MODIS Data Research Articles

The Nexus between Land Use/Cover changes and Land Surface Temperature: Remote sensing based Two-Decadal Analysis

The use of Remote Sensing (RS) data is crucial for promptly detecting and monitoring changes in both short and long term, providing real time information on Land Use/Cover (LULC), Land Surface Temperature (LST), and Enhanced Vegetation Index (EVI), adapting spatio-temporal variations. The primary focus of this study is to assess the effect of LULC changes on LST in Tashk-Bakhtegan and Maharloo (TBM) lakes basin, Iran, within 2001, 2011, and 2021, using MODIS data. Specifically, five main LULC classes involving: water body, rangeland, cropland, urban area, and bareland were identified. Beside accuracy and transition of LULC maps using User Accuracy (UA), Producer Accuracy (PA), and Kappa Coefficient (KC), the analysis included changes in LULC, Enhanced Vegetation Index (EVI), and LST, as well as the relationship among them when vegetation cover was at its peak. Moreover, a one-way analysis of variance (ANOVA) test was performed to group these variables using Duncan's test. The results showed that the accuracy of LULC maps were more than 84% for all the years. Furthermore, the conversion of croplands to rangelands showed the most significant changes, with a total of 1311.38 km2 during 2001–2021. Average EVI remained almost stable across the total area, whereas average LST generally increased by 0.65 °C. Barelands consistently exhibited the highest temperatures in all the years, followed by urban areas. While no significant changes were observed in the EVI averages, significant changes were observed in the LST across all LULC classes in different years. The results also indicated a consistent negative correlation between LST and EVI, stronger in croplands than rangelands, with Spearman's correlation coefficient of −0.714, −0.674, and −0.623 over the total area in 2001, 2011, and 2021, respectively. The findings are crucial for land planners to comprehend the effects of LULC changes on LST to adopt appropriate strategies in the TBM lakes basin.

A combined drought index for monitoring and assessment of drought severity over India by integrating CHIRPS, MODIS and GRACE data

A combined drought index for monitoring and assessment of drought severity over India by integrating CHIRPS, MODIS and GRACE data

Identification of the Driving factors impacts of Land Surface Albedo over Iran: An analysis with the MODIS data

Albedo is a key parameter in climatic research and depends on environmental and climatic factors. Modeling these factors greatly contributes to understanding environmental variations. To this end, the data of Land Surface Albedo, Land Surface Temperature (LST), Vegetation, Snow, Elevation, Slope, and Aspect of the MODIS sensor from 1/1/2001 to 30/12/2021 with a 1000-m spatial resolution were used. After pre-processing, monthly, seasonal, and annual albedo modeling was performed using multiple linear regression (MLR) in the highlands of Iran. The results of monthly modeling revealed the salient direct role of snow on the albedo of Iran's highlands in all months, except for July, August, and September. In these months, due to the lack of snow coverage and the fruiting of agricultural lands and gardens, the inverse role of vegetation on albedo variations is determining. Seasonal examinations also showed that snow plays a significant role on the albedo of Iran's highlands in winter, spring, and fall; however, vegetation has a determining role in the summer. The annual results indicated that snow, vegetation, elevation, slope, LST, and aspect, respectively, are the factors affecting albedo in the highlands of Iran. Furthermore, the role of snow, LST, and aspect is positive, while the role of vegetation, elevation, and slope is negative on albedo.

Earth observation reveals the shifting patterns of China's lake colour driven by climate change and land cover

Water colour has been recognized as one of the most important Essential Climate Variables of the lake ecosystem, as it is directly related to changes in water constituents and almost all of the lake's ecological changes could alter water colour. Given the high retrieval accuracy from existing Earth observation satellite data, water colour, in terms of Forel Ule Index (FUI), can be a realistic indicator to track the long-term changes in the lake ecosystem and further explore the lake response to environmental changes. This paper aims to comprehensively investigate the spatiotemporal variation patterns of FUI in 159 large lakes (≥25 km2) across China during 2000–2022 based on the MODIS data and detect the climatic and anthropogenic driving forces of these changes. The 23 years of MODIS records revealed an overall downward trend of lake FUI across China, indicating the lakes in China shifted to bluer colour during the past two decades. Through driving factor analyses, the complicated interplay among lake colour, lake morphology, regional climate shifts and human interference dynamics was uncovered. In the long term, it was found the pronounced change in lake colour in the western lake zones was primarily attributed to climate warming and humidification, whereas that in the eastern lake zones was mainly related to the alterations in regional land cover during the past two decades. Seasonally, lake basin's air temperature was identified as the main factor impacting the seasonal patterns of lake colour, followed by wind speed and runoff. Spatially, there was high spatial variability in lake colour across China, which was mainly associated with lake elevation and lake basin's precipitation rate, although the factors exhibited considerable divergence across different zones. Based upon the above findings, the implications for lake environment protection and management in different regions of China were further discussed.

How the characteristics of land cover changes affect vegetation greenness in Guangdong, a rapid urbanization region of China during 2001-2022.

To evaluate the quantitative impacts of land cover change on vegetation greenness in the significantly human-impacted subtropical region, the characteristics of land cover change were explored by land use dynamic degree, transition matrix and normalized entropy. Various methods including Standardized coefficient, LMG (Lindeman-Merenda-Gold), GEN (Genizi measure) and CAR (Correlation-Adjusted Marginal Correlation) were employed to estimate the contributions of land cover changes on vegetation greenness using MODIS data during 2001-2022 in Guangdong. The conclusions revealed that land cover changes exhibited obvious temporal characteristics in Guangdong with a significantly increasing trend of normalized entropy indicating a more balanced distribution of land cover types under human intervention. NDVI (Normalized Difference Vegetation Index) tended to increase likely due to the large-scale increase in evergreen forest. With regard to the contributions of impact factors on vegetation greenness, the contributions evaluated by LMG, GEN and CAR showed that the natural variation of NDVI accounted for the major contribution (> 33%), while the changes of evergreen forest and grassland had the highest contribution (> 37%) according to Standardized coefficient. These differences were mainly due to the characteristics of land cover changes in Guangdong, the correlations among impact factors and the inherent attributions of the methods. Moreover, the expansions of evergreen forest and urban at the expense of the reductions of grassland and cropland also had significant impacts on NDVI (> 10%) according to LMG, GEN and CAR indicating that human-induced land cover changes had remarkable influences on NDVI.

Estimation, Spatiotemporal Dynamics, and Driving Factors of Grassland Biomass Carbon Storage Based on Machine Learning Methods: A Case Study of the Hulunbuir Grassland

Precisely estimating the grassland biomass carbon storage is vital for evaluating grassland carbon sequestration potential and the monitoring and management of grassland resources. With the increasing intensity of climate change (CC) and human activities (HA), it is necessary to explore spatiotemporal variations in biomass carbon storage and its response to CC and HA. In this study, we focused on the Hulunbuir Grassland, utilizing sample plots data, MODIS data, environmental factors (terrain, soil, and climate), location factor, and texture characteristics to assess the performance of four machine learning algorithms: random forest, support vector machine, gradient boosting decision tree, and extreme gradient boosting in estimating grassland aboveground biomass (AGB). Based on the optimal model combined with root-shoot ratio data, grassland distribution data, and carbon content coefficients, the spatiotemporal characteristics and driving factors of biomass carbon storage from 2001–2022 were analyzed. The results showed that (1) the random forest achieved the highest prediction accuracy for grassland AGB, making it appropriate for AGB estimation in the Hulunbuir Grassland. (2) The spectral indices were the key variables of the grassland AGB, especially the enhanced vegetation index and difference vegetation index. (3) The 22-year average total biomass (TB) of the study area was 1037.10 gC/m2, of which the 22-year average AGB was 48.73 gC/m2 and 22-year average belowground biomass was 988.37 gC/m2, showing a spatial distribution feature of gradual increase from west to east. (4) From 2001–2022, TB carbon storage showed an insignificant growth trend (p > 0.05). The 22-year average carbon storage of TB was 72.34 ± 18.07 gC. (5) Climate factors were the main driving factors for the spatial pattern of grassland TB carbon density, while the combined effects of CC and HA were the main contributors to the interannual increase in grassland TB carbon density.

Cyanobacterial blooms prediction in China’s large hypereutrophic lakes based on MODIS observations and Bayesian theory

Cyanobacterial harmful algal blooms (HABs) pose a significant threat to aquatic ecosystems, water quality, and public health, particularly in large hypereutrophic lakes. Developing accurate short-term prediction models is essential for early warning and effective management of HABs. This study introduces a Bayesian-based model aimed at predicting HABs in three of China’s large hypereutrophic lakes: Lake Taihu, Lake Chaohu, and Lake Hulunhu. By integrating MODIS data from the Terra and Aqua satellites with meteorological data spanning from 2010 to 2018, the model forecasts HABs distributions 1, 4, and 7 days in advance. Validation with meteorological data from 2019 to 2020 showed high accuracy, with 0.83 at the pixel level, 0.74 for zonal predictions, and 0.64 for lake-wide HABs area forecasts. Further evaluation using 2023 weather forecast data yielded similar accuracies of 0.78, 0.57, and 0.62, respectively. In addition to predicting the spatial extent of HABs, the model provides binary HABs maps, outbreak areas, and HABs status within lake zones. This method for building prediction models significantly enhances early warning and management capabilities for HABs, providing a scalable framework that can be adapted to other regions facing similar threats from HABs.

Mapping irrigation regimes in Chinese paddy lands through multi-source data assimilation

Water-saving irrigation (WI) is a crucial agricultural management with the benefits to save irrigation water, reduce energy consumption, and suppress methane emissions from paddy lands. Classifying WI practices from traditional flooding irrigation (FI) is a key component in detecting the rice irrigation status, which is significant to estimate the total agriculture-associated greenhouse gas emissions. In this study, we developed an automatic method to map irrigation regimes across Chinese paddy lands. First, we used seven variables related with irrigation facility or vegetation cover as proxy to generate representative WI and FI samples. Besides, we composited 123 features of optical bands and synthetic aperture radar from MODIS and Sentinel-1 data. Then, we trained a random forest model for each province with these samples. Finally, we applied the trained model to generate maps of WI/FI practices at 500 m resolution. Comparisons of the resultant maps with census data indicated highly accurate estimations of the WI area at a city- or province-level, with a R2 higher than 0.92. The overall accuracy of the classification was approximately 0.73, as validated through ground truth samples. Additionally, we also conducted a data quality analysis and confirmed the classification results were reliable in main rice production area of China. With the push towards carbon neutrality goals and the increasing demand for clean management practices, we developed and demonstrated an advanced method to produce near real-time maps of irrigation regimes and provide crucial data support for agricultural emissions reduction and irrigation management decisions.

Do Subsampling Strategies Reduce the Confounding Effect of Errors in Bispectral Retrievals on Estimates of Aerosol Cloud Interactions?

AbstractBi‐spectral retrievals of droplet effective radius and cloud optical depth are widely utilized to estimate aerosol cloud interactions (ACI) in warm clouds in the marine boundary layer. Here, we assess the effect of retrieval errors due to the neglect of 3D radiative transfer during the retrieval process on this analysis of ACI. We use an ensemble of stochastically‐modeled cloud fields and 3D radiative transfer simulations to study the retrieval errors at a solar zenith angle of 30°. Simulated retrieval biases in droplet number concentration (Nd) for all three MODIS channels vary systematically from +35% to −80% as cloud heterogeneity increases. Pixel‐level errors can be much larger. Commonly utilized subsampling strategies do not reduce the systematic variation in retrieval error. Negative error correlations between optical depth and droplet effective radius produce spuriously negative slopes between the logarithm of liquid water path and Nd (−1.0 to −0.3). Pixels at the center of (8 km)2 patches that are not overcast have a relative bias in Nd of −50%. The relative frequency of these biased pixels varies linearly with the clear fraction in MODIS data and form the basis for a simple parameterization of Nd bias that varies with cloud fraction (CF). Using this parameterization, synthetic data experiments indicate that estimates of the first indirect effect in the tropical ocean can be overestimated by up to 30%, and the effect of aerosol on CF is overestimated by 50%, due to neglecting the correlation between retrieval errors and cloud microphysics.

Characterization of Water Bodies through Hydro-Physical Indices and Anthropogenic Effects in the Eastern Northeast of Brazil

Brazil, despite possessing the largest renewable freshwater reserves in the world (8.65 trillion m3 annually), faces growing challenges in water management due to increasing demand. Agriculture, responsible for 68.4% of water consumption, is one of the main drivers of this demand, especially in the São Francisco River Basin, where irrigation accounts for 81% of total water withdrawals. Water bodies play a crucial role in sustaining ecosystems and supporting life, particularly along the East-West axis of Alagoas, a water-rich region in the ENEB. This study aimed to map and quantify the spatiotemporal variations of water bodies in the ENEB region and assess the impacts of human activities using MODIS satellite data, applying hydrological indices such as NDWI, MNDWI, and AWEI. Between 2003 and 2022, significant variations in the extent of water bodies were observed, with reductions of up to 100 km2 during dry periods and expansions of up to 300 km2 during wet seasons compared to dry periods. AWEI and MNDWI proved to be the most effective indices for detecting water bodies with MODIS data, providing accurate insights into water dynamics. Additionally, the MapBiomas Rios dataset, despite being resampled from a 30 m to a 500 m resolution, offered the most accurate representation of water bodies due to its methodology for data acquisition. Changes in albedo and surface temperature were also detected, highlighting the influence of climate change on the region’s water resources. These findings are crucial for guiding the sustainable management of water resources, not only in Alagoas but also in other regions of Brazil and similar semi-arid areas around the world. The study demonstrates the hydrological variability in the state of Alagoas, indicating the need for adaptive strategies to mitigate the impacts of climate change and anthropogenic pressures, supporting the need for informed decision-making in water resource management at both local and national levels.

Reversal of the Spatiotemporal Patterns at the End of the Growing Season of Typical Steppe Vegetation in a Semi-Arid Region by Increased Precipitation

Vegetation phenology serves as a sensitive indicator of climate change. However, the mechanism of the hydrothermal role in vegetation phenology changes is still controversial. Utilizing the data on the Fraction of Absorbed Photosynthetically Active Radiation (FPAR) from MODIS and meteorological data, the study employed the dynamic threshold method to derive the end of the growing season (EOS). The research delved into the spatiotemporal patterns of the EOS for typical steppe vegetation in the semi-arid region of Inner Mongolia spanning the period from 2003 to 2022. Furthermore, the investigation scrutinized the response of EOS to temperature and precipitation dynamics. The results showed that (1) the dynamic threshold method exhibited robust performance in the EOS of typical steppe vegetation, with an optimal threshold of 45% and a Root Mean Square Error (RMSE) of 5.5 days (r = 0.81); (2) the spatiotemporal patterns of the EOS of typical steppe vegetation in the semi-arid region experienced a noteworthy reversal from 2003 to 2022; (3) the lag effects of precipitation and temperature on the EOS were found, and the lag time scales were mainly 1 month and 2 months. The increase in precipitation in August was the key reason for the reversal of the EOS, and satisfying the precipitation was a prerequisite for the temperature to delay the EOS. The study emphasizes the important role of water availability in regulating the response of the EOS to hydrothermal factors and highlights the utility and reliability of FPAR in monitoring the EOS of typical steppe vegetation.

Early leaf senescence under drought conditions in the Northern hemisphere

Changes in the dates of autumn foliar senescence (DFS) have significant impacts on regional carbon uptake, while current approaches for the estimation of DFS are still lacking. The most important issue is that there are complicated factors that affect the DFS, among which drought effects probably have contributed the most. Using long-term DFS observations derived from the third-generation normalized difference vegetation index dataset (NDVI3g), we found a wider spread of earlier DFS trends over the Northern Hemisphere from 1999 to 2015, three times larger than that from 1982 to 1998. The five multivariate analysis of variance approaches consistently suggest the key role of drought in regulating these changes. We therefore derived a new DFS algorithm with the standardized precipitation evapotranspiration index (SPEI) to characterize these drought effects, and validations from both NDVI3g and MODIS data demonstrated that our new algorithm provided significantly improved estimates of DFS for all plant functional types, with higher accuracy for water-limited ecosystems. We further applied this new algorithm to predict DFS under various shared socioeconomic pathways (SSPs) by the end of this century, and we found overall earlier DFS than the current expectations. Our results therefore highlight the importance of drought in the modeling of plant phenology using remote sensing observations and thus are highly important for understanding the relationships between land carbon sinks and climate change, especially given that droughts are projected to be more severe and frequent in the future.

Variation in Glacier Albedo on the Tibetan Plateau between 2001 and 2022 Based on MODIS Data

Albedo is a primary driver of the glacier surface energy balance and consequent melting. As glacier albedo decreases, it further accelerates glacier melting. Over the past 20 years, glaciers on the Tibetan Plateau have experienced significant melting. However, our understanding of the variations in glacier albedo and its driving factors in this region remains limited. This study used MOD10A1 data to examine the average characteristics and variations in glacier albedo on the Tibetan Plateau from 2001 to 2022; the MOD10A1 snow cover product, developed at the National Snow and Ice Data Center, was employed to analyze spatiotemporal variations in surface albedo. The results indicate that the albedo values of glaciers on the Tibetan Plateau predominantly range between 0.50 and 0.60, with distinctly higher albedo in spring and winter, and lower albedo in summer and autumn. Glacier albedo on the Tibetan Plateau decreased at an average linear regression rate of 0.06 × 10−2 yr−1 over the past two decades, with the fastest declines occurring in autumn at an average rate of 0.18 × 10−2 yr−1, contributing to the prolongation of the melting period. Furthermore, significant variations in albedo change rates with altitude were found near the snowline, which is attributed to the transformation of the snow and ice surface. The primary factors affecting glacier albedo on the Tibetan Plateau are temperature and snowfall, whereas in the Himalayas, black carbon and dust primarily influence glacier albedo. Our findings reveal a clear decrease in glacier albedo on the Tibetan Plateau and demonstrate that seasonal and spatial variations in albedo and temperature are the most important driving factors. These insights provide valuable information for further investigation into surface albedo and glacier melt.

CALIPSO-based aerosol extinction profile estimation from MODIS and MERRA-2 data using a hybrid model of Transformer and CNN

Acquiring aerosol vertical distribution information is crucial to accurately quantify the aerosol radiation effect on climate and understand the environmental pollution mechanism of the atmosphere. Passive remote sensing has shown its capability to gain large-scale, high spatiotemporal resolution aerosol vertical information such as aerosol layer height (ALH). However, it is still challenging to extract detailed aerosol vertical distribution information, e.g., aerosol extinction profile (AEP), from passive observations. To fill this gap, this study proposed a hybrid model of Transformer and convolutional neural network (CNN) to estimate the AEP from passive multispectral remote sensing (MODIS) measurements with the aid of three-dimensional reanalysis data (MERRA-2). Specifically, the model is learned to estimate the AEP, which is called AproNet, by using the active space-borne lidar (CALIPSO) data as supervised information. Besides, we design a shape invariant loss (SIL) to better capture the shape characteristics of the AEP and incorporate an auxiliary scene awareness loss (SAL) to enhance the model's generalization capacity and physical reliability outside the CALIPSO orbit. The extensive quantitative experiments show that the AEPs estimated by the proposed model agree well with the CALIPSO measurements with an overall performance of IOA=0.821, R=0.800, MAE= 0.014, and RMSE= 0.041, respectively. Qualitative comparisons also demonstrate the model's reliability in estimating the aerosol three-dimensional spatial distribution. Independent year test and comparisons with ground-based lidar measurements further indicate the robustness of the proposed model despite some degradation in performance. However, the incompleteness and uncertainty of the CALIOP products limited the performance of the proposed model to some extent. In the future, the model needs to be further physically constrained and strengthened with more data sources to improve reliability. In general, this study paves the way for acquiring aerosol extinction profiles with high spatiotemporal resolution over a large geographical space.

Biogenic Volatile Organic Compound Emission and Its Response to Land Cover Changes in China During 2001–2020 Using an Improved High‐Precision Vegetation Data Set

AbstractBiogenic volatile organic compounds (BVOCs) are regarded as important precursors for ozone and secondary organic aerosol, mainly from vegetation emissions. In the context of the expanding trend of vegetation greening, the development of high‐precision vegetation data and accurate BVOC emission estimates are essential to develop effective air pollution control measures. In this study, by integrating the multi‐source vegetation cover data, we established a high‐resolution vegetation distribution (HRVD) data set to develop a high spatio‐temporal resolution emission inventory and investigated the impact of different land cover data sets on emission simulation and impact of land cover change on BVOC emissions during 2001–2020. The annual total BVOC emissions in China for 2020 was 15.66 Tg, which were mainly from trees. The emissions simulated by CNLUCC and MODIS data sets were 1.53% and 1.72% higher than those simulated by HRVD data sets, respectively. The spatial distribution of emission differences was consistent with that of land cover differences. The simulated BVOC emissions by the HRVD data set had the best accuracy as they improved the bias between modeling and observation from 69.06% to 65.35% and decreased the underprediction of observations by a factor of 2.13 compared with simulation by MEGAN default vegetation data. The annual BVOC emissions caused by changing vegetation distribution and LAIv (LAI of vegetation covered surfaces) enhanced at a rate of 72.06 Gg yr−1 during 2001–2020. LAIv was the main driver of emission variations. The total OH reactivity of the resulted BVOC emissions increased at a rate of 1.59 s−1 yr−1, with isoprene contributed the most.

Trophic Status Monitoring for the Bangka Strait in Indonesia during the COVID-19 Period Using MODIS Data

Trophic Status Monitoring for the Bangka Strait in Indonesia during the COVID-19 Period Using MODIS Data

Detecting offshore wind farm-induced wind wake signatures on sea surface using MODIS data

Detecting offshore wind farm-induced wind wake signatures on sea surface using MODIS data

Analysis of the Influence of Driving Factors on Vegetation Changes Based on the Optimal-Parameter-Based Geographical Detector Model in the Yima Mining Area

The growth of vegetation directly maintains the ecological security of coal mining areas. It is of great significance to monitor the dynamic changes in vegetation in mining areas and study the driving factors of vegetation spatial division. This study focuses on the Yima mining area in Henan Province. Utilizing MODIS and multi-dimensional explanatory variable data, the Theil–Sen Median + Mann–Kendall trend analysis, variation index, Hurst index, and optimal-parameter-based geographical detector model (OPGD) are employed to analyze the spatiotemporal changes and future trends in the EVI (enhanced vegetation index) from 2000 to 2020. This study further investigates the underlying factors that contribute to the spatial variation in vegetation. The results indicate the following: (1) During the period studied, the Yima mining area was primarily characterized by a moderate-to-low vegetation cover. The area exhibited significant spatial variation, with a notable pattern of “western improvement and eastern degradation”. This pattern indicated that the areas that experienced improvement greatly outnumbered the areas that underwent degradation. Moreover, there was an inclination towards a deterioration in vegetation in the future. (2) Based on the optimal parameter geographic detector, it was found that 2 km was the optimal spatial scale for the analysis of the driving factors of vegetation change in this area. The optimal parameter combination was determined by employing five spatial data discretization methods and selecting an interval classification range of 5–10. This approach effectively addresses the subjective bias in spatial scales and data discretization, leading to enhanced accuracy in vegetation change analysis and the identification of its driving factors. (3) The spatial heterogeneity of vegetation is influenced by various factors, such as topography, socio-economic conditions, climate, etc. Among these factors, population density and mean annual temperature were the primary driving forces in the study area, with Q > 0.29 and elevation being the strongest explanatory factor (Q = 0.326). The interaction between temperature and night light was the most powerful explanation (Q = 0.541), and the average Q value of the interaction between the average annual temperature and other driving factors was 0.478, which was the strongest cofactor among the interactions. The interactions between any two factors enhanced their impact on the vegetation’s spatial changes, and each driving factor had its suitable range for affecting vegetative growth within this region. This research provides scientific support for conserving vegetation and restoring the ecological system.

Spatiotemporal Evolution and Influencing Factors of Heat Island Intensity in the Yangtze River Delta Urban Agglomeration Based on GEE

Urban agglomerations significantly alter the regional thermal environment. It is urgent to investigate the evolution and influence mechanisms of urban agglomeration heat island intensity from a regional perspective. This study is supported by Google Earth Engine long-term MODIS data series. On the basis of estimating surface urban heat island intensity (SUHI) in the Yangtze River Delta urban agglomeration from 2001 to 2020 based on the suburban temperature difference method, the causes of heat islands in the urban agglomeration were analyzed by using geographical detector analysis. Additionally, the heat island proportion (PHI) and SUHI indicators were used to compare and analyze the changing characteristics of the urban heat island effect of ten representative cities. The research reveals the following: (1) The average SUHI of the study area increased from 0.11 °C in 2001 to 0.29 °C in 2020, with an average annual increase rate of 0.009 °C. (2) According to the results of the geographical detector analysis, SUHI was influenced by several driving factors exhibiting obvious seasonal variations. (3) SUHI difference between cities is significant in the summer (1.52 °C), but smallest in the winter; the PHI difference between cities is larger in the autumn (46.7%), while it is smaller in the summer. The research findings aim to effectively serve the formulation of collaborative development plans for the Yangtze River Delta urban agglomeration.

A dataset of NDVI for the vegetation growing season in Central Asia with a resolution of 250 m (2001–2020) and 30 m (2020)

Central Asia is the largest arid and semi-arid region in the Northern Hemisphere, and its ecological environment is extremely fragile and susceptible to the effects of global climate change. Maintaining the stability of the region's ecosystem is crucial to global economic and social development due to its unique geographic location. Vegetation serves as a significant indicator of the ecological environment, and the spatial and temporal distribution pattern, as well as the change trend of vegetation, are important indicators in evaluating the ecological status of the region. The Normalized Difference Vegetation Index (NDVI) is a commonly used remote sensing index to study vegetation, which characterizes the spatio-temporal changes of vegetation. This dataset utilized MODIS13Q1 to generate long-term time series growing season mean NDVI data with a spatial resolution of 250m in Central Asia from 2001 to 2020. To obtain the 2020 growing season mean NDVI data with a higher spatial resolution of 30m, the Cubist algorithm based on rule segmentation regression was utilized to fuse the Landsat data and MODIS data. Meanwhile, this dataset carried out quality control of data products from three aspects: data source quality control, model training optimization, and model independent verification to ensure the accuracy and reliability of data. The generation of this dataset provides powerful data support for the analysis of vegetation dynamic change and spatial pattern in Central Asia.