Moderate Resolution Imaging Spectroradiometer Research Articles

Assessing Aerosol Changes over Delhi using Satellite and Ground Measurement Data: Insights from a COVID-19 Lockdown Period

The tragic COVID-19 pandemic, while presenting numerous devastating consequences, has inadvertently provided a unique opportunity for studying air pollution. In this study, we specifically evaluate the spatiotemporal changes in aerosols before and during the COVID-19 lockdown from March to May 2020 over Northwest India, with a particular focus on two subregions in the vicinity of Delhi: Delhi-West and Delhi-East. The assessment was conducted using aerosol optical depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) mission, aerosol profiles from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission, and ground-based measurements of PM2.5 and PM10. Results demonstrated evident reductions in surface particulate matter and AOD during the lockdown. Approximately 40% of the contribution to the total AOD was from aerosols below 1 km. Different rates of change in AOD were observed for the two subregions across different lockdown phases, attributed to differences in emission sources: Delhi-East is more influenced by residential emissions, while Delhi-West is more affected by natural sources. The surface concentration and variabilities of PM2.5 and PM10 confirmed the differences between the two subregions, emphasizing the role of anthropogenic activities in PM2.5 emissions over the study area.

Application of the Reconstructed Solar-Induced Chlorophyll Fluorescence by Machine Learning in Agricultural Drought Monitoring of Henan Province, China from 2010 to 2022

Solar-induced chlorophyll fluorescence (SIF) serves as a proxy indicator for vegetation photosynthesis and can directly reflect the growth status of vegetation. Using SIF for drought monitoring offers greater potential compared to traditional vegetation indices. This study aims to develop and validate a novel approach, the improved Temperature Fluorescence Dryness Index (iTFDI), for more accurate drought monitoring in Henan Province, China. However, the low spatial resolution, data dispersion, and short temporal sequence of SIF data hinder its direct application in drought studies. To overcome these challenges, this study constructs a random forest SIF downscaling model based on the TROPOspheric Monitoring Instrument SIF (TROPOSIF) and the Moderate-resolution Imaging Spectroradiometer (MODIS) data. Assuming an unchanging spatial scale relationship, an improved SIF (iSIF) product with a temporal resolution of 500 m over the period March to September, 2010–2022 was obtained for Henan Province. Subsequently, using the retrieved iSIF and the surface temperature difference data, the iTFDI was proposed, based on the assumption that under the same vegetation cover conditions, lower soil moisture and a greater diurnal temperature range of the surface indicate more severe drought. Results showed that: (1) The accuracy of the TROPOSIF downscaling model achieved coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) values of 0.847, 0.073 mW m−2 nm−1 sr−1, and 0.096 mW m−2 nm−1 sr−1, respectively. (2) The 2022 iTFDI drought monitoring results indicated favorable soil moisture in Henan Province during March, April, July, and August, while extensive droughts occurred in May, June, and September, accounting for 70.27%, 71.49%, and 43.61%, respectively. The monitored results were consistent with the regional water conditions measured at ground stations. (3) The correlation between the Standardized Precipitation Evapotranspiration Index (SPEI) and iTFDI at five stations was significantly stronger than the correlation with the Temperature Vegetation Dryness Index (TVDI), with the values −0.631, −0.565, −0.612, −0.653, and −0.453, respectively. (4) The annual Sen’s slope and Mann–Kendall significance test revealed a significant decreasing trend in drought severity in the southern and western regions of Henan Province (6.74% of the total area), while the eastern region showed a significant increasing trend (4.69% of the total area). These results demonstrate that the iTFDI offers a significant advantage over traditional indices, providing a more accurate reflection of regional drought conditions. This enhances the ability to identify drought trends and supports the development of targeted drought management strategies. In conclusion, the iTFDI constructed using the downscaled iSIF data and surface temperature differential data shows great potential for drought monitoring.

Wavelet-fusion image super-resolution model with deep learning for downscaling remotely-sensed, multi-band spectral albedo imagery

Generating granular-scale surface albedo data is extremely important for solar photovoltaic site planning and to optimise renewable energy yield of bifacial panel installations. The albedo effect brings about a significant increase in power in bifacial photovoltaic systems, compared to their mono-facial counterparts, since the spectral response of bifacial solar panels correlates with the incident solar radiation wavelength on the back of the panel, to provide additional power generation capacity. Thus, harnessing the albedo data at relatively local scales is critical towards boosting solar power generation and providing greater power density in local electricity grids. This paper develops novel modelling approaches to produce high-resolution spectral albedo imagery across the Visible and Near Infrared (VNIR) bands, using the Wavelet-Fusion super-resolution model (i.e., Wavelet-FusionSR) trained with the Learned Gamma Correction approach by applying satellite image enhancement methodology. The proposed Wavelet-FusionSR model utilises the low-resolution moderate-resolution Imaging Spectroradiometer (MODIS) as well as high-resolution multi-spectral Advanced Space-borne Thermal Emission Reflection Radiometer (ASTER) satellite images, as critical inputs and ground-truth imagery, respectively, in order to perform sensor-to-sensor deep downscaling, without employing any synthetic or low-resolution satellite imagery data pairs. To augment the proposed deep learning algorithm across the decomposed sub-images of low-resolution inputs, we integrate local and global feature representation learning to train the proposed Wavelet-FusionSR model with Cauchy loss functions. In comparison with five competing benchmark models, the proposed Wavelet-FusionSR model demonstrates performance superiority using quantitative image downscaling metrics and visual assessments of the downscaled images for the visible band of solar radiation. The proposed Wavelet-FusionSR model yielded a Mean Square Error (MSE) of 0.00017, Signal-to-noise-ratio (PSNR) of 37.80, Structural Similarity Index (SSIM) of 0.999 and combined loss, MS-SSIMLoss, based on Multi Structural Similarity and Mean Absolute Error of 2.354 for the Visible Band images, and an MSE of 0.0014, PSNR of 28.43, SSIM of 0.999 and MS-SSIMLoss of 7.426 for the NIR spectral bands, demonstrating high efficacy of the proposed Wavelet-FusionSR method. The Wavelet-FusionSR method therefore attains high-resolution spectral albedo imagery outputs.

Generation of global 1 km all-weather instantaneous and daily mean land surface temperatures from MODIS data

Abstract. Land surface temperature (LST) serves as a crucial variable in characterizing climatological, agricultural, ecological, and hydrological processes. Thermal infrared (TIR) remote sensing provides high temporal and spatial resolutions for obtaining LST information. Nevertheless, TIR-based satellite LST products frequently exhibit missing values due to cloud interference. Prior research on estimating all-weather instantaneous LST has predominantly concentrated on regional or continental scales. This study involved generating a global all-weather instantaneous and daily mean LST product spanning from 2000 to 2020 using XGBoost. Multisource data, including Moderate-Resolution Imaging Spectroradiometer (MODIS) top-of-atmosphere (TOA) observations, surface radiation products, and reanalysis data, were employed. Validation using an independent dataset of 77 individual stations demonstrated the high accuracy of our products, yielding root mean squared errors (RMSEs) of 2.787 K (instantaneous) and 2.175 K (daily). The RMSE for clear-sky conditions was 2.614 K for the instantaneous product, which is slightly lower than the cloudy-sky RMSE of 2.931 K. Our instantaneous and daily mean LST products exhibit higher accuracy compared to the MODIS official LST product (instantaneous RMSE = 3.583 K; daily 3.105 K) and the land component of the fifth generation of the European ReAnalysis (ERA5-Land) LST product (instantaneous RMSE = 4.048 K; daily 2.988 K). Significant improvements are observed in our LST product, notably at high latitudes, compared to the official MODIS LST product. The LST dataset from 2000 to 2020 at the monthly scale, the daily mean LST on the first day of 2010 can be freely downloaded from https://doi.org/10.5281/zenodo.4292068 (Li et al., 2024), and the complete product will be available at https://glass-product.bnu.edu.cn/ (last access: 22 August 2024).

The Influences of Indian Monsoon Phases on Aerosol Distribution and Composition over India

This study investigates the impacts of summer monsoon activity on aerosols over the Indian region. We analyze the variability of aerosols during active and break monsoon phases, as well as strong and weak monsoon years, using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Our findings show a clear distinction in aerosol distribution between active and break phases. During active phases, the Aerosol Optical Depth (AOD) and aerosol extinction are lower across the Indian region, while break phases are associated with higher AOD and extinction. Furthermore, we observed a significant increase in AOD over Central India during strong monsoon years, compared to weak monsoon years. Utilizing the vertical feature mask (VFM) data from CALIPSO, we identified polluted dust and dusty marine aerosols as the dominant types during both active/break phases and strong/weak monsoon years. Notably, the contributions of these pollutants are significantly higher during break phases compared to during active phases. Our analysis also reveals a shift in the origin of these aerosol masses. During active phases, the majority originate from the Arabian Sea; in contrast, break phases are associated with a higher contribution from the African region.

Changes in Land Use and Land Cover, Normalized Difference Vegetation Index and Land Surface Temperature in the Narsingdi District During 2001 to 2021

Rapid urbanization and industrialization cause land use changes, reduce green spaces, and increase the land surface temperature. Green spaces of a city area maintain environmental quality by absorbing air pollutants and reducing land surface temperature. The present study aimed to detect the changes in land use and land cover (LULC), normalized difference vegetation index (NDVI), and land surface temperature (LST) in the Narsingdi district including six Upazilas from 2001 to 2021. The Landsat 7- Enhanced Thematic Mapper Plus (ETM+), the Landsat 8- Operational Land Imager (OLI) imageries and the Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to analyze the LULC, NDVI, and LST by using Google Earth Engine (GEE) and ArcGIS 10.8.2. The images were analyzed into four land use classifications – agricultural land, built-up area, forest vegetation, and water body. Among all the Upazilas, Narsingdi Sadar was the vulnerable area, where agricultural land, forest coverage, and water body decreased significantly by 6.67%, 2.2%, and 4.66%, respectively. The built-up area increased by a considerable amount of 13.53% during the last twenty years. The forest coverage of Narsingdi Sadar Upazila was calculated at only 8.91% in 2021 and decreased from 11.11% in 2001. The NDVI values surprisingly increased in all the Upazilas, but in Narsingdi Sadar, it is comparatively low (0.08) than in other Upazilas. The increasing trend in LST in the Narsingdi Sadar Upazila is alarming, 1.14oC from 2001 to 2021 (0.57oC/decade). The significant changes in LULC, NDVI, and LST made the Narsingdi Sadar a more critical area in the Narsingdi district. The findings of the study will be helpful to policymakers in making appropriate decisions in future city development. J. of Sci. and Tech. Res. 5(1): 93-118, 2023

The impact of land-use change on the ecological environment quality from the perspective of production-living-ecological space: A case study of the northern slope of Tianshan Mountains

Elucidating the relationships between production-living-ecological space (PLES) and ecological environments can help identify new strategies to promote sustainable development. The northern slope of the Tianshan Mountains represents a typical complex ecosystem characterized by mountain-oasis-desert interactions in arid regions with prominent human-land conflicts and intricate ecological environments. Here, we analyzed the spatiotemporal variations in land use in the study area from the perspective of PLES. Based on moderate resolution imaging spectroradiometer (MODIS) data, an improved remote sensing ecological index (IRSEI) suitable for arid areas was established to evaluate the eco-environment quality (EEQ) by introducing salinity and cleanliness indexes. A stepwise regression model was then used to identify and quantify the impact of the PLES transfer modes on EEQ. The results showed that: (1) The oasis area was dominated by agricultural production land, whereas the desert and alpine areas were dominated by other and pasture ecological lands, respectively. The oasis area primarily involved the conversion of pasture ecological land to agricultural production land, whereas the desert and alpine areas showed mutual transformation of ecological space. (2) The size relationship of the EEQ in each sub-region was alpine region (0.555) > oasis region (0.509) > desert region (0.424). The EEQ in the study area showed an overall upward trend but decreased in the alpine region. (3) Changes in the PLES and IRSEI primarily occurred in the oasis region. Among the 21 PLES transfer modes that affect EEQ, an increase in agricultural production land was the primary mode that enhanced IRSEI, whereas an increase in impervious surfaces significantly decreased IRSEI. The degree of influence was as follows: industrial and mining production (−0.594) > urban living (−0.462) > rural living land (−0.316). In addition, EEQ is also affected by the combined effects of climate, topography, and other factors. By proposing IRSEI, we highlight the impact of land-use transfer mode on ecological environment quality from the perspective of PLES, which can provide a reference for ecological environment evaluation in arid areas and is of great significance for land-use planning and ecological environment protection.

A universal triangle method for evapotranspiration estimation with MODIS products and routine meteorological observations: Algorithm development and global validation

The surface temperature (Ts) and vegetation index (VI) triangle method is one of the most famous and widely applied methods for regional evapotranspiration (ET) estimation. However, no robust validation has been performed yet to evaluate its accuracy on a global scale, making its accuracy unclear when compared with other global ET models. The main reason behind it is that most traditional triangle models depend on the Ts-VI feature space within a certain spatial domain to parameterize their boundary conditions. The domain dependency makes it impossible to derive universal boundaries for continental or global application. Under this background, this study presented a universal triangle method for ET estimation with Moderate Resolution Imaging Spectroradiometer (MODIS) products and routine meteorological observations. The solution for boundary conditions was performed pixel by pixel without the consideration of spatial domain based configuration. Specifically, the dry boundary was solved theoretically based on the surface energy balance equation, and the wet boundary was substituted by wet-bulb temperature estimated from dew point temperature and air temperature. The validation against ground observations collected from 33 flux towers worldwide shows that this universal triangle method achieved evaporative fraction (EF) estimation with the correlation coefficient of 0.65, the mean absolute error of 0.17, the root-mean-square-error of 0.20, and the bias of 0.13. These four metrics of daily ET estimation were 0.83, 0.71 mm/d, 0.90 mm/d, and 0.41 mm/d, respectively. This analysis is the first comprehensive validation of the triangle method on a global scale. The comparison with early work suggests that our universal triangle method on a global scale has reached a comparable accuracy to traditional triangle models at regional scale. However, the universal triangle method has the capacity to monitor ET continuously pixel by pixel without domain dependency. This is just what most traditional triangle methods do not possess.

Substantial Underestimation of Fine-Mode Aerosol Loading from Wildfires and Its Radiative Effects in Current Satellite-Based Retrievals over the United States.

Wildfires generate abundant smoke primarily composed of fine-mode aerosols. However, accurately measuring the fine-mode aerosol optical depth (fAOD) is highly uncertain in most existing satellite-based aerosol products. Deep learning offers promise for inferring fAOD, but little has been done using multiangle satellite data. We developed an innovative angle-dependent deep-learning model (ADLM) that accounts for angular diversity in dual-angle observations. The model captures aerosol properties observed from dual angles in the contiguous United States and explores the potential of Greenhouse gases Observing Satellite-2's (GOSAT-2) measurements to retrieve fAOD at a 460 m spatial resolution. The ADLM demonstrates a strong performance through rigorous validation against ground-based data, revealing small biases. By comparison, the official fAOD product from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the Multiangle Imaging Spectroradiometer (MISR) during wildfire events is underestimated by more than 40% over western USA. This leads to significant differences in estimates of aerosol radiative forcing (ARF) from wildfires. The ADLM shows more than 20% stronger ARF than the MODIS, VIIRS, and MISR estimates, highlighting a greater impact of wildfire fAOD on Earth's energy balance.

Atmospheric pollutants in Rosario, Argentina analysed through remote sensing: Wildfires and COVID-19 lockdown effects

Spatial-temporal dynamics of atmospheric pollutants can be analysed by space-based observations, contributing to environmental management and public health interventions. The influence of wildfires and anthropogenic activities on air quality is studied for the city of Rosario and its surroundings, including both urban and non-urban areas. Utilizing advanced satellite-based observations, we assess Aerosol Optical Depth (AOD) and Nitrogen Dioxide (NO2). The study employs Multi-Angle Implementation of Atmospheric Correction (MAIAC) and Moderate Resolution Imaging Spectroradiometer (MODIS) onboard NASA's Terra and Aqua satellites for AOD analysis. NO2 measurements were derived from the Ozone Monitoring Instrument (OMI) on the Aura satellite. Backward trajectory analysis using Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) shows the connection between fire pixels and air masses reaching Rosario. During the COVID-19 lockdown, a period with no significant fire events in the studied region was selected. This provided, for the first time, a baseline on the AOD median value of 0.05 for Rosario city and its surroundings, while NO2 total column median values were close to 3.00 x 1015 molecules/cm2. In a business-as-usual scenario, AOD increased by approximately 52.8% (March 2022) up to 111.3% (March 2019). NO2 median values remain almost the same (March 2019) or reached a median value of 3.38 x 1015 molecules/cm2 (March 2022). During wildfire events, such as March 2023, AOD surged by around 50.9%–108.6% compared to the business-as-usual scenario (March 2019 and March 2022, respectively). NO2 median values ranged from 3.52 x 1015 molecules/cm2 (March 2023) to 3.66 x 1015 molecules/cm (March 01, 2020 to March 19, 2020). NO2 levels correlated with intense fire periods. The analysis provides valuable insights into the complex interplay between natural events, human activities, and air quality dynamics in the region.

Early polar-orbiting satellite-based fire remote sensing during the 1960s

ABSTRACT Satellite-based sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) now routinely provide a detailed picture of global fire activity on a daily basis. These systems explicitly included biomass burning as a phenomenon of interest, and their design was heavily influenced by experience gained with the comparatively primitive Advanced Very High Resolution Radiometer (AVHRR) first deployed on the TIROS-N satellite in the late 1970s. The AVHRR is generally recognized as the earliest spaceborne sensor to provide a middle infrared (MWIR) channel sensitive to actively burning vegetation fires and other high-temperature, sub-pixel thermal anomalies, forming the basis of countless fire studies before higher quality MODIS data became available. Here, we consider in the context of active fire monitoring a sensor much older than the AVHRR, namely the High Resolution Infrared Radiometer (HRIR) on board NASA’s Nimbus-I, -II, and -III satellites, launched in 1964, 1966, and 1969, respectively. This early imaging sensor had a single MWIR channel primarily intended for night-time meteorological observations (a complementary daytime near-infrared channel was added for Nimbus-III) that could potentially serve as a source of pre-AVHRR active fire data. To this end, we inspected night-time HRIR imagery acquired over the western United States and found unambiguous evidence of multiple confirmed wildfires in 1966. In addition, we found strong evidence of actively burning fires in Africa, specifically the Democratic Republic of the Congo, western Zambia, and northern Namibia, and in northern Australia, commensurate with their fire seasons. Our findings indicate that it was actually the HRIR that acquired the earliest MWIR imagery that could be used for identifying biomass burning from space, well over a decade before the first AVHRR was deployed. We found in addition that the retrieval of fire radiative power (FRP) from higher dynamic-range HRIR observations was potentially possible pending refined calibration.

Heatwave vulnerability of large metropolitans in Bangladesh: An evaluation

Heatwaves pose a significant risk to human society, and assessing vulnerability at the local scale is challenging due to the multifaceted nature of contributing factors. This study focuses on evaluating the heatwave vulnerability of five major cities in Bangladesh: Chittagong, Dhaka, Khulna, Rajshahi, and Sylhet. Heatwave vulnerability index (HVI) was estimated, integrating exposure, sensitivity, and adaptive capacity components. Time-series data from the Moderate Resolution Imaging Spectroradiometer (MODIS) for land surface temperature (LST) spanning 2000–2019 served as the primary dataset. The HVI calculation employed a principal component analysis method. The spatial distribution of the resulting HVI across five metropolitan areas were evaluated by estimating spatial autocorrelation and identifying clusters and hot spots in vulnerable areas. The findings revealed that urban centers characterized by extensive built-up areas exhibited higher exposure to urban heat compared to their surroundings. Dhaka, the capital, recorded the highest mean annual LST, while Sylhet registered the lowest. Rajshahi and Sylhet stood out for having the largest percentage of heat-exposed and sensitive areas, respectively. Sylhet also had the highest percentage of heat-vulnerable areas, reaching 63 %, whereas Dhaka had the highest number of vulnerable individuals, totaling 12.5 million. The findings also suggested that augmenting urban green and blue infrastructures holds potential for mitigating the adverse impacts of heatwaves. The methodology and outcomes of this study provide a valuable foundation for devising heatwave adaptation strategies not only in Bangladesh but also in other regions facing similar challenges.

Assessing forest fire dynamics and risk zones in Central Indian forests: a comparative study of the Khandwa and North Betul forest divisions of Madhya Pradesh.

Forest fires pose significant environmental and socioeconomic threats, particularly in regions such as Central India, where forest ecosystems are vital for biodiversity and local livelihoods. Understanding forest fire dynamics and identifying fire risk zones are crucial for effective mitigation. The current study explores the spatiotemporal dynamics of forest fires in the Khandwa and North Betul forest divisions in the Central Indian region over 22years using Mann-Kendall and Sen's slope tests on MODIS (Moderate Resolution Imaging Spectroradiometer) fire point data. We found a nonsignificant increase in forest fires in both divisions. Khandwa showed a nonsignificant slope rise of more than three events per year, while North Betul revealed an increase of around one event per year. The lack of statistical significance suggests that upward trends of forest fire events may result from random fluctuations rather than consistent patterns. Spatial autocorrelation analysis revealed significant clustering of fire incidents in both regions. Khandwa confirmed moderate clustering (Moran's I = 0.043), whereas North Betul showed robust clustering (Moran's I = 0.096). Kernel density estimation further identified high-risk clusters in both divisions, necessitating zonal-wise targeted fire management strategies. Fire risk zonation was developed using the analytic hierarchy process (AHP), combining 10 environmental and socioeconomic factors. The AHP model, validated using MODIS fire data, showed reliable accuracy. The results revealed many of both divisions in the high- to very high-risk categories. Approximately, 45% of the area of the Khandwa and nearly 50% of the area of North Betul fall under high to very high fire risk zones. Khandwa's high-risk areas mainly lie in the northern and southeastern parts, while North Betul lies in the northwestern and north-eastern regions. The identified fire-prone areas indicate the pressing need for local or region-specific fire prevention and mitigation strategies. Thus, the findings of this study provide valuable insights into forest fire risk management and contribute to more focused research and methodological developments.

Utilizing the Google Earth Engine for Agricultural Drought Conditions and Hazard Assessment Using Drought Indices in the Najd Region, Sultanate of Oman

Accurately evaluating drought and its effects on the natural environment is difficult in regions with limited climate monitoring stations, particularly in the hyper-arid region of the Sultanate of Oman. Rising global temperatures and increasing incidences of insufficient precipitation have turned drought into a major natural disaster worldwide. In Oman, drought constitutes a major threat to food security. In this study, drought indices (DIs), such as temperature condition index (TCI), vegetation condition index (VCI), and vegetation health index (VHI), which integrate data on drought streamflow, were applied using moderate resolution imaging spectroradiometer (MODIS) data and the Google Earth Engine (GEE) platform to monitor agricultural drought and assess the drought risks using the drought hazard index (DHI) during the period of 2001–2023. This approach allowed us to explore the spatial and temporal complexities of drought patterns in the Najd region. As a result, the detailed analysis of the TCI values exhibited temporal variations over the study period, with notable minimum values observed in specific years (2001, 2005, 2009, 2010, 2014, 2015, 2016, 2017, 2019, 2020, and 2021), and there was a discernible trend of increasing temperatures from 2014 to 2023 compared to earlier years. According to the VCI index, several years, including 2001, 2003, 2006, 2008, 2009, 2013, 2015, 2016, 2017, 2018, 2020, 2021, 2022, and 2023, were characterized by mild drought conditions. Except for 2005 and 2007, all studied years were classified as moderate drought years based on the VHI index. The Pearson correlation coefficient analysis (PCA) was utilized to observe the correlation between DIs, and a high positive correlation between VHI and VCI (0.829, p < 0.01) was found. Based on DHI index spatial analysis, the northern regions of the study area faced the most severe drought hazards, with severity gradually diminishing towards the south and east, and approximately 44% of the total area fell under moderate drought risk, while the remaining 56% was classified as facing very severe drought risk. This study emphasizes the importance of continued monitoring, proactive measures, and effective adaptation strategies to address the heightened risk of drought and its impacts on local ecosystems and communities.

Disentangling the Spatiotemporal Dynamics, Drivers, and Recovery of NPP in Co-Seismic Landslides: A Case Study of the 2017 Jiuzhaigou Earthquake, China

The 2017 Jiuzhaigou earthquake, registering a magnitude of 7.0, triggered a series of devastating geohazards, including landslides, collapses, and mudslides within the Jiuzhaigou World Natural Heritage Site. These destructive events obliterated extensive tracts of vegetation, severely compromising carbon storage in the terrestrial ecosystems. Net Primary Productivity (NPP) reflects the capacity of vegetation to absorb carbon dioxide. Accurately assessing changes in NPP is crucial for unveiling the recovery of terrestrial ecosystem carbon storage after the earthquake. To this end, we designed this study using the Moderate Resolution Imaging Spectroradiometer (MODIS) Net Primary Productivity datasets. The findings are as follows. NPP in the co-seismic landslide areas remained stable between 525 and 575 g C/m2 before the earthquake and decreased to 533 g C/m2 after the earthquake. This decline continued, reaching 483 g C/m2 due to extreme rainfall events in 2018, 2019, and 2020. Recovery commenced in 2021, and by 2022, NPP had rebounded to 544 g C/m2. The study of NPP recovery rate revealed that, five years after the earthquake, only 18.88% of the co-seismic landslide areas exhibited an NPP exceeding the pre-earthquake state. However, 17.14% of these areas had an NPP recovery rate of less than 10%, indicating that recovery has barely begun in most areas. The factor detector revealed that temperature, precipitation, and elevation significantly influenced NPP recovery. Meanwhile, the interaction detector highlighted that lithology, slope, and aspect also played crucial roles when interacting with other factors. Therefore, the recovery of NPP is not determined by a single factor, but rather by the interactions among various factors. The ecosystem resilience study demonstrated that the current recovery of NPP primarily stems from the restoration of grassland ecosystems. Overall, while the potential for NPP recovery in co-seismic landslide areas is optimistic, it will require a considerable amount of time to return to the pre-earthquake state.

Advancing Volcanic Activity Monitoring: A Near-Real-Time Approach with Remote Sensing Data Fusion for Radiative Power Estimation

The global, near-real-time monitoring of volcano thermal activity has become feasible through thermal infrared sensors on various satellite platforms, which enable accurate estimations of volcanic emissions. Specifically, these sensors facilitate reliable estimation of Volcanic Radiative Power (VRP), representing the heat radiated during volcanic activity. A critical factor influencing VRP estimates is the identification of hotspots in satellite imagery, typically based on intensity. Different satellite sensors employ unique algorithms due to their distinct characteristics. Integrating data from multiple satellite sources, each with different spatial and spectral resolutions, offers a more comprehensive analysis than using individual data sources alone. We introduce an innovative Remote Sensing Data Fusion (RSDF) algorithm, developed within a Cloud Computing environment that provides scalable, on-demand computing resources and services via the internet, to monitor VRP locally using data from various multispectral satellite sensors: the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS), the Sea and Land Surface Temperature Radiometer (SLSTR), and the Visible Infrared Imaging Radiometer Suite (VIIRS), along with the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI). We describe and demonstrate the operation of this algorithm through the analysis of recent eruptive activities at the Etna and Stromboli volcanoes. The RSDF algorithm, leveraging both spatial and intensity features, demonstrates heightened sensitivity in detecting high-temperature volcanic features, thereby improving VRP monitoring compared to conventional pre-processed products available online. The overall accuracy increased significantly, with the omission rate dropping from 75.5% to 3.7% and the false detection rate decreasing from 11.0% to 4.3%. The proposed multi-sensor approach markedly enhances the ability to monitor and analyze volcanic activity.

Evaluating MODIS cloud-free snow cover datasets using massive spatial benchmark data in the Tibetan Plateau

Accurate snow cover data is crucial for understanding climate change, managing water resources, and calibrating models. The MODIS (Moderate-resolution Imaging Spectroradiometer) and its cloud-free snow cover datasets are widely used, but they have not been systematically evaluated due to different benchmark data and evaluation parameters. Conventional methods using station observations as a ground truth suffer from underrepresentation and mismatches in temporal and spatial scales. This study established a scale-matched spatial benchmark dataset, compiling from 18,433 Landsat series and 11,172 Sentinel-2 images over two decades, totaling ∼1.86 billion samples and ∼320 million snow samples. We evaluated seven MODIS cloud-free snow cover datasets for seasons, elevation zones, land covers and subregions using this benchmark data. For the clear-sky part, NIEER_MODIS_SCE (MODIS snow cover extent product over China) performs best due to its use of optimal NDSI thresholds suitable for each land use type. This highlights the importance of regional customization in snow mapping algorithms, and it can be further improved in spring, forests and zone 1 by combining it with M*D10A1GL06. For the cloud removed part, one-step integrated spatiotemporal cloud removal datasets perform better than any other approach does. The second-best dataset is obtained from a simple but effective single temporal cloud removal method using nearby time information. For the whole dataset, the best NIEER_MODIS_SCE has an overall accuracy of 0.82 and snow retrieval accuracy of 84.56 %. It performs excellently in most settings but weakest in forests thus requiring more efficient strategies. This research provides new perspectives and methods for objectively assessing MODIS snow cover products and other relevant datasets. These methods can be readily extended to other regions and adapted to future satellite missions. And such findings may guide the selection of more valid snow cover data and the developing of even better snow detecting strategies.

Temporal Patterns of Vegetation Greenness for the Main Forest-Forming Tree Species in the European Temperate Zone

In light of recently accelerating global warming, the changes in vegetation trends are vital for the monitoring of the dynamics of both whole ecosystems and individual species. Detecting changes within the time series of specific forest ecosystems or species is very important in the context of assessing their vulnerability to climate change and other negative phenomena. Hence, the aim of this paper was to identify the trend change points and periods of greening and browning in multi-annual time series of the normalised difference vegetation index (NDVI) and enhanced vegetation index (EVI) of four main forest-forming tree species in the temperate zone: pine, spruce, oak and beech. The research was conducted over the last two decades (2002–2022), and was based on vegetation indices data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). To this end, several research approaches, including calculating the linear trends in the moving periods and BEAST algorithm, were adapted. A pattern of browning then greening then constant was detected for coniferous species, mostly pine. In turn, for broadleaved species, namely oak and beech, a pattern of greening then constant was identified, without the initial phase of browning. The main trend change points seem to be ca. 2006 and ca. 2015 for coniferous species and solely around 2015 for deciduous ones.

Assessing uncertainties of a remote sensing-based discharge reflectance model for applications to large rivers of the Congo Basin

ABSTRACT Adequate monitoring of river discharge is crucial for effective water resource management. However, this objective remains difficult to achieve in the context of large and ungauged river basins. This study assesses the performance of remote sensing applications for discharge monitoring in the lower reach of the Congo River, where daily discharge information is required to support many water resource operations. The approach is based on the use of Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing imagery to produce a daily time series of a ratio of reflectance values (C/M) for discharge monitoring. The validation of the approach is performed based on three-year water level data collected at the outlet gauging site and limited in situ Acoustic Doppler Current Profiler (ADCP) cross-section measurements for high and low flow seasons. The simulated discharge closely matches the observed values and falls within acceptable ranges, with errors below 10% and Nash-Sutcliffe coefficients ranging from 0.65 to 0.76 for ADCP and gauging station, respectively.

A Data‐Driven Approach to Assess the Impact of Climate Change on a Tropical Mangrove in India

AbstractAs a potential carbon sink, mangroves play an important role in climate mitigation. India houses several major global mangrove patches, which remain vulnerable to climate change. The ecosystem‐atmosphere CO2 exchange is most accurately measured by the eddy covariance method, whereas satellites provide the biophysical parameters for a wider area. In the present study, the Sentinel‐2 satellite data is used to map the land cover types in the Pichavaram mangrove forest and identify two major dominant species (Rhizophora spp. and Avicennia marina), which indicated more than 95% classification accuracy. We used 2 years (2017 and 2018) of in situ gross primary productivity (GPP) and leaf area index (LAI) measurements and rectified the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP and LAI products from 2010 to 2018. The modified MODIS GPP and LAI products were used to develop machine learning models, that is, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) to study the climate influence on mangrove productivity. The RF model (R2 = 0.85 and root mean square error (RMSE) = 0.2) outperformed the XGBoost model (R2 = 0.75 and RMSE = 0.26) and was used to project the impact of climate change on the mangrove GPP for two extreme climate change scenarios, namely SSP1‐1.26 and SSP5‐8.5. The GPP increases and decreases in future during wet and dry periods, respectively. Overall, the projected GPP indicated a reduction of 3.73%–20.3% from 2050 to 2060 and of 4.82%–28.15% from 2090 to 2100, compared to its current average (from 2010 to 2018).