MODIS Data Research Articles

Analysis of regional variation of water transparency in the Yellow Sea and East China Sea based on MODIS data

Analysis of regional variation of water transparency in the Yellow Sea and East China Sea based on MODIS data

Adaptive meta-modeling of evapotranspiration in arid agricultural regions of Saudi Arabia using climatic factors, drought indices and MODIS data

Adaptive meta-modeling of evapotranspiration in arid agricultural regions of Saudi Arabia using climatic factors, drought indices and MODIS data

Analysis of the impact mechanisms and driving factors of urban spatial morphology on urban heat islands

The intensity of urban heat islands (UHI) is closely related to urban underlying surface characteristics. This study investigates the effects of underlying surfaces on land surface temperature (LST) in the highly urbanized central districts and rapidly developing peri-urban areas of Tianjin, China. Ten driving factors linked to urban spatial morphology, land surface, and socioeconomic conditions were selected from 2 and 3D perspectives. LST was derived using Landsat 8 and MODIS data from August 28, 2020. XGBoost and SHAP were applied to analyze the contributions of individual and interacting factors on LST. The results showed that: (1) In the central districts, the contributions of NDVI (0.89), BH (0.30), and Albedo (0.25) were the highest. In the peri-urban areas, the contributions of NDVI (0.90), Albedo (0.60), and NLI (0.42) were the highest. NDVI showed a significant negative correlation with LST in both areas, with a more pronounced cooling effect compared to other factors. (2) In the central districts, the combination of BH and BD had the greatest impact on LST, with cooling effects observed when BH > 20m and BD decreased. In the peri-urban areas, the combination of NDVI and PIS was most influential, with higher PIS increasing LST when NDVI < 0.5. (3) Urban spatial parameters had a weaker impact on LST in the peri-urban areas compared to the central districts, where socioeconomic parameters played a stronger role. Overall, 2D perspectives had a greater impact on LST than 3D perspectives. The findings highlight the need for tailored regulatory measures based on urban development stages.

Scalogram habitat measures as predictors of bird abundance

Birds select habitat characteristics, such as variability in habitat structure, across multiple spatial scales (grain and extent). Measuring habitat variability at multiple scales can better capture factors that influence avifauna communities than focusing on one scale only. One valuable tool in assessing habitat heterogeneity is the cumulative dynamic habitat index (DHI), which is derived from satellite data and captures temporal variability in vegetation productivity. Our goals were to develop new habitat measures from the cumulative DHI at multiple scales based on scalograms, and to test their performance in models of bird abundance. We counted birds at 188 plots during three breeding seasons (2007–2009) at Fort McCoy military installation, USA, to assess the abundance of forest (ovenbird), shrubland (indigo bunting), and grassland (grasshopper sparrow) bird specialists. We then calculated NDVI based on PlanetScope (3 m), Sentinel‐2 (10 m), Landsat‐8 (30 m), and MODIS (250 m) data to quantify cumulative DHI. We summarized the averaged NDVI cumulative DHI within multiple extents around each bird survey and developed 11 new habitat measures to test their predictive power in models of bird abundance. We found positive relationships between cumulative DHI at different extents and the abundances of both ovenbirds and indigo buntings, a forest and a shrubland specialist, respectively; and a negative relationship with grasshopper sparrows, a grassland specialist. In multiple linear regression models that incorporated single‐ and multi‐grain predictors, the scalogram habitat measures explained moderate to high levels of variability in bird abundance, with R2 = 0.77, 0.37, and 0.75 for our forest, shrubland, and grassland specialists, respectively. Our results show that scalograms are an effective tool for capturing multiscale habitat configuration, because they capture the variability of habitat conditions in forests, shrublands, and grasslands. The scalogram habitat measures that we developed can be computed using our new R package ‘scalogram'.

A global 1 km resolution daily surface longwave radiation product from MODIS satellite data from 2000–2023

Surface longwave radiation (SLWR) is one of the two components of the surface radiation budget (SRB), which is a driving force of many land surface process models, the water cycle and climate change. To our knowledge, there are currently no publicly accessible long-term high-resolution (1 km) global daily surface longwave radiation products. We developed hybrid methods for estimating the 1 km resolution instantaneous clear-sky surface longwave upwelling radiation (SLUR) and surface longwave downwelling radiation (SLDR) and the cloud base temperature-based (CBT) single-layer cloud model (SLCM) for estimating the instantaneous cloudy-sky SLDR from MODIS data. The Essential thermaL Infrared remoTe sEnsing (ELITE) broadband emissivity (BBE) product and reanalysis surface temperature were employed to calculate the cloudy-sky SLUR. Synchronized with the diurnal information extracted from the ERA5 reanalysis data, we generated a 1 km resolution all-sky daily surface longwave radiation product (including SLUR and SLDR) via temporal integration. The produced daily SLUR and SLDR were validated via ground measurements collected from 369 sites distributed worldwide from nine flux networks. The root mean square error (RMSE) of the daily SLUR is less than 18 W/m2, and the RMSE of the daily SLDR is approximately 25 W/m2. The accuracy is commensurate with that of CERES-SYN.

Geospatial Analysis of the 2019-20 Desert Locust damage in northern India using MODIS and VIIRS Satellite Data

Geospatial Analysis of the 2019-20 Desert Locust damage in northern India using MODIS and VIIRS Satellite Data

Research on cloud detection method based on voting ensemble learning using FY-4B satellite data

Machine learning is widely used for satellite cloud detection. Ensemble models generally offer superior generalization over single models in complex scenarios. Addressing the common use of homogeneous ensembles, this study introduces heterogeneous base learners (SVM, NB, LR, DT, RF, MLP) trained on cloud features from FY-4B AGRI infrared data. We propose a Voting Ensemble Learning method to enhance generalization and adaptability for stable cloud detection. Results show that while RF and MLP excel individually, the Voting Ensemble significantly boosts overall accuracy and stability. Cross-validation with MODIS data confirms >91% accuracy for the ensemble over deep sea, shallow water, land, and snow, with false alarm rates generally <8% (except 12% for snow). The ensemble performs stably across seasons. Compared to single models, the Voting Ensemble demonstrates markedly improved adaptability, accuracy, and robustness across diverse seasons and scenarios, offering a more reliable solution for complex cloud detection challenges.

The evolution of the 2022–2024 eruption at Home Reef, Tonga, analyzed from space shows vent migration due to erosion

On September 9, 2022, a new eruption period began at the submarine volcano Home Reef, part of the Tonga Volcanic Arc. We integrated multi-sensor/multi-platform satellite datasets, including very high spatial resolution TerraSAR-X radar and PlanetScope multispectral data, together with Sentinel-2 and Landsat-8/9 as well as MODIS and VIIRS thermal data to monitor and characterize this latest eruption at Home Reef over a two-year period. Here, we present the results from this multi-sensor approach, used to investigate eruption dynamics (thermal activity and relative intensity level) and delineate changes in the shape and area of the newly formed island. The eruption showed four distinct phases: During September–October 2022, lava flows formed a ~ 54,900 m² circular island. In the following three eruption phases, the island grew towards the south (September–November 2023) and east (January 2024 and June–September 2024), expanding the island’s area to over 122,000 m². During each subsequent phase, the eruptive vent migrated toward the side of the island where the most erosion had occurred since the previous phase. This has implications for volcanic and tsunami hazards from island-forming eruptions of this type.

Change Patterns of Ecological Vulnerability and Its Dominant Factors in Mongolia During 2000–2022

Under global climate change, the ecological vulnerability issue in Mongolia has become increasingly severe. However, the change process of the ecological environment and the dominant driving factors in different periods and sub-regions of Mongolia are not clear. In this paper, we propose a new ecological vulnerability index for Mongolia using MODIS data, combined with the Geographical Detector and the gravity center model, to reveal the spatiotemporal changes and driving mechanisms of ecological vulnerability in Mongolia from 2000 to 2022. The results show the following: (1) the newly proposed remote sensing ecological vulnerability index has high applicability in ecosystems mainly in Mongolia, with an accuracy rate of 89.39%; (2) Mongolia belongs to the category of moderate vulnerability, with an average ecological vulnerability index of 1.57, and the center of vulnerability is shifting toward the southwest direction; (3) Tmax is the leading driving factor of ecological vulnerability in Mongolia, especially at high altitudes and in arid regions, where it directly affects vegetation growth, desertification, and water availability. The dominant interactive factors have shifted from Tmax ∩ Tmin to Tmin ∩ PRE, with PRE being the leading factor in the eastern, central, and southern regions of Mongolia, Tmax being the leading factor in the western region, and Tmin being the leading factor in the northwestern region. This study provides an index system for constructing the ecological vulnerability system in Mongolia and offers scientific references for the regional protection of the ecological environment in Mongolia.

Temporal–Spatial Partial Differential Equation Modeling of Land Cover Dynamics via Satellite Image Time Series and Sparse Regression

Land cover dynamics play a critical role in understanding environmental changes, but accurately modeling these dynamics remains a challenge due to the complex interactions between temporal and spatial factors. In this study, we propose a novel temporal–spatial partial differential equation (TS-PDE) modeling method combining sparse regression to uncover the governing equations behind long-term satellite image time series. By integrating temporal and spatial differential terms, the TS-PDE framework captures the intricate interactivity of these factors, overcoming the limitations of traditional pixel-wise prediction methods. Our approach leverages 1×1 convolutional kernels within a convolutional neural network (CNN) solver to approximate derivatives, enabling the discovery of interpretable equations that generalize across temporal–spatial domains. Using MODIS and Planet satellite data, we demonstrate the effectiveness of the TS-PDE method in predicting the value of the normalized difference vegetation index (NDVI) and interpreting the physical significance of the derived equations. The numerical results show that the model achieves good performance, with mean structural similarity index (SSIM) values exceeding 0.82, mean peak signal-to-noise ratio (PSNR) values ranging from 28.5 to 32.8, and mean mean squared error (MSE) values approximating 9×10−4 for low-resolution MODIS images. For high-resolution Planet images, this study emphasizes the efficacy of TS-PDE in terms of PSNR, SSIM, and MSE metrics, with all datasets exhibiting an average SSIM value of over 0.81, an average PSNR maximum of 30.9, and an average MSE of less than 0.0042. The experimental findings demonstrate the capability of TS-PDE in deriving governing equations and providing effective predictions for the regional-scale dynamics of these time series images. The findings of this study provide potential insights into the mathematical modeling of land cover dynamics.

The use of a shuffled complex evolution algorithm for calibration of a dynamic leaf emergence model leads to better spatial ETa predictions

ABSTRACT Dynamic phenology has so far been a modelling aspect that has received little attention. However, it has been shown that leaf emergence takes place earlier due to the shift in vegetation phase caused by climate change and is strongly dependent on temperature. Here, we demonstrate the calibration of a model for dynamic phenology within the water balance model WaSiM. Temperature sums and dormancy are used as controlling variables. The calibration of the respective parameters was realised using a shuffled complex evolution algorithm. ETa relevant parameters were calibrated based on MODIS data as a reference. Evaluation was done by comparing the ETa curves to MODIS ETa curves as well as a comparison of spatial ETa patterns based on Landsat ETa data. The evaluation shows that the dynamic phenology model used is capable of predicting the start of leaf emergence while also leading to better fitting evapotranspiration curves for the deciduous forest compared with the initial static parameterisation approach.

Utilizing Remote Sensing Data to Evaluate the Urban Heat Island: A Case Study of Quetta City, Balochistan

The urban heat island (UHI) effect presents a significant environmental challenge in rapid urbanization. Its mitigating impacts on public health, power consumption, and urban livability require a brief understanding of energy dynamics. MODIS satellite data, has detailed spatial and temporal coverage that serves as an essential tool in analyzing temperature trends, changes in land cover, and other related variables. Therefore, this study leverages MODIS data to analyze the impact of UHI, identifying significant changes in temperature distribution patterns over time. Our results inferred expansion in the high-temperature zone, shrinking in the central temperature zone, and comparatively stable in the low-temperature zone. These trends align with the trajectory of global warming and emphasize a wide-ranged impact on the ecosystem, weather patterns, and human activities. These findings trend a pronounced peak in global warming during the months observed for rainfall, underscoring a clear connection to climate change. However, the analysis highlights the limitations of short-term datasets in capturing long-term UHI trends and calls for more comprehensive temporal data to visualize geographic complexity. urbanization and climate change in the region It provide the basis for creating sustainable urban planning strategies and important step towards achieving climate-friendly urban ecosystems that can mitigate the cascading effects of global warming.

Snow Cover Variability and Trends over Karakoram, Western Himalaya and Kunlun Mountains During the MODIS Era (2001–2024)

Monitoring the snow cover variability and trends is crucial due to its significant contribution to river formation and sustenance. Using gap-filled MODIS data over the 2001–2024 period, the spatial distribution and temporal evolution of three snow cover metrics were studied: number of days, onset and end of the snow cover season across fourteen regions covering the Karakoram, Western Himalayas and Kunlun Mountains. The obtained signals exhibit considerable complexity, making it difficult to find a unique factor explaining their variability, even if elevation emerged as the most important one. The mean values of snow-covered days span from about 14 days in desert regions to about 184 days in the Karakoram region. Given the high interannual variability, the metrics show no significant trend across the study area, even if significant trends were identified in specific regions. The obtained results correlate well with the ERA5 and ERA5-Land values: the Taklamakan Desert and the Kunlun Mountains experienced a significant decrease in the snow cover extent possibly associated with an increase in temperature and a decline in precipitation. Similarly, the Karakoram and Western Himalayas region show a positive snow cover trend possibly associated with a stable temperature and a positive precipitation trend.

Global reconstruction of gridded aridity index and its spatial and temporal characterization from 2003 to 2022

ABSTRACT Aridity index (AI) is an effective estimator of drought status, and spatiotemporally continuous long-term AI dataset is critical for drought assessment and applications. Due to the spatial heterogeneity of global climate and topography, there exist significant uncertainties of AI estimates in areas with sparse ground observations, and high-resolution global AI estimation remains a challenge. In this study, we propose an LSTM-based approach to model the nonlinear intra-annual relationship between satellite-derived data and AI and enhance model performance through ensemble learning by leveraging MODIS data at different observation times. A long-term annually gridded global AI dataset is generated at a resolution of 0.05° × 0.05° from 2003 to 2022. Validation against the Global Surface Summary of the Day database yields biases, root mean squared errors and coefficients from −0.04 to 0.02, 0.19 to 0.86, and 0.62 to 0.83 across different continents. Comparisons with AI estimates based on Climatic Research Unit or ERA5-Land datasets further demonstrate the high accuracy of our AI estimates. Preliminary analysis reveals a global wetting trend over the past two decades. This dataset offers valuable support for research on dryland ecosystems, agriculture, and climate change, offering critical insights to address global environmental and sustainability challenges.

Development of Automatic Detection of Dust Storms for MODIS Satellite Images over Iraq Using ArcGIS Model Builder

Geographical Information Systems (GIS) provides a set of instruments and techniques to explain and display different geographical data, and ArcGIS Model Builders consider a very beneficial technique to represent these procedures in a very effective and influential manner. This paper indicates that using the ArcGIS tool Model Builder is the preferred technique to display the flux of procedures, in which the model's design is highly preferable to decrease the time consumed to apply any procedure. One model was used to explain the best methodology to resolve the issue, which deals with dust storm detection over Iraq using MODIS data. This study Facilitate Terra and Aqua MODIS satellite images; the first case was Terra / MODIS during the first half of the day with NDDI and IDDI, while the second case was Aqua/ MODIS during the second half of the day with the three thermal indices MEDI, BTV, TB beside IDDI. This study displays the effectiveness of these indices in two cases to show their validity in detecting dust storms compared to synoptic data over the study area. Besides, it displays ArcGIS Model Builder's importance in applying these Indices more efficiently and smoothly to benefit from it in serving society and the scientific community.

Spatiotemporal analysis of sea ice in the Weddell Sea of Antarctic based on GTWR

This study investigates the spatiotemporal dynamics of Antarctic sea ice concentration (SIC) and its interactions with environmental factors from 2011 to 2023, focusing on the Weddell Sea. SIC products derived from MODIS data were assessed and compared with six widely used datasets, including AMSR2/NT2 and MWRI/NT2. Among these, MWRI/NT2 exhibited the highest consistency with MODIS-derived SIC, achieving a correlation coefficient of 0.94, the lowest bias (0.23%), and the smallest mean absolute deviation (MAD) and root mean square deviation (RMSD), making it the preferred dataset for further analysis. Seasonal trends reveal that SIC experienced the most significant decline during autumn (–10.7 ± 2.3 × 10³ km² yr⁻¹) and the smallest reduction in winter (–1.3 ± 0.5 × 10³ km² yr⁻¹). Correlation analysis identified sea surface temperature (SST), wind speed, and latent heat flux (LHF) as the primary drivers of seasonal SIC variability, with SST exhibiting strong negative correlations across all seasons (r = –0.81, p < 0.01). Spatially, SIC in the Weddell Sea displayed significant heterogeneity in its relationship with environmental factors. SST demonstrated a negative correlation with SIC, particularly in the western Weddell Sea, with lags of –3 to –5 months. LHF consistently promoted sea ice growth, with the strongest influence along the eastern Weddell Sea coast. Zonal and meridional winds exhibited both promoting and suppressing effects on SIC depending on the region and time period, reflecting complex wind-sea ice interactions. Mean sea level pressure (MSLP) showed opposing effects: suppressing SIC in the northern Weddell Sea and promoting it in the southern Weddell Sea. The use of geographically and temporally weighted regression (GTWR) allowed the quantification of the spatial and temporal heterogeneity of these factors, with LHF identified as the most influential variable (median standardized coefficient = 1.44). These findings highlight the intricate interplay between atmospheric, oceanic, and sea ice dynamics in the Weddell Sea and provide a framework for understanding the drivers of sea ice variability under changing climatic conditions.

Multi-Temporal Analysis of Cropping Patterns and Intensity Using Optical and SAR Satellite Data for Sustaining Agricultural Production in Tamil Nadu, India

Analyzing the spatial and temporal trends in cropping patterns and intensity on a larger scale is essential for implementing timely policy decisions and strategies in response to climate change and variability. By converting cropping intensity estimates, we can compute net and gross production values, indirectly indicating food security status in the study region. This study compared the utility of optical (MOD13Q1) and SAR (Sentinel 1A) datasets for determining cropping patterns and associated intensity estimates across multiple agricultural seasons from 2019 to 2023, with spatial resolutions of 250 m and 20 m, respectively. The analysis revealed that the highest and lowest gross cropped areas using Sentinel 1A data were 55.85 lakh hectares (2022–2023) and 52.88 lakh hectares (2019–2020), respectively. For MODIS data, the highest and lowest gross cropped areas were 62.07 lakh hectares (2022–2023) and 56.87 lakh hectares (2019–2020). Similarly, the highest and lowest net sown areas using Sentinel 1A data were 43.71 lakh hectares (2022–2023) and 41.76 lakh hectares (2019–2020), and for MODIS data, the values were 48.81 lakh hectares (2022–2023) and 46.39 lakh hectares (2019–2020), respectively. Regardless of the datasets used, the highest gross and net cropped areas were reported in Tiruvannamalai district and the lowest in Kanchipuram district. Thiruvarur district reported the highest cropping intensity, while Sivagangai district had the lowest. Among all seasons, the rabi season accounted for the maximum area, followed by the kharif and summer seasons. The study concluded that single cropping (51%) was the dominant cropping pattern in Tamil Nadu, followed by double cropping (31%) and triple cropping (17%) in both datasets. Sentinel 1A data showed better performance in estimating gross and net cropped areas than optical data, with deviations ranging from 7.02% to 11.01%, regardless of the year and cropping estimates derived. The results indicated that the spatial resolution of the datasets was not a significant factor in determining cropping patterns and intensity on a larger scale. However, this may differ for smaller study areas.

Mapping Boro Rice Cultivation in Bangladesh Using Multi-Temporal MODIS Data and Phenological Approach

Mapping Boro Rice Cultivation in Bangladesh Using Multi-Temporal MODIS Data and Phenological Approach

Spatiotemporal patterns and driving forces of net primary productivity in South and Southeast Asia based on Google Earth Engine and MODIS data

Spatiotemporal patterns and driving forces of net primary productivity in South and Southeast Asia based on Google Earth Engine and MODIS data

Satellite Remote Sensing Reveals More Beneficial Effect of Forest Plant Diversity on Drought Resistance in More Arid Areas of Yunnan, China.

Plant diversity is important in enhancing an ecosystem's drought resistance. However, the relationship between plant diversity and drought resistance has historically aroused much controversy. Given that most previous studies on the relationship were conducted with insitu data at a small or point scale, this study explored the relationship with satellite remote sensing, taking Yunnan Province of China as the study area. Specifically, Sentinel-2 remote sensing data were used to estimate plant diversity. The temporal correlation between the standardized vegetation index (SVI) and standardized precipitation evapotranspiration index (SPEI) was used to express the vegetation sensitivity to drought or drought resistance. A moving window method was developed to explore the relationship between plant diversity and drought resistance. MODIS and SPEI data from 2000 to 2018, as well as Meteorological reanalysis data from 1990 to 2020, were utilized. Results indicated that (1) the remotely sensed plant diversity index was found significantly correlated with field investigations of plant diversity in the study area, with a correlation coefficient of around 0.43-0.49 and p-value < 0.001. The plant diversity of the study area can be recognized with the Sentinel-2 remote sensing data. (2) The area proportion of having positive temporal correlation coefficients between SVIs and one-month SPEI varied around 60%-88% during the dry seasons, while that varied around 30%-50% during the wet seasons. Most of the forest vegetation in the study area was sensitive to drought in the dry seasons rather than in the wet seasons. (3) About 80% of the study area presented a beneficial effect of plant diversity on drought resistance, that is, the higher the plant diversity, the lower the forest sensitivity to drought. (4) The beneficial effect of plant diversity has different manifestations in different regions, and it was stronger in more arid and drought-prone areas.