison to NDVI. Findings: The findings of this study demonstrate the viability of identifying locations with poor and superior performance in terms of production management approaches through a rapid and economical solution for early rice grain yield assessment. Results indicated that while some of those, such as enhanced vegetation index (EVI) and optimized soil adjusted vegetation index, were able to estimate rice yield with high accuracy, NDVI is still the best indicator to predict rice yield before harvest. However, experiments can be conducted in different regions in future studies to evaluate the generalizability of the approach. Originality/value: To achieve this objective, the authors considered the following purposes: using Sentinel-2 time-series data, determining the appropriate growth stage for estimating rice yield and evaluating different vegetation indices for estimating rice yield. [ABSTRACT FROM AUTHOR]

We developed a new algorithm for computing radiometrically and spectrally consistent surface reflectances from multiple sensors. The algorithm approximates surface reflectances of reference sensors directly from top-of-atmosphere (TOA) reflectances of sensors-to-be-transformed. A unique characteristic of the algorithm is that coefficients in the algorithm are computed independently using statistics of time-series reflectance data for each sensor; thus, no regressions or optimizations using pairs of data from different sensors are required. This characteristic can lead to a substantial reduction in the number of computational tasks required for calibrating models when numerous satellite sensors or datasets are used. First, a system of equations relating TOA reflectances of one sensor and surface reflectances of another sensor in the red and near-infrared bands was analytically approximated using a linear mixture model of three land-cover types and radiative transfer in the atmosphere. The equations were subsequently used to develop an unmixing-based algorithm for radiometric corrections and spectral transformations. The algorithm was evaluated using synchronous observation data and long-term time-series data with middle spatial resolution, which were obtained from the Landsat 4–5 Multispectral Scanner (MSS) and Thematic Mapper (TM) sensors. Results obtained using contemporaneous data from the two sensors indicated that cross-sensor differences in reflectances and in a spectral index, the normalized difference vegetation index (NDVI), between the MSS and TM sensors were reduced to reasonable levels after the algorithm was applied; the magnitudes of remaining biases were less than 0.005 in reflectance units and less than 0.03 in NDVI units. Results obtained using time-series data for four regions of interest with different land-cover types indicated that the transformed MSS time-series data well synchronized with the TM data used as a reference. Reflectance differences remaining after implementation of the algorithm were possibly due to instability of the algorithm for computing parameters, sensor-dependent quality assurance (QA) data and QA accuracy, and geolocation errors, among others. The concept of the developed algorithm might be applicable universally to various combinations of spectral bands and sensors/missions, which should be further evaluated for cross-sensor radiometric and spectral harmonization with the aim of multi-sensor analysis. [ABSTRACT FROM AUTHOR]

Climate warming hampers grassland growth, particularly in dryland regions. To preserve robust grassland growth and ensure the resilience of grassland in these arid areas, a comprehensive understanding of the interactions between vegetation and climate is imperative. However, existing studies often analyze climate–vegetation interactions using concurrent vegetation indices and meteorological data, neglecting time-lagged influences from various determinants. To address this void, we employed the random forest machine learning method to predict the grassland NDVI (Normalized Difference Vegetation Index) in Asian drylands (including five central Asia countries, the Republic of Mongolia, and Parts of China) from 2001 to 2020, incorporating time-lag influences. We evaluated the prediction model's performance using three indexes, namely the coefficient of determination (R2), root-mean-square error (RMSE), and Mean Absolute Error (MAE). The results underscore the superiority of the model incorporating time-lag influences, demonstrating its enhanced capability to capture the grassland NDVI in Asian drylands (R2 ≥ 0.915, RMSE ≤ 0.033, MAE ≤ 0.019). Conversely, the model without time-lag influences exhibited relatively poor performance, notably inferior to the time-lag-inclusive model. The latter result aligns closely with remote sensing observations and more accurately reproduces the spatial distributions of the grassland NDVI in Asian drylands. Over the study period, the grassland NDVI in Asian drylands exhibited a weak decreasing trend, primarily concentrated in the western region. Notably, key factors influencing the grassland NDVI included the average grassland NDVI in the previous month, total precipitation in the current month, and average soil moisture in the previous month. This study not only pioneers a novel approach to predicting grassland growth but also contributes valuable insights for formulating sustainable strategies to preserve the integrity of grassland ecosystems. [ABSTRACT FROM AUTHOR]

As a by-product of Interferometric Synthetic Aperture Radar (SAR, InSAR) technique, interferometric coherence is a measure of the decorrelation noise for InSAR observation, where the lower the coherence value, the more serious the decorrelation noise. In the densely vegetated area, the coherence value could be too low to obtain any valuable signals, leading to the degradation of InSAR performance and the possible waste of expensive SAR data. Normalized Difference Vegetation Index (NDVI) value is a measure of the vegetation coverage and can be estimated from the freely available optical satellite images. In this paper, a multi-stage model is established to quantitatively estimate the decorrelation noise for vegetable areas based on Landsat-derived NDVI prior to the acquisition of SAR data. The modeling process is being investigated with the L-band ALOS-1/PALSAR-1 data and the Landsat-5 optical data acquired in the Meitanba area of Hunan Province, China. Furthermore, the reliability of the established model is verified in the Longhui area, which is situated near the Meitanba area. The results demonstrate that the established model can quantitatively estimate InSAR decorrelation associated with the vegetation coverage. [ABSTRACT FROM AUTHOR]

The evolution of dryland pasture quality is closely related to the seasonal and inter-annual variability characteristic of the Mediterranean climate. This variability introduces great unpredictability in the dynamic management of animal grazing. The aim of this study is to evaluate the potential of two complementary tools (satellite images, Sentinel-2 and proximal optical sensor, OptRx) for the calculation of the normalized difference vegetation index (NDVI), to monitor in a timely manner indicators of pasture quality (moisture content, crude protein, and neutral detergent fiber). In two consecutive years (2018/2019 and 2019/2020) these tools were evaluated in six fields representative of dryland pastures in the Alentejo region, in Portugal. The results show a significant correlation between pasture quality degradation index (PQDI) and NDVI measured by remote sensing (R2 = 0.82) and measured by proximal optical sensor (R2 = 0.83). These technological tools can potentially make an important contribution to decision making and to the management of livestock production. The complementarity of these two approaches makes it possible to overcome the limitations of satellite images that result (i) from the interference of clouds (which occurs frequently throughout the pasture vegetative cycle) and (ii) from the interference of tree canopy, an important layer of the Montado ecosystem. This work opens perspectives to explore new solutions in the field of Precision Agriculture technologies based on spectral reflectance to respond to the challenges of economic and environmental sustainability of extensive livestock production systems. [ABSTRACT FROM AUTHOR]

Canopy cover (CC) is a key parameter in calibration and validation of crop growth models, especially those used in operational field monitoring. However, CC direct measurements require intense field campaigns, increasing the cost in time-series data acquisition for large agricultural areas. Normalized Difference Vegetation Index (NDVI) is a commonly used remote-sensing vegetation index, expressing crop water-status, being indirectly related to CC. In this paper, we explore the relationship between on-site CC and the high-resolution NDVI data acquired via Sentinel 2 products. This relationship was utilized to produce CC time series over the cultivation period in four maize fields in northern and central Greece. Subsequently, the expression linking CC and NDVI was used to operationally validate CC change in a crop model capable to simulate the maize growth cycle (AquaCrop). The proposed method involves the dynamic in-season re-adjustment to a number of key model input parameters, based on the remotely acquired CC time series, namely maximum CC, canopy growth and decline coefficient, growing degree days needed to the beginning of senescence stage. These re-adjusted parameters were imported to model's crop file to improve simulations in CC, soil water content, final biomass and yield. Results showed that the remotely acquired CC time series could be successfully used as an alternative mean to validate CC simulations. Moreover, the ingestion of re-estimated parameters to crop file, improved model's capability to simulate CC (R2 > 0.98; RMSE < 5.12%), biomass (Pe < 12%) and yield (Pe < 12%). No significant differences were observed in model's performance regarding soil water content simulation. • A strong correlation was found between field measured canopy cover and Sentinel 2 derived NDVI. • Canopy cover time series derived from Sentinel 2 NDVI imagery proved to be a useful tool for field operational monitoring. • Local calibrated AquaCrop showed poor results regarding biomass and yield production when validation data sets were used. • In season modification of crop file improved model's predictions in terms of canopy cover, biomass and yield production. [ABSTRACT FROM AUTHOR]

• The rainfall variation in spring drives NDVI changes in sub-Mediterranean grasslands. • Drought and cold stress intensities before the growing season drive NDVI change. • The amplitude of NDVI response to climate variability is influenced by landform. • The effect of climate on NDVI is influenced by the management condition. • Climate change will impact mostly on productive Mediterranean grasslands. The modification of precipitation regimes and the increase in summer aridity under the predicted climate change scenarios are hypothesized to have repercussions on the semi-extensive pastoral systems of Mediterranean grasslands, leading to change in forage quantity/quality, increased interannual variability in phytomass production, reduction of the carrying capacity of rangelands, and worsening of animal welfare. The research aims were to find what climatic features affect changes in the annual peak of Aboveground Net Primary Production (ANPP), and how topographic variables and management conditions influence these variations in a pastoral system of central Apennines (Italy). We monitored Normalized Difference Vegetation Index (NDVI) variations at the peak of phytomass production using Landsat satellite images, used as a proxy for ANPP changes in the most productive phase of the growing season, when herds are carried to pastures and hay-meadows are mown, on a dataset of 10,000 randomly sampled pixels (30 × 30 m), in the years 2003, 2007, 2009, 2010, and 2013−2015. We investigated the relation between NDVI and climatic variables, topographic parameters, vegetation physiognomy, and management type, using generalized linear mixed-effects modeling, variation partitioning, and correlation analysis. We observed an increase in average yearly temperatures and high variability in the rainfall seasonal distribution pattern, particularly of spring precipitation. Climatic fluctuations influenced the overall amount of forage production more than management and topographic conditions. The spring variation of climatic variables (precipitation in March, drought stress in April and May), the drought and cold stress intensities in the previous year and the cold stress in the preceding winter were the main drivers of NDVI change. The landform factors interacted with climate variability in determining the amplitude of NDVI changes, which were the widest in semi-flat mountain tops and flat valley bottoms and the smallest in south-facing slopes. The effect of the climatic variables on NDVI was different depending on the management condition, suggesting that grassland management partially filters the climatic drivers of changes in forage production. Based on our results, the most productive grassland communities and the areas used for grazing by domestic herbivores could be the most impacted by the predicted climate changes for the Mediterranean basin, potentially reducing the economic sustainability of semi-extensive farming in the Apennine mountains. [ABSTRACT FROM AUTHOR]

Biologically active areas play an extremely important role in the structure of a city and increasing their coverage, especially in large urban centres, is an activity with a number of advantages. This article compares, in terms of green spaces, two European cities of similar size - Warsaw (517.2 km²) and Oslo (454 km²). Both cities are capitals of their respective countries but implement different spatial policies in the scope of the Green Deal. In Warsaw, many industrial and post-industrial areas still exist and simultaneously urban green areas are decreasing year by year. In Oslo, a strategy based on deindustrialisation of the city and possible maximum use of urban greenery and public spaces is implemented. The research described in this article involved analysing the coverage of the analysed cities and their districts with biologically active area and then checking the correlation with other indicators that can be affected by this coverage. These included data on the incidence of the most common diseases among residents, the attractiveness of living for the elderly and families with children, as well as air and soil pollution and the occurrence of negative effects of climate change. The correlation of urban space use in terms of the presence of industrial land in relation to currently existing green spaces in the districts concerned was subsequently determined. [ABSTRACT FROM AUTHOR]

Vegetation monitoring is considered an important application in remote sensing task due to variation of vegetation types and their distribution. The vegetation concentration around the Earth is increase in 5% in 2000 according to NASA monitoring. This increase is due to the Indian vegetable programs. In this research, the vegetation monitoring in Baghdad city was done using Normalized Difference Vegetation Index (NDVI) for temporal Landsat satellite images (Landsat 5 TM& Landsat 8 OIL). These images had been used and utilize in different times during the period from 2000, 2010, 2015 & 2017. The outcomes of the study demonstrate that a change in the vegetation Cover (VC) in Baghdad city. (NDVI) generally shows a low value of plant cover. The highest NDVI values were occur in 2000 and the lowest values for both years 2015-2017. This change is due to a correlation of climate indices such as precipitation, temperature, and dust storms. This study present that (NDVI) method is a powerful and useful way of monitoring vegetation. The calculation of vegetable areas show (43.3, 37.4, 9.1, and 22.7 Km2). The result were evaluated using (Environment for Visualizing Images ENVI) Ver. 4.8 package. [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]

When combined with conducive atmospheric conditions, air pollution caused by fossil fuel consumption associated with transportation, industry and electricity production for households, create and sustain continuous pollution over the megalopolis of Tehran, Iran. In conjunction with daily meteorological forecasts, remote sensing can be used to identify and predict days with hazardous levels of air pollution, providing an opportunity for air quality alert systems to be triggered and warnings circulated to reduce health risks to citizens of Tehran province. Combining remotely sensed ozone column density (OCD) data from the Sentinel-5 TROPOMI sensor with NASA Giovanni data concerning meteorological parameters (temperature (T), wind speed (WS) and specific humidity (SH)), geographical parameters and population data, this study considers the drivers and effects of ozone pollution on the urban climate and vegetation condition (normalized difference vegetation index (NDVI)) of 16 counties in Tehran province, Iran during 12 months (i.e., January 2021 to December 2021). Future monthly forecasts of the OCD, climatic and terrestrial factors in 2022 are also presented. Google Earth Engine and the NASA Giovanni platforms were employed for the processing and analysis of data using an interpolation technique. Additionally, a Box–Jenkins ARIMA and Exponential Smoothing (ETS) models were compared and tailored to generate monthly forecasts of OCD, T, WS, SH and NDVI. The highest and lowest OCD was obtained in June and December 2021, with a concentration of 0.14277 mol/m2 and 0.12383 mol/m2, respectively. However, the annual average OCD was higher in the cities of Shahriar and Pakdasht in March, with values of 0.13237 mol/m2 and 0.13244 mol/m2, respectively. The lowest OCD recorded was 0.13105 mol/m2, in Shemiranat city, in the north of Tehran. The results indicate a positive correlation between OCD and NDVI, and a negative correlation between OCD, SH, WS and T. A strong seasonal trend in OCD was identified for all cities, but across the entire province, altitude and population size were the most significant explanatory variables for spatial variations in OCD. This research demonstrates that an effective OCD monitoring and forecasting model may be generated from remote sensing and meteorological variables. The implementation and utilization of these models are of paramount importance as they offer vital information to authorities for continuous air quality monitoring and strategic planning, particularly for days with hazardous air pollution. By effectively implementing the OCD model, it has the potential to directly contribute to improved health outcomes in major cities across Iran. [ABSTRACT FROM AUTHOR]

Previous studies have shown that climate change has significant cumulative effects on vegetation growth. However, there remains a gap in understanding the characteristics of cumulative climatic effects on different vegetation types and the underlying driving mechanisms. In this study, using the normalized difference vegetation index data from 1982 to 2015, along with accumulated temperature, precipitation, and solar radiation data, we quantitatively investigated the intra-annual cumulative effects of climatic factors on global vegetation biomes across climatic zones. We also explored the underlying driving mechanisms. The results indicate that precipitation has a longer intra-annual cumulative effect on vegetation, with effects lasting up to 12 months for large percentages of most vegetation biomes. The cumulative effect of solar radiation is mostly concentrated within 0–6 months. Temperature has a shorter cumulative effect, with no significant cumulative effect of temperature on large percentages of tree-type vegetation. Compared to other vegetation types, evergreen broadleaf forests, close shrublands, open shrublands, savannas, and woody savannas exhibit more complex cumulative climatic effects. Each vegetation type shows a weak-to-moderate correlation with accumulated precipitation while exhibiting strong-to-extremely-strong positive correlations with accumulated temperature and accumulated solar radiation. The climate-induced regulations of water, heat, and nutrient, as well as the intrinsic mechanisms of vegetation's tolerance, resistance, and adaptation to climate change, account for the significant heterogeneity of cumulative climatic effects across vegetation biomes in different climatic zones. This study contributes to enriching the theoretical understanding of the relationship between vegetation growth and climate change. It also offers crucial theoretical support for developing climate change adaptation strategies and improving future "vegetation-climate" models. [ABSTRACT FROM AUTHOR]

The earth's surface comprises different kinds of land cover, water resources, and soil, which create environmental factors for varied animals, plants, and humans. Knowing the significant effects of land cover is crucial for long-term development, climate change modeling, and preserving ecosystems. In this research, the Google Earth Engine platform and freely available Landsat imagery were used to investigate the impact of the expansion and degradation in urbanized areas, watersheds, and vegetative cover on the land surface temperature in Baghdad from 2004 to 2021. Land cover indices such as the Normalized Difference Vegetation Index, Normalized Difference Water Index, and Normalized Difference Built-up Index (NDVI, NDWI, and NDBI) were determined to examine the effects of land cover changes. In addition, the land surface temperature was calculated to assess urbanization expansion's impact on Baghdad's climate warming. The results showed a drastic decrease in vegetative cover and green land, on the other hand, a significant expansion in urbanized areas. Hence, from 2004 to 2021, the urbanized areas and open land rose by 37% and 3%, respectively, while the vegetative cover decreased by 41%. The maximum land surface temperature has risen 4° C, and the minimum land surface temperature has risen 2.5°C. [ABSTRACT FROM AUTHOR]

NDVI data have been widely used to detect and monitor vegetation status at regional, continental, and global scales. FY-3D MERSI-II NDVI (FNDVI) is a critical operational product used in many studies monitoring ecosystems and agriculture and assessing climate change and its risks, including drought and fire. MERSI-II and MODIS have very similar spectral response functions in the red and near-infrared channels, making MERSI/NDVI an effective replacement for MODIS/NDVI (MNDVI). Therefore, it is critical to conduct a thorough evaluation of the product's quality. In this study, the consistency characteristics of two normalized difference vegetation index (NDVI) products, FY-3D MERSI-II NDVI and MODIS NDVI, were compared and validated at national and regional scales in China from 2020 to 2021. To assess the consistency of these two NDVI datasets, the correlation coefficient, root-mean-square error, and mean bias error were used. The findings revealed that the spatial distribution patterns of FNDVI and MNDVI were highly consistent across the country at the monthly time scale. The correlation coefficients were greater than 0.9475 for the two years 2020–2021, while the average deviation was between 0.02 and 0.05, and the root-mean-square error was 0.11. Based on the difference in the time consistency between FNDVI and MNDVI, the changes in the monthly NDVI values of the two types of satellites are generally consistent across the country. Among the three typical experimental areas, the relative deviation of the regional time series for products was the highest in Xinjiang. The relative average deviation of FNDVI in other regions was low, and its change trend was consistent with that of MODIS. [ABSTRACT FROM AUTHOR]

Globalization, population change, and rural-to-urban movement are the main causes of the enormous issues faced by rural communities. Every growing nation, including the Kurdistan Region of Iraq (KRI), has a sizable proportion of rural farmers among its populace. This study was done in the Sulaimani Governorate, which was divided into six main districts namely; Garmian, Penjwin, Halabja, Chamchamal, Sharazur, and Raniya, and aimed to compare the groundwater level with summer crop water demand. The weighted Sum Method (WSM) and a Normalized Difference Vegetation Index map (NDVI) were used to analyze and illustrate the current water demand status for summer crops. The study employed a participatory research design, utilizing a purposive sampling method to select 60 representative farmers, 25 key decision-makers, representatives from Non-Governmental Organizations (NGOs), and experts in the field of water management. Data was collected through the use of focus group discussions (FGDs) and Key Informant Interviews (KIIs) to gather information from the selected participants. This study is innovative in its attempt to establish a correlation between the yield of summer crops and the availability of groundwater. As a conclusion for this study, the results suggest that the level of groundwater plays a significant role in determining the production of summer crops. Additionally, the sustainable development of various regions within the Sulaimani Governorate is influenced by a complex interplay of environmental, economic, and social factors. Future work will be focus on doing a detail research regarding time series for ground water level (GWL) vs. crop production vs. NDVI. [ABSTRACT FROM AUTHOR]