Moderate Resolution Imaging Spectroradiometer Satellite Research Articles

Time series prediction of aerosol optical depth across the northern Indian region: integrating PSO-optimized SARIMA-SVR based on MODIS data

AbstractAccurately predicting aerosol optical depth (AOD), a key parameter for characterizing atmospheric aerosols, is essential due to the increasing prevalence of air pollution and its detrimental effects. From existing literature on AOD time-series prediction, linear models like seasonal autoregressive integrated moving average (SARIMA) are commonly used, while nonlinear models such as machine learning (ML) and deep learning (DL) have gained popularity recently for their ability to handle nonlinear patterns. This study introduces a hybrid model, particle swarm optimization-seasonal autoregressive integrated moving average-support vector regression (PSOSARIMA-PSOSVR), which integrates linear and nonlinear modeling through residual modeling to significantly enhance AOD prediction accuracy. By combining these approaches, we aim to better handle the nonlinearities and overall variability in AOD data, leading to more accurate predictions. To address hyperparameter tuning challenges, including the risks of model misspecification and overfitting or underfitting, PSO is utilized for optimization. PSO’s evolutionary optimization ensures efficient tuning for optimal model performance. Monthly AOD data sourced from the moderate resolution imaging spectroradiometer (MODIS) satellite covering the northern Indian region from 2001 to 2019 is used for experiments. Performance metrics such as root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination ($$R^2$$ R 2 ), Nash-Sutcliffe efficiency (NSE), and root mean square error ratio (RSR) are employed to evaluate model accuracy and dependability. The results of this study illustrate that the proposed PSOSARIMA-PSOSVR model outperforms both standalone PSOSARIMA and PSOSVR models. Moreover, it consistently surpasses SARIMA, SVR, and long short-term memory (LSTM) in AOD prediction, effectively addressing non-stationarity and variability challenges in AOD data. This study suggests the hybrid model as a promising approach for improving the accuracy of AOD prediction.

Remote sensing-based agricultural drought mapping in Northern Jordan using Landsat and MODIS data

Monitoring agricultural drought in a semi-arid environment is critical, especially during the growing season, as it negatively impacts vegetation health and crop yield. This study aimed to monitor the spatiotemporal variation of agricultural drought in Northern Jordan using Landsat-8 and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. The Spatio-Temporal Image Fusion Model (STI-FM) was used to produce synthetic Landsat images with a high spatiotemporal resolution (30 m / 8 days) by utilizing a pair of successive MODIS images at two points in time (time-1 and time-2) and one Landsat-8 image at time-1. Agricultural drought was mapped and monitored using spectral indices namely, Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI). Additionally, the Standard Precipitation Index (SPI) -based meteorological rainfall data was used to validate the accuracy of the drought maps. The results revealed significant spatiotemporal variations in drought conditions, with April 2020 showing the least dry conditions, while 2019 was identified as the driest year. Validation through SPI indicated high accuracy, with kappa values ranging from 0.70 to 0.85 and overall accuracy ranging from 80 % to 92 %. Furthermore, the STI-FM data fusion algorithm effectively generated high-resolution Landsat-8 images, demonstrating a strong correlation between original and synthetic images for the red and NIR spectral bands (0.93 and 0.84, respectively). These findings highlight the effectiveness of integrating STI-FM with spectral indices and SPI for accurate and high-resolution agricultural drought monitoring, which can support improved water resource management and agricultural planning in semi-arid regions.

Socio-Economic Implications of Snow Cover Area in the Upper Indus Basin

The Indus River, one of the principal waterways in Asia, specifically the Indian Subcontinent, originates from the Himalayan Region and Qinghai-Tibet Plateau. Understanding snow cover dynamics is one of the most vital components for effective hydrological resource management and assessing the impacts of local, regional, and global climatic variations. Variations in the radiation of snow microstructures and snowpacks have profound socio-economic and environmental consequences, affecting agricultural productivity, reservoir supply for factories, environmental sustainability, governance of land resources, and overall community resilience. Fluctuations in snowmelt patterns can lead to water shortages or surplus, influencing food security and economic stability in a country largely dependent on agriculture. Utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data, this study assesses the implications of Snow Cover Area (SCA) on water security and livelihood of agricultural-related sectors. A comparative analysis of snow cover reveals a shifting trend in the accumulation period, with notable anomalies affecting the agricultural output, which affects the household income related to the agricultural sector. This research underscores the necessity of adapting water management strategies to align with evolving snow cover dynamics to mitigate potential socio-economic risks.

Identification of High-Quality Vegetation Areas in Hubei Province Based on an Optimized Vegetation Health Index

This research proposes an optimized method for identifying high-quality vegetation areas, with a focus on forest ecosystems, using an improved Vegetation Health Index (VHI). The study introduces the Land Cover Vegetation Health Index (LCVHI), which integrates the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI) with land cover data. Utilizing MODIS (Moderate Resolution Imaging Spectroradiometer) satellite imagery and Google Earth Engine (GEE), the study assesses the impact of land cover changes on vegetation health, with particular attention to forested areas. The application of the LCVHI demonstrates that forests exhibit a VHI approximately 25% higher than that of croplands, and wetlands show an 18% higher index compared to grasslands. Analysis of data from 2012 to 2022 in Hubei Province, China, reveals an overall upward trend in vegetation health, highlighting the effectiveness of environmental protection and forest management measures. Different land cover types, including forests, wetlands, and grasslands, significantly impact vegetation health, with forests and wetlands contributing most positively. These findings provide important scientific evidence for regional and global ecological management strategies, supporting the development of forest conservation policies and sustainable land use practices. The research results offer valuable insights into the effective management of regional ecological dynamics.

Drought risk in Moldova under global warming and possible crop adaptation strategies.

This study analyzes the relationship between drought processes and crop yields in Moldova, together with the effects of possible future climate change on crops. The severity of drought is analyzed over time in Moldova using the Standard Precipitation Index, the Standardized Precipitation Evapotranspiration Index, and their relationship with crop yields. In addition, rainfall variability and its relationship with crop yields are examined using spectral analysis and squared wavelet coherence. Observed station data (1950-2020 and 1850-2020), ERA5 reanalysis data (1950-2020), and climate model simulations (period 1970-2100) are used. Crop yield data (maize, sunflower, grape), data from experimental plots (wheat), and the Enhanced Vegetation Index from Moderate Resolution Imaging Spectroradiometer satellites were also used. Results show that although the severity of meteorological droughts has decreased in the last 170 years, the impact of precipitation deficits on different crop yields has increased, concurrent with a sharp increase in temperature, which negatively affected crop yields. Annual crops are now more vulnerable to natural rainfall variability and, in years characterized by rainfall deficits, the possibility of reductions in crop yield increases due to sharp increases in temperature. Projections reveal a pessimistic outlook in the absence of adaptation, highlighting the urgency of developing new agricultural management strategies.

Evaluation of the coupling of EMACv2.55 to the land surface and vegetation model JSBACHv4

Abstract. We present the coupling of the Jena Scheme for Biosphere–Atmosphere Coupling in Hamburg version 4 (JSBACHv4) to the ECHAM/MESSy Atmospheric Chemistry (EMAC) model. With JSBACH, the soil water bucket model in EMAC is replaced by a diffusive hydrological transport model for soil water that includes water storage and infiltration in five soil layers, preventing soil from drying too rapidly and reducing biases in soil temperature and moisture. A three-layer soil scheme is implemented, and phase changes in water in the soil are considered. The leaf area index (LAI) climatology in EMAC has been substituted with a phenology module calculating the LAI. Multiple land cover types are included to provide a state-dependent surface albedo, which accounts for the absorption of solar radiation by vegetation. Plant net primary productivity, leaf area index and surface roughness are calculated according to the plant functional types. This paper provides a detailed evaluation of the new coupled model based on observations and reanalysis data, including ERA5/ERA5-Land datasets, Global Precipitation Climatology Project (GPCP) data and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. Land surface temperature (LST), terrestrial water storage (TWS), surface albedo (α), net top-of-atmosphere radiation flux (RadTOA), precipitation (precip), leaf area index (LAI), fraction of absorbed photosynthetic active radiation (FAPAR) and gross primary productivity (GPP) are evaluated in particular. The strongest correlation (r) between reanalysis data and the newly coupled model is found for LST (r=0.985, with an average global bias of −1.546 K), α (r=0.947, with an average global bias of −0.015) and RadTOA (r=0.907, with an average global bias of 3.56 W m−2). Precipitation exhibits a correlation with the GPCP dataset of 0.523 and an average global bias of 0.042 mm d−1. The LAI optimisation yields a correlation of 0.637 with observations and a global mean deviation of −0.212. FAPAR and GPP exemplify two of the many additional variables made available through JSBACH in EMAC. FAPAR and observations show a correlation of 0.663, with an average global difference of −0.223, while the correlation for GPP and observations is 0.564 and the average global difference is −0.001 kg carbon km−1. Benefiting from the numerous added features within the simulated land system, the representation of soil moisture is improved, which is critical for vegetation modelling. This improvement can be attributed to a general increase in soil moisture and water storage in deeper soil layers and a closer alignment of simulated TWS with observations, mitigating the previously widespread problem of soil drought. We show that the numerous newly added components strongly improve the land surface, e.g. soil moisture, TWS and LAI, while surface parameters, such as LST, surface albedo or RadTOA, which were mostly prescribed according to climatologies, remain similar. The coupling of JSBACH brings EMAC a step closer towards a holistic comprehensive Earth system model and extends its versatility.

Yield prediction models for some wheat varieties with satellite‐based drought indices and machine learning algorithms

AbstractIn recent years, frequent drought events in Konya, one of Türkiye's most important cereal production centres, have led to increased pressure on water and soil resources, resulting in yield losses, particularly in wheat production. Alternative yield prediction models, especially those that play a crucial role in agricultural import–export planning in the region, are important for economic contributions and the development of early warning systems. In this context, the aim of this study is to develop models that can be used in the yield prediction of wheat varieties widely grown in the Konya Altınova region. Agricultural drought indices obtained from Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) products of the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite were used to obtain model inputs. These indices are the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI) and Vegetation Supply Water Index (VSWI). In obtaining the input parameters for the models, the growth periods of the varieties in the region were also considered. Using various machine learning algorithms, 21 yield prediction models for Bayraktar‐2000, 12 for Kızıltan‐91 and 8 for Bezostaya‐1 were presented as alternatives, with model performances (coefficient of determination, R2) ranging between 0.74 and 0.97, 0.73 and 0.96, and 0.69 and 0.87, respectively.

Evaluation of Two Satellite Surface Solar Radiation Products in the Urban Region in Beijing, China

Surface solar radiation, as a primary energy source, plays a pivotal role in governing land–atmosphere interactions, thereby influencing radiative, hydrological, and land surface dynamics. Ground-based instrumentation and satellite-based observations represent two fundamental methodologies for acquiring solar radiation information. While ground-based measurements are often limited in availability, high-temporal- and spatial-resolution, gridded satellite-retrieved solar radiation products have been extensively utilized in solar radiation-related studies, despite their inherent uncertainties in accuracy. In this study, we conducted an evaluation of the accuracy of two high-resolution satellite products, namely Himawari-8 (H8) and Moderate Resolution Imaging Spectroradiometer (MODIS), utilizing data from a newly established solar radiation observation system at the Beijing Normal University (BNU) station in Beijing since 2017. The newly acquired measurements facilitated the generation of a firsthand solar radiation dataset comprising three components: Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), and Diffuse Horizontal Irradiance (DHI). Rigorous quality control procedures were applied to the raw minute-level observation data, including tests for missing data, the determination of possible physical limits, the identification of solar tracker malfunctions, and comparison tests (GHI should be equivalent to the sum of DHI and the vertical component of the DNI). Subsequently, accurate minute-level solar radiation observations were obtained spanning from 1 January 2020 to 22 March 2022. The evaluation of H8 and MODIS satellite products against ground-based GHI observations revealed strong correlations with R-squared (R2) values of 0.89 and 0.81, respectively. However, both satellite products exhibited a tendency to overestimate solar radiation, with H8 overestimating by approximately 21.05% and MODIS products by 7.11%. Additionally, solar zenith angles emerged as a factor influencing the accuracy of satellite products. This dataset serves as crucial support for investigations of surface solar radiation variation mechanisms, future energy utilization prospects, environmental conservation efforts, and related studies in urban areas such as Beijing.

Geostationary aerosol retrievals of extreme biomass burning plumes during the 2019–2020 Australian bushfires

Abstract. Extreme biomass burning (BB) events, such as those seen during the 2019–2020 Australian bushfire season, are becoming more frequent and intense with climate change. Ground-based observations of these events can provide useful information on the macro- and micro-physical properties of the plumes, but these observations are sparse, especially in regions which are at risk of intense bushfire events. Satellite observations of extreme BB events provide a unique perspective, with the newest generation of geostationary imagers, such as the Advanced Himawari Imager (AHI), observing entire continents at moderate spatial and high temporal resolution. However, current passive satellite retrieval methods struggle to capture the high values of aerosol optical thickness (AOT) seen during these BB events. Accurate retrievals are necessary for global and regional studies of shortwave radiation, air quality modelling and numerical weather prediction. To address these issues, the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm has used AHI data to measure extreme BB plumes from the 2019–2020 Australian bushfire season. The sensitivity of the retrieval to the assumed optical properties of BB plumes is explored by comparing retrieved AOT with AErosol RObotic NETwork (AERONET) level-1.5 data over the AERONET site at Tumbarumba, New South Wales, between 1 December 2019 at 00:00 UTC and 3 January 2020 at 00:00 UTC. The study shows that for AOT values > 2, the sensitivity to the assumed optical properties is substantial. The ORAC retrievals and AERONET data are compared against the Japan Aerospace Exploration Agency (JAXA) Aerosol Retrieval Product (ARP), Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue over land, MODIS MAIAC, Sentinel-3 SYN and VIIRS Deep Blue products. The comparison shows the ORAC retrieval significantly improves coverage of optically thick plumes relative to the JAXA ARP, with approximately twice as many pixels retrieved and peak retrieved AOT values 1.4 times higher than the JAXA ARP. The ORAC retrievals have accuracy scores of 0.742–0.744 compared to the values of 0.718–0.833 for the polar-orbiting satellite products, despite successfully retrieving approximately 28 times as many pixels over the study period as the most successful polar-orbiting satellite product. The AHI and MODIS satellite products are compared for three case studies covering a range of BB plumes over Australia. The results show good agreement between all products for plumes with AOT values ≤ 2. For extreme BB plumes, the ORAC retrieval finds values of AOT > 15, significantly higher than those seen in events classified as extreme by previous studies, although with high uncertainty. A combination of hard limits in the retrieval algorithms and misclassification of BB plumes as cloud prevents the JAXA and MODIS products from returning AOT values significantly greater than 5.

Drought Monitoring Using Moderate Resolution Imaging Spectroradiometer-Derived NDVI Anomalies in Northern Algeria from 2011 to 2022

Drought has emerged as a major challenge to global food and water security, and is particularly pronounced for Algeria, which frequently grapples with water shortages. This paper sought to monitor and assess the temporal and spatial distribution of drought severity across northern Algeria (excluding the Sahara) during the growing season from 2011 to 2022, while exploring the relationship between the normalized difference vegetation index (NDVI) anomaly and climate variables (rainfall and temperature). Temporal NDVI data from the Terra moderate resolution imaging spectroradiometer (MODIS) satellite covering the period 2000–2022 and climate data from the European Reanalysis 5th Generation (ERA5) datasets collected during the period 1990–2022 were used. The results showed that a considerable portion of northern Algeria has suffered from droughts of varying degrees of severity during the study period. The years 2022, 2021, 2016, and 2018 were the hardest hit, with 76%, 71%, 66%, and 60% of the area, respectively, experiencing drought conditions. While the relationship between the NDVI anomaly and the climatic factors showed variability across the different years, the steady decrease in vegetation health indicated by the NDVI anomaly corroborates the observed increase in drought intensity during the study period. We conclude that the MODIS-NDVI product offers a cost-efficient approach to monitor drought in data-scarce regions like Algeria, presenting a viable alternative to conventional climate-based drought indices, while serving as an initial step towards formulating drought mitigation plans.

Dust storms from the Taklamakan Desert significantly darken snow surface on surrounding mountains

Abstract. The Taklamakan Desert (TD) is a major source of mineral dust emissions into the atmosphere. These dust particles have the ability to darken the surface of snow on the surrounding high mountains after deposition, significantly impacting the regional radiation balance. However, previous field measurements have been unable to capture the effects of severe dust storms accurately, and their representation on regional scales has been inadequate. In this study, we propose a modified remote-sensing approach that combines data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and simulations from the Snow, Ice, and Aerosol Radiative (SNICAR) model. This approach allows us to detect and analyze the substantial snow darkening resulting from dust storm deposition. We focus on three typical dust events originating from the Taklamakan Desert and observe significant snow darkening over an area of ∼ 2160, ∼ 610, and ∼ 640 km2 in the Tien Shan, Kunlun, and Qilian mountains, respectively. Our findings reveal that the impact of dust storms extends beyond the local high mountains, reaching mountains located approximately 1000 km away from the source. Furthermore, we observe that dust storms not only darken the snowpack during the spring but also in the summer and autumn seasons, leading to increased absorption of solar radiation. Specifically, the snow albedo reduction (radiative forcing) triggered by severe dust deposition is up to 0.028–0.079 (11–31.5 W m−2), 0.088–0.136 (31–49 W m−2), and 0.092–0.153 (22–38 W m−2) across the Tien Shan, Kunlun, and Qilian mountains, respectively. This further contributes to the aging of the snow, as evidenced by the growth of snow grain size. Comparatively, the impact of persistent but relatively slow dust deposition over several months during non-event periods is significantly lower than that of individual dust events. This highlights the necessity of giving more attention to the influence of extreme events on the regional radiation balance. This study provides a deeper understanding of how a single dust event can affect the extensive snowpack and demonstrates the potential of employing satellite remote sensing to monitor large-scale snow darkening.

Sensitivity of surface downward longwave radiation to aerosol optical depth over the Lake Taihu region, China

Downward longwave radiation (DLR) is an important component of the global radiation budget, significantly influencing surface energy dynamics. Accurate quantification of aerosol impacts on surface DLR is crucial for refining numerical weather predictions and comprehending surface energy balance. While the impact of aerosols on downward shortwave radiation has garnered substantial attention, investigation into the longwave radiation effect of aerosols remains relatively limited. In this study, the impact of aerosols on surface DLR is quantified by using continuous radiation data, conventional meteorological observations, and aerosol optical depth (AOD) retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. These datasets span the period from 2011 to 2018 and are obtained from six stations of the Lake Taihu Flux Observational Network situated in Eastern China. The sensitivity of DLR to AOD (dDLR/dAOD) is determined through multiple linear regressions using both observational data and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis. Additionally, a radiative transfer model is employed to derive the dDLR/dAOD. A three-variable linear regression of the observational data shows that DLR increases by 4.66 ± 1.99 W m−2 per unit increase in AOD (mean ± 1 standard deviation) under clear-sky conditions. The radiative transfer model and the three-variable linear regression of MERRA-2 reanalysis yield similar sensitivity values of 4.69 ± 1.23 W m−2 AOD−1 and 4.10 ± 0.25 W m−2 AOD−1, respectively. However, these sensitivity values are considerably smaller than those reported in previous studies. Plausible factors contributing to the observed variance in sensitivity values are discussed. These results help to improve our understanding on the aerosol longwave radiation effect over the Lake Taihu region in Eastern China.

Relationship between aerosol and cloud characteristics over Delhi in North India during the dry and wet season

The study investigates the spatio-temporal variations of aerosol and cloud properties retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite at an urban megacity Delhi in the northern part of India during the dry and wet season for 15 years period from November 2004 to October 2019. This research comprises the study of crucial aerosol optical properties such as aerosol optical depth (AOD), Angstrom exponent (AE) along with the cloud properties such as water vapor (WV), cloud liquid water path (CLWP), cloud effective radius liquid (CERL), cloud fraction (CF), cloud optical depth (COD), cloud top pressure (CTP), and cloud top temperature (CTT). High AOD was observed across the Indo-Gangetic Plain (IGP), including Delhi and exhibited a range of 0.49–0.89 during the dry season and 0.73 to 0.98 during the wet season with a mean value of 0.73 ± 0.05 during the entire study period. High AE value (mean 1.40 ± 0.05) indicates dominance of fine-mode aerosols over the station, associated with high anthropogenic activities. A significantly large increasing trend in AOD was observed over the station during the wet season as compared to the dry season. The seasonal studies represent the maximum value of WV and CTP in the wet season. In addition, CF, CERL, COD and CLWP values were also high during the wet season. Furthermore, a correlation analyses between the aerosol and cloud parameters were carried out during both the seasons. From the analysis, AOD shows a positive correlation with WV, CF, COD and CLWP in both the seasons whereas a negative correlation was observed with CTP in wet season, while positive in dry season. On the other hand, a poor correlation was observed between AOD verses AE and CTT in both the seasons. These findings can contribute to advancing our understanding of aerosol-cloud interactions and inflate air quality assessments at an urban megacity like Delhi.

Seasonal variations in chlorophyll–a and sea surface temperature in the exclusive economic zone of Sri Lanka

Oceanic phytoplankton plays a crucial role in regulating the biogeochemical cycle in marine ecosystems. Light harvesting pigments such as chlorophyll–a (chl–a) can be used to understand marine primary productivity, especially phytoplankton abundance. Sea surface temperature is closely linked with phytoplankton abundance and climatic changes. The objective of the current study is to determine spatial and temporal variations of chl–a and sea surface temperature and their co-variation, over 12 years using statistical methodologies and Remote Sensing, Geographic Information System (GIS) and Cloud Computing Approach. In this study, the available sea surface temperature and chl–a data acquired from a Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor were proceeded through Google Earth Engine and Earth Science Data System. Data visualization was carried out using ArcMap™ software. Results show a weak negative linear relationship (−0.40, P < 0.05) between sea surface temperature and chl–a. Therefore, additional favorable environmental factors such as coastal upwelling, and nutrient discharge through river run-off can control the primary productivity of phytoplankton. We also illustrated spatial and temporal maps for chl–a and sea surface temperature distribution in the exclusive economic zone of Sri Lanka. Chlorophyll–a concentration is enhanced during the southwest monsoon, and the lowest concentration is observed during the first inter-monsoon period. Next, the highest sea surface temperature is recorded during the first inter-monsoon period, and the lowest sea surface temperature is observed during the northeast monsoon. Seasonal behavior of chl–a concentration and sea surface temperature are changed as chl–a (SWM) > chl–a (NEM) > chl–a (SIM) > chl–a (FIM) and SST (FIM) > SST (SIM) > SST (SWM) > SST (NEM), respectively. In addition, both trend lines for each variable show a positive gradient, which may be attributed to global warming. Finally, we observed the alteration of chl–a and sea surface temperature that linked to changes in ocean chemistry during the global COVID–19 pandemic.

Exploring the factors responsible for aerosol asymmetric trends over Indo-Gangetic Plain using remote sensing observations

The present study investigates the influencing factors responsible for the asymmetry in aerosol optical depth (AOD) trends using long-term datasets (2003–2019) over western and eastern Indo-Gangetic Plain (IGP) regions during the pre-monsoon season. Analysis from Modern-Era Retrospective Analysis for Research and Applications Version-2 (MERRA-2) for different aerosols illustrates that dust aerosols dominate over the western IGP (W-IGP), while sulphate and carbonaceous aerosols (black carbon (BC) and organic carbon (OC)) majorly contributed to the total AOD over the eastern IGP (E-IGP). Our study reveals a significant decline in AOD over the W-IGP, while a rising trend over E-IGP from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations. A dipole pattern in AOD trends over IGP indicates the aerosol loading from combined effects of various natural and anthropogenic emissions under favourable meteorological conditions over the W-IGP and E-IGP, respectively. Furthermore, the declining AOD trend over W-IGP is mainly attributed to increased pre-monsoonal rainfall, which supports the wet deposition, increases soil moisture, thus reducing soil erodibility, and correlates strongly with meteorological factors. The rising AOD trend over the E-IGP appears to be influenced by increased anthropogenic emissions (i.e., BC, OC, and sulphate) from the industrialization of the region, decreased rainfall, and enhanced westerly-induced advection of aerosols from W-IGP. Our study indicates that the regional meteorological variables and anthropogenic sources influence changes in the AOD trends over the IGP region.

Spatial and temporal patterns of land surface temperature in Greenland from 2000-2019

Temperature dynamics on the island of Greenland are an important factor in shaping ecological events. Investigating the land surface temperature (LST) patterns is critical for understanding ecological dynamics across different regions. Further melting of the Greenland ice sheet could deva state marine and terrestrial ecosystems. This study used data from Moderate Resolution Imaging Spectroradiometer satellites to understand the seasonal patterns and patterns of LST over the entire island. Focusing on the period between 2000 and 2019, this study used a natural cubic spline model to identify seasonal patterns for all sub-regions. The data were seasonally adjusted and filtered with a second-order autocorrelation component. The spline was fitted again to identify the LST pattern, and a multivariate regression model was then used to adjust for spatial correlation. We illustrate that most of the land surface of Greenland hasstable temperature trends. These observed patterns in LST in Greenland during the study period suggest that the observed ice-sheet melting in Greenland within the last two decades could be due to other factors, not necessarily LST patterns.

QGIS-based modeling and analysis of urban dynamics affecting land surface temperature towards climate hazards in coastal zones of Portugal

Climate risk creates considerable concern due to the density of natural and socio-economic assets in coastal areas. Monitoring land use/cover changes, detecting population growth, and analyzing their impact on land surface temperature (LST) are necessary for effective urban management. In this study, land use/land cover (LULC), population, and LST changes in coastal regions of Portugal. Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery was examined using World Bank population and CORINE data. Changes in land use types and LST values from 1990 to 2018 were analyzed. At the same time, LULC predictions were made using the Modules for Land Use Change Simulation (MOLUSCE) plug-in included in the QGIS software, and population projections were analyzed with LULC predictions in 2046. The results show the significant impact of land use on temperatures. It has been demonstrated that green and water areas can effectively cool cities. In the LULC changes between 1990 and 2018, the Leiria region stands out, with an annual increase of 4.04% in built areas from 121.58 to 259.06 km2. According to the simulations between 2018 and 2046, it was predicted that 18.74% of agricultural areas and 14.43% of forest areas would be transformed into built environments. The study is also essential as it confirms that the MOLUSCE plug-in can be effectively applied to land cover simulation on a large regional scale.

Dynamics of Peatland Fires in South Sumatra in 2019: Role of Groundwater Levels

During the dry season, extensive peatland fires in South Sumatra and another peatland in Indonesia result in environmental damage and pose health risks to humans. The Indonesian Government has implemented several measures to prevent the recurrence of these fires. One such measure involves the establishment of observation stations to monitor hydrometeorological parameters in different peatlands across Indonesia, including those in South Sumatra. To effectively control fires in South Sumatra’s peatland and minimize hotspot occurrences, it is essential to determine hydrometeorological parameters that can serve as fire control indicators. Therefore, this study aimed to investigate the relationship between groundwater levels and hotspot occurrences by analyzing groundwater level data collected from six Peat Restoration Agency stations in South Sumatra’s peatland, along with hotspot data obtained from Moderate Resolution Imaging Spectroradiometer satellite measurements. The findings reveal a significant correlation between groundwater levels and hotspots at the six stations. As the GWL increased, the number of hotspots tended to decrease, and vice versa. This means that GWL can be used as a controlling variable for hotspot emergence. To effectively minimize hotspot occurrences, it is recommended to maintain a minimum groundwater level of −0.45 ± 0.09 m in the peatland of South Sumatra.

Can land-use and land-cover change explain reduced resilience in forests?

Generating signals of reduced resilience in ecosystems is crucial for conservation and management endeavors. However, the practical implications of such systems are still limited due to the lack of high-frequency data and uncertainties associated with predicting complex systems such as ecosystems. This study aims to investigate the potential of time series analysis of remote sensing data in detecting signals of reduced resilience in mangrove forest ecosystems. Using time series analysis of remote sensing images, the resilience of mangrove forests was explored across two distinct study sites. One site (Qeshm Island) has been adversely affected by land-use and land-cover changes, while the other (Gabrik) serves as a reference ecosystem. The study uses data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite to quantify three remotely sensed indices: the Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI), and the Modified Vegetation Water Ratio (MVWR). In addition, Landsat data has been used to explore temporal alterations in land-use and land-cover change. To identify signals of reduced resilience, trend analyses of indicators such as autocorrelation (acf (1)) and standard deviation (SD) are applied. The findings revealed a notable decrease in resilience, signaled by significant upward trends in NDVI statistical metrics for Qeshm Island (Kendall’s τ of acf (1): 0.50 and SD: 0.90), contrasting with the pattern observed in Gabrik (Kendall’s τ of acf (1): −0.19 and SD: −0.19). These results align with our expectations derived from previous studies. Despite MNDWI significantly indicating reduced resilience in Qeshm Island (Kendall’s τ of acf (1): 0.86 and SD: 0.90), it also signaled decreased resilience in Gabrik (Kendall’s τ of acf (1): 0.79 and SD: 0.90). Moreover, MVWR failed to indicate signals of reduced resilience in both sites, specifically in Qeshm (Kendall’s τ of acf (1): −0.10 and SD: −0.07) and in Gabrik (Kendall’s τ of acf (1): −0.72 and SD: −0.12). These findings may be explained through quantitative analyses of land-use and land-cover change. While Qeshm Island and Gabrik share similarities in climate, geography, and annual rainfall, the analysis of land-use and land-cover change revealed significant differences between the two study areas. Qeshm Island underwent drastic increases in the built-up class by a 64.40% change between 1996 and 2014, whereas the built-up class expanded modestly by a 4.04% change in the Gabrik site. This study contributes to advancing our understanding of ecosystem dynamics. The findings of this study can be integrated with ecosystem management tools to enhance the effectiveness of conservation efforts. This is the first report of the successful application of remote sensing in generating signals of reduced resilience within mangrove forests in the Middle East.

Satellite Imagery Data Curation Workflow for Wildfire Detection With Advanced Segmentation Modeling

Across the globe, wildfires are occurring at increased frequency, significantly impacting ecosystems and human civilizations. This research paper focuses on the efficacy of two of the most advanced semantic segmentation machine learning models, specifically U-Net based on convolutional neural network and SegFormer based on Vision Transformer network for wildfire detection utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. The dataset is assembled using a specially built pipeline composed of 1) a workflow to obtain the wildfire candidate location and date, and 2) a subsequent step to collect satellite imagery data utilizing Google Earth Engine image collection and image download service. Experimental evaluation on this dataset shows that both models demonstrate high predictive power of fire at specific geolocations, with ViT outperforming U-Net at the edges of fire regions.