Search Results (875)

This study analyzed the spatiotemporal changes in grassland NDVI from 2000 to 2020 in the eastern route of the China–Mongolia–Russia Economic Corridor, a region with frequent ecological–economic interactions, and explained the main driving factors, influencing patterns, and degrees of grassland NDVI changes in different regions. Based on MODIS NDVI data, the study employs emerging spatiotemporal hotspot analysis, Maximum Relevance Minimum Redundancy (mRMR) feature selection, and Gaussian Process Regression (GPR) to reveal the spatiotemporal variation characteristics of grassland NDVI, while identifying long-term stable trends, and to select the most relevant and non-redundant factors to analyze the main driving factors of grassland NDVI change. Partial dependence plots were used to visualize the response and sensitivity of grassland NDVI to various factors. The results show the following: (1) From 2000 to 2020, the NDVI of grassland in the study area showed an overall upward trend, from 0.61 to 0.65, with significant improvement observed in northeastern China and northeastern Russia. (2) Spatiotemporal hotspot analysis indicates that 51% of the area is classified as persistent hotspots for grassland NDVI, mainly distributed in Russia, whereas 12% of the area is identified as persistent cold spots, predominantly located in Mongolia. (3) The analysis of key drivers reveals that precipitation and land surface temperature are the dominant climatic factors shaping grassland NDVI trends, while the effects of soil conditions and human activity vary regionally. In China, NDVI is primarily driven by land surface temperature (LST), GDP, and population density; in Mongolia, precipitation, LST, and GDP exert the strongest influence; whereas in Russia, livestock density and soil organic carbon play the most significant roles. (4) For the whole study area, in persistent cold spot areas of grassland NDVI, the negative effects of rising land surface temperature were most pronounced, reducing NDVI by 36% in the 25–40 °C range. The positive effects of precipitation on NDVI were most evident under low to moderate precipitation conditions, with the effects diminishing as precipitation increased. Soil moisture and soil pH have stronger effects in persistent hotspot areas. Regarding human activity factors, the livestock factor in Mongolia shows an inverted U-shaped relationship with NDVI, and increasing population density contributed to grassland degradation in persistent cold spots. Proper grazing intensity regulation strategy is crucial in these areas with inappropriate grazing intensity, while social and economic activities promoted vegetation cover improvement in persistent hotspots in China and Russia. These findings provide practical insights to guide grassland ecosystem restoration and ensure sustainable development along the eastern route of the China–Mongolia–Russia Economic Corridor. China should prioritize ecological compensation policies. Mongolia needs to integrate traditional nomadic grazing with modern practices. Russia should focus on strengthening regulatory frameworks to prevent the over-exploitation of grasslands. Especially for persistent cold spot areas of grassland NDVI in Mongolia and Russia that are prone to grassland degradation, attention should be paid to the significant negative impact of livestock on grassland. Full article

While numerous studies have investigated the NDVI-LST relationship at local or regional scales, existing global analyses are outdated and fail to incorporate recent environmental changes driven by climate change and human activity. This study aims to address this gap by conducting an extensive global analysis of NDVI-LST correlations from 2000 to 2024, utilizing multi-source satellite data to assess latitudinal and ecosystem-specific variability. The MODIS dataset, which provides global daily LST data at a 1 km resolution from 2000 to 2024, was used alongside MODIS-derived NDVI data, which offers global vegetation indices at a 1 km resolution and 16-day temporal intervals. A correlation analysis was performed by extracting NDVI and LST values for each raster cell. The analysis revealed significant negative correlations in regions such as the western United States, Brazil, southern Africa, and northern Australia, where increased temperatures suppress vegetation activity. A total of 38,281,647 pixels, or 20% of the global map, exhibited statistically significant correlations, with 80.4% showing negative correlations, indicating a reduction in vegetation activity as temperatures rise. The latitudinal distribution of significant correlations revealed two prominent peaks: one in the tropical and subtropical regions of the Southern Hemisphere and another in the temperate zones of the Northern Hemisphere. This study uncovers notable spatial and latitudinal patterns in the LST-NDVI relationship, with most regions exhibiting negative correlations, underscoring the cooling effects of vegetation. These findings emphasize the crucial role of vegetation in regulating surface temperatures, providing valuable insights into ecosystem health, and informing conservation strategies in response to climate change. Full article

The Normalized Difference Vegetation Index (NDVI) is a measurement of landscape “greenness” and is used as a proxy for productivity to assess species distributions and habitats. Seasonal levels of productivity have been strongly related to avian population dynamics, suggesting dependence upon biomass production for completing annual life cycle events. The breeding season is a critical component of the avian life cycle that involves higher nutritional requirements to feed young, avoiding predators, and attracting mates. Our objective was to determine how the NDVI affects avian abundance and richness across breeding seasons with varied rainfall in South Texas, USA. Breeding bird point-count surveys were conducted, and MODIS Terra NDVI data were collected. We observed both positive and negative effects between May and June avian abundance, richness, and the NDVI depending upon the year (i.e., wet or average rainfall) and NDVI values in the months prior to (i.e., April) and during the peak of breeding season (May), with no significant effect of the NDVI in June, suggesting the months prior to peak breeding season may be most influential. This information can aid land management recommendations and better predict how environmental changes like rainfall may affect avian dynamics on a landscape for both wildlife and domestic animals. Full article

Accurate crop yield prediction and modeling are essential for ensuring food security, optimizing resource allocation, and guiding policy decisions in agriculture, ultimately benefiting society at large. With the increasing threat of weather change, it is important to understand the impacts of weather dynamics on agricultural productivity, particularly for crucial crops like soybeans. This study considers the study area of the Greater Mississippi River Basin, where most soybeans are typically planted, with a large variety of weather across from the North to the South in the US. Leveraging the greenness and density measured by the normalized difference vegetation index (NDVI) from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images, along with weather variables including mean precipitation, minimum temperature, and maximum temperature, we aim to uncover the relationships between these variables and soybean yield for different geographical and weather regions. Our analysis focuses on the four weather regions within the US: Very Cold, Cold, Mixed Humid, and Hot Humid, where most soybeans are planted in the Mississippi River Basin. The findings reveal that soybean yield in the Cold and Very Cold regions is positively correlated with minimum temperatures, whereas in the Mixed Humid and Hot Humid regions, negative correlations between maximum temperatures and yields are found. We identify a significant positive correlation between precipitation and soybean yield across all regions. In addition, the NDVI shows significant positive correlations with the soybean yield. Both linear and nonlinear regression models, including support vector machine and random forest models, are trained with remotely sensed data and weather data, showing a reliable and improved crop yield prediction. The findings of this study contribute to a better understanding of how soybean yield responds to climatic variations and will help the national agricultural management system in better monitoring and predicting crop yield when facing the increasing challenge of weather dynamics. Full article

Monitoring mangrove phenology requires frequent, high-resolution remote sensing data, yet satellite imagery often suffers from coarse resolution and cloud interference. Traditional methods, such as denoising and spatiotemporal fusion, faced limitations: denoising algorithms usually enhance temporal resolution without improving spatial quality, while spatiotemporal fusion models struggle with prolonged data gaps and heavy noise. This study proposes an optimized mangrove phenology extraction approach (OMPEA), which integrates Landsat and MODIS data with a denoising algorithm (e.g., Gap Filling and Savitzky–Golay filtering, GF–SG) and a spatiotemporal fusion model (e.g., Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model, ESTARFM). The key of OMPEA is that GF–SG algorithm filled data gaps from cloud cover and satellite transit gaps, providing high-quality input to ESTARFM and improving its accuracy of NDVI imagery reconstruction in mangrove phenology extraction. By conducting experiments on the GEE platform, OMPEA generates 1-day, 30 m NDVI imagery, from which phenological parameters (i.e., the start (SoS), end (EoS), length (LoS), and peak (PoS) of the growing season) are derived using the maximum separation (MS) method. Validation in four mangrove areas along the coastal China shows that OMPEA significantly improves the potential to capture mangrove phenology in the presence of incomplete data. The OMPEA significantly increased usable data, adding 7–33 Landsat images and 318–415 MODIS images per region. The generated NDVI series exhibits strong spatiotemporal consistency with original data (R²: 0.788–0.998, RMSE: 0.007–0.253) and revealed earlier SoS and longer LoS at lower latitudes. Cross-correlation analysis showed a 2–3 month lagged effects of temperature on mangroves’ growth, with precipitation having minimal impact. The proposed OMPEA improves the possibility of capturing mangrove phenology under non-continuous and low-resolution data, providing valuable insights for large-scale and long-term mangrove conservation and management. Full article

Traditional methods for crop data collection are labor-intensive, inefficient and, more costly compared to remote sensing (RS) techniques. This study aims to identify key climatic variables influencing maize and wheat yields and develop predictive models while also evaluating the performance of the CropWatch cloud yield prediction model (CW_YPM) in major agricultural regions of Ethiopia. Climate data from 54 meteorological stations spanning 2000–2021 were analyzed. RS data, including NDVI from MODIS at 250 m resolution, agroecological zones, and observed crop yield data, were utilized for model prediction and validation. Correlation analysis and a stepwise modeling approach with multiple regression models were applied. The results revealed regional variations in the effects of climatic parameters on yields, with vapor pressure deficits showing negative correlations and rainfall exhibiting positive correlations. Non-linear models generally outperformed linear models in yield prediction—using both climate-only (CO) and combined climate-NDVI data. The best CO model for maize in the Horo Guduru area achieved an RMSE of 0.392 tons/ha, an R² of 0.94, and an index of agreement (d) of 0.984. Incorporating NDVI improved accuracy, with the best maize model in the Illu Ababor area achieving an RMSE of 0.477 tons/ha, an R² of 0.91, and d of 0.976. CW_YPM also performed effectively across the study area. This research highlights the value of integrating critical climatic variables with the NDVI to enhance crop yield forecasting in Ethiopia, thereby-supporting agricultural planning and food security initiatives. Full article

Fractional vegetation cover (FVC) is an important indicator of regional ecological environment change, and quantitative research on the spatial and temporal distribution of FVC and the trend of change is of great significance to the monitoring, evaluation, protection, and restoration of regional ecology. This study estimates the FVC of the eastern Tibetan Plateau margin from 2000 to 2020 using the image element dichotomous model based on the Google Earth Engine platform using MODIS-NDVI images. It also investigates the temporal and spatial changes of the FVC in this region and its drivers using the Theil–Sen and Mann–Kendall trend tests, spatial autocorrelation analysis, geodetector, and machine learning approaches impact. The results of this study indicated a generally erratic rising tendency, with the Min River Basin (MRB) near the eastern tip of the Tibetan Plateau having an annual average FVC of 0.67 and an annual growth rate of 0.16%. The percentage of places with better vegetation reached 60.37%. The regional FVC showed significant positive spatial autocorrelation and was clustered. Driver analyses showed that soil type, DEM, temperature, potential evapotranspiration, and land use type were the main drivers influencing FVC on the eastern margin of the Tibetan Plateau. In addition, the random forest (RF) model outperformed the support vector machine (SVM), backpropagation neural network (BP), and long short-term memory network (LSTM) in FVC regression fitting. In summary, this study shows that the overall FVC in the eastern margin of the Tibetan Plateau is on an upward trend, and the regional ecological environment has improved significantly over the past two decades. Full article

The clumping index (CI) is a key structural parameter that quantifies the nonrandomness of the spatial distribution of vegetation canopy leaves. Investigating seasonal variations in the CI is crucial, especially for estimating the leaf area index (LAI) and studying global carbon and water cycles. However, accurate estimations of the seasonal CI have substantial challenges, e.g., from the need for accurate hot spot measurements, i.e., the typical feature of the bidirectional reflectance distribution function (BRDF) shape in the current CI algorithm framework. Therefore, deriving a phenologically simplified stable CI product from a high-frequency CI product (e.g., 8 days) to reduce the uncertainty of CI seasonality and simplify CI applications remains important. In this study, we applied the discrete Fourier transform and an improved dynamic threshold method to estimate the start of season (SOS) and end of season (EOS) from the CI time series and indicated that the CI exhibits significant seasonal variation characteristics that are generally consistent with the MODIS land surface phenology (LSP) product (MCD12Q2), although seasonal differences between them probably exist. Second, we divided the vegetation cycle into two phenological stages based on the MODIS LSP product, ignoring the differences mentioned above, i.e., the leaf-on season (LOS, from greenup to dormancy) and the leaf-off season (LFS, after dormancy and before greenup of the next vegetation cycle), and developed the phenologically simplified two-stage CI product for the years 2001–2020 using the MODIS 8-day CI product suite. Finally, we assessed the accuracy of this CI product (RMSE = 0.06, bias = 0.01) via 95 datasets from 14 field-measured sites globally. This study revealed that the CI exhibited an approximately inverse trend in terms of phenological variation compared with the NDVI. Globally, based on the phenologically simplified two-stage CI product, the CI_LOS is smaller than the CI_LFS across all land cover types. Compared with the LFS stage, the quality for this CI product is better in the LOS stage, where the QA is basically identified as 0 and 1, accounting for more than ~90% of the total quality flag, which is significantly higher than that in the LFS stage (~60%). This study provides relatively reliable CI datasets that capture the general trend of seasonal CI variations and simplify potential applications in modeling ecological, meteorological, and other surface processes at both global and regional scales. Therefore, this study provides both new perspectives and datasets for future research in relation to CI and other biophysical parameters, e.g., the LAI. Full article

Solar-induced chlorophyll fluorescence (SIF), as a direct indicator of vegetation photosynthesis, offers a more accurate measure of plant photosynthetic dynamics than traditional vegetation indices. However, the current SIF satellite products have low spatial resolution, limiting their application in fine-scale agricultural research. To address this, we leveraged MODIS data at a 1 km resolution, including bands b1, b2, b3, and b4, alongside indices such as the NDVI, EVI, NIRv, OSAVI, SAVI, LAI, FPAR, and LST, covering October 2018 to May 2020 for Shandong Province, China. Using the Random Forest (RF) model, we downscaled SIF data from 0.05° to 1 km based on invariant spatial scaling theory, focusing on the winter wheat growth cycle. Various machine learning models, including CNN, Stacking, Extreme Random Trees, AdaBoost, and GBDT, were compared, with Random Forest yielding the best performance, achieving R² = 0.931, RMSE = 0.052 mW/m²/nm/sr, and MAE = 0.031 mW/m²/nm/sr for 2018–2019 and R² = 0.926, RMSE = 0.058 mW/m²/nm/sr, and MAE = 0.034 mW/m²/nm/sr for 2019–2020. The downscaled SIF products showed a strong correlation with TanSIF and GOSIF products (R² > 0.8), and consistent trends with GPP further confirmed the reliability of the 1 km SIF product. Additionally, a time series analysis of Shandong Province’s wheat-growing areas revealed a strong correlation (R² > 0.8) between SIF and multiple vegetation indices, underscoring its utility for regional crop monitoring. Full article

The research initiatives envisaged by the PNRR (Italian National Recovery and Resilience Plan) include the creation of innovation ecosystems to promote collaboration between universities, research centers, and local institutions with a focus on territorial integration and sustainability. The NODES Project (Nord-Ovest Digitale E Sostenibile) is part of this research. In this context, the Laboratory of Geomatics of the University of Pavia, in collaboration with other partners, deals with the study of the suitability maps for the renowned Pinot Noir wine. To achieve this, we considered different thematic input layers: elevation, slope, aspect, soil depth and type, Land Use Land Cover maps, NDVI, and current and forecast climatic aspects. An important thematic layer is concerned with the air temperature, which requires high spatial and temporal resolution. In the selected study area, the Lombardy Region has some accurate and reliable weather stations with high temporal resolution but low spatial resolution (7 stations in 648.5 square kilometers, i.e., one every 92 square kilometers). In addition, we considered Land Surface Temperature (LST) MODIS maps: these maps have good spatial resolution but present some voids and low temporal resolution. From the first evaluations made, the temperatures reported by MODIS are not always in excellent agreement with the ones from monitoring stations. To evaluate MODIS as a data source, we decided to use Kriging spatio-temporal interpolation. Starting from multitemporal MODIS data matrices, we interpolate them to estimate the temperature of the weather stations, in order to compare the estimation with the real weather station data, thus allowing the validation of MODIS data. Full article

Spatial and temporal information about cropping patterns of single and multiple crops is important for monitoring crop production and land-use intensity. We used time-series MODIS NDVI 8-day composite data to develop annual cropping pattern products at a 250 m spatial resolution for China, covering the period from 2001 to 2023. To address the potential impacts of varying parameters in both data pre-processing and the peak detection algorithm on the accuracy of cropping pattern mapping, we employed a grid-search method to fine-tune these parameters. This process focused on optimizing the Savitzky–Golay smoothing window size and the peak width parameters using a calibration dataset. The results highlighted that an optimal combination of a five to seven MODIS composite window size in Savitzky–Golay smoothing and a peak width of four MODIS composites achieved good overall mapping accuracy. Pixel-wise accuracy assessments were conducted for the selected mapping years of 2001, 2011, and 2021. Overall accuracies were between 89.7% and 92.0%, with F1 scores ranging from 0.921 to 0.943. Nationally, this study observed a fluctuating trend in multiple cropping percentages, with a notable increase after 2013, suggesting shifts toward more intensive agricultural practices in recent years. At a finer spatial scale, the combination of Mann–Kendall and Sen’s slope analyses revealed that approximately 12.9% of 3 km analytical windows exhibited significant changes in cropping intensity. We observed spatial clusters of increasing and decreasing crop intensity trends across provinces such as Hebei, Shandong, Shaanxi, and Gansu. This study underscores the importance of data smoothing and peak detection methods in analyzing high temporal resolution remote sensing data. The generation of annual single/multiple cropping pattern maps at a 250 m spatial resolution enhances our comprehension of agricultural dynamics through time and across different regions. Full article

Desert vegetation is undergoing complex and diverse changes due to global climate change and human activities. To effectively utilize water resources and promote ecological recovery in desert areas, it is necessary to clarify the main driving mechanisms of vegetation growth in these regions. In this study, based on MODIS and Landsat 8 remote sensing image data, the vegetation changes and driving mechanisms before and after water diversion in the Kubuqi Desert from 2001 to 2020 were quantitatively analyzed using multiple linear regression, random forest, support vector machine, and deep neural network. The results show that the average NDVI in the study area has increased from 0.08 to 0.13 over the past 20 years, and the year of NDVI mutation corresponded with the lowest precipitation, which occurred in 2010. After the water diversion, under the combined influence of human and natural factors, NDVI increased steadily without any abrupt changes, indicating that water is the main limiting factor for vegetation growth. The change of NDVI also showed obvious spatial heterogeneity, among which the improvement of the southwest irrigation area was the most significant, and the area with NDVI above 0.1 showed an expanding trend, and the maximum value exceeded 0.4. This demonstrates that moderate water diversion can reduce desert areas, expand lake areas, and promote vegetation growth, yielding positive ecological effects. The integration of multiple linear regression, support vector machines, random forests, and deep neural network methods effectively reveals the driving mechanisms of NDVI and indirectly informs future water diversion intervals. Overall, these research results can provide a reliable reference for the efficient development of water diversion projects and have high application value. Full article

The rainfed cereal growing regions of Northern Kazakhstan experience significant yield fluctuations due to dependence on weather conditions. Early detection and monitoring of droughts is crucial for effective mitigation strategies in this region. This study emphasises the following objectives: (1) description of the current vegetation condition with a possible separation of short-term weather effects and (2) analysing trends of changes with their directionality and quantification. Terra MODIS satellite images from 2000 to 2023 are used. Differential indices—Normalised Difference Vegetation Index (NDVI) and Vegetation Condition Index (VCI)—are used to determine the characteristics of each current season. A key component is the comparison of the current NDVI values with historical maximum, minimum, and average values to identify early indicators of drought. NDVI deviations from multiyear norms and VCI values below 0.3 visually reflect changing vegetation conditions influenced by seasonal weather patterns. The results show that the algorithm effectively detects early signs of drought through observed deviations in NDVI values, showing a trend towards increasing drought frequency and intensity in Northern Kazakhstan. The algorithm was particularly effective in detecting severe drought seasons in advance, as was the case in June 2010 and May 2012, thus supporting early recognition of drought onset. The Integrated Vegetation Index (IVI) and Integrated Vegetation Condition Index (IVCI) time series are used for integrated multiyear assessments, in analysing temporal changes in vegetation cover, determining trends in these changes, and ranking the weather conditions of each growing season in the multiyear series. Areas with high probability of drought based on low IVCI values are mapped. The present study emphasises the value of remote sensing as a tool for drought monitoring, offering timely and spatially detailed information on vulnerable areas. This approach provides critical information for agricultural planning, environmental management and policy making, especially in arid and semi-arid regions. The study emphasises the importance of multiyear data series for accurate drought forecasting and suggests that this methodology can be adapted to other drought-sensitive regions. Emphasising the socio-economic benefits, this study suggests that the early detection of drought using satellite data can reduce material losses and facilitate targeted responses. Full article

The Wide-Swath Imaging (WSI) sensor is a Chinese satellite launched in 2022, capable of providing data at resolutions ranging from 75 to 600 m for monitoring aerosols, fire points, and dust, among other uses. In this study, we developed a Dark Dense Vegetation method to retrieve the Aerosol Optical Depth (AOD) quickly from WSI 600 m data. First, after splitting into three types according to the Normalized Difference Vegetation Index (NDVI), we calculated the empirical parameters of land reflectance between the red (0.65 μm) and blue (0.47 μm) channels using Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance products over the Beijing area. Second, the decrease in the NDVI was simulated and analyzed under different AODs and solar zenith angles, and we introduced an iterative inversion approach to account for it. The simulation retrievals demonstrated that the iterative inversion produced accurate results after less than four iterations. Thirdly, we utilized the atmospherically corrected NDVI for dark target identification and output the AOD result. Finally, retrieval experiments were conducted using WSI 600 m data collected over Beijing in 2023. The retrieved AOD images highlighted two air pollution events occurring during 3–8 March and 27–31 October 2023. The inversion results in 2023 showed a strong correlation with Aerosol Robotic Network station data (the correlation coefficient was greater than 0.9). Our method exhibited greater accuracy than the MODIS aerosol product, though it was less accurate than the Multi-Angle Implementation of Atmospheric Correction product. Full article