Landsat Data Research Articles

Dynamic trends in land surface temperature and land use/land cover transitions in semi-arid metropolitan city, Jaipur.

The increasing surface heat in metropolitan areas is one of the biggest issues, especially as natural surfaces are being replaced by impermeable concrete surfaces. This study uses Landsat data (1991-2022) to examine the spatio-temporal dynamics of LST and LULC in Jaipur, highlighting the impact of urban expansion and the city's semi-arid nature on the thermal landscape. We have used the maximum likelihood classifier for supervised LULC classification and the mono-window algorithm for retrieving LST. The evaluation is done using buffer analysis. Furthermore, to assess the interrelationship between LST and LULC indices (NDVI & NDBI), regression analysis is used. The CA-ANN model is employed to project LSTs of 2032 and 2042. The findings indicate that the built-up land in the study area grew by 52.80% from 1991-2022. Most of this expansion has come at the expense of agriculture/open land, and vegetation cover. The mean LST in the city has risen by 5.9°C, with the inner zone (B1) increasing from 35.44°C to 41.93°C, indicating urbanisation-induced heat stress. In the outer zones (B5-B6), dry sandy and rocky soils contribute to elevated temperatures. Water bodies show the lowest LST, while open and barren lands have the highest. LST exhibit a positive correlation with NDBI and a weak negative correlation with NDVI. Predictions indicate that by 2042, about 99% of the urban landscape will encounter surface temperatures above 40°C, with 28.79% exceeding 45°C. Raised temperatures could exacerbate the UHI effect, leading to serious health and environmental concerns.

Analysis and Future Projections of Land Use and Land Cover Changes in the Hindon River Basin, India Using the CA-Markov Model

Land use and land cover change is a significant issue in emerging countries. The enormous rate of population growth, industrialization, and urbanization is responsible for these developments. Monitoring and mapping of changes in land cover and land use is essential to the sustainable development and management of the area. The study attempts to track changes in LULC pattern for the years 2002, 2013, and 2023 in the Hindon River Basin, a major tributary of the Yamuna River, using remote sensing and geographic information system techniques. Images obtained from Landsat data were employed to extract historical land use and land cover maps. Additionally, the CA-Markov model was implemented to forecast future land use and land cover patterns. This study examines the historical and predicted LULC in the area. Field observations and site-specific interviews were used to confirm and determine the ground realities. High-resolution images were used to evaluate the accuracy of the classified map. According to the results, the agricultural land decreased from 60.98% in 2002 to 54.70% in 2050, while built-up areas increased from 12.95% to 21.25% during the same period. By 2050, vegetation is predicted to increase to 2.58%, whereas surface water, fallow land, barren areas, and dry water bodies are predicted to decrease to 0.58%, 18.87%, 1.20%, and 0.83%, respectively. The rapid pace of urbanization is facilitating economic growth within the country; however, this development is occurring at the expense of the natural landscape, which subsequently diminishes the overall quality of human life. In order to maintain sustainable development in the Hindon Basin, proper urban planning is essential. Important policy implications for the sustainable management of land use and conservation in the Hindon River basin are highlighted by the study’s research and findings.

Estimation of Vegetation Carbon Sinks and Their Response to Land Use Intensity in the Example of the Beijing–Tianjin–Hebei Region

Accurate regional carbon sequestration estimates are essential for China’s emission reduction and carbon sink enhancement efforts to address climate change. Enhancing the spatial precision of vegetation carbon sink estimates is crucial for a deeper understanding of the underlying response mechanisms, yet this remains a significant challenge. In this study, the Beijing–Tianjin–Hebei (BTH) region was selected as the study area. We employed the GF-SG (Gap filling and Savitzky–Golay filtering) model to fuse Landsat and MODIS data, generating high-resolution imagery to enhance the accuracy of NPP (Net Primary Productivity) and NEP (Net Ecosystem Productivity) estimates for this region. Subsequently, the Sen+MK model was used to analyze the spatiotemporal variations in carbon sinks. Finally, the land use intensity index, which reflects human activity disturbances, was applied, and the bivariate Moran’s spatial autocorrelation method was used to analyze the response mechanisms of carbon sinks. The results indicate that the fused GF-SG NDVI (Normalized Difference Vegetation Index) data provided highly accurate 30 m resolution imagery for estimating NPP and NEP. The spatial distribution of carbon sinks in the study area showed higher values in the northeastern forest regions, relatively high values in the southeastern plains, and lower values in the northwestern plateau and central urban areas. Additionally, 58.71% of the area exhibited an increasing trend, with 11.73% showing significant or strongly significant growth. A generally negative spatial correlation was observed between land use intensity and carbon sinks, with the impact of land use intensity on carbon sinks exceeding 0.3 in 2010. This study provides methodological insights for obtaining vegetation monitoring data and estimating carbon sinks in large urban agglomerations and offers scientific support for developing ecological and carbon reduction strategies in the BTH region.

Evaluating Urban Heat Islands Dynamics and Environmental Criticality in a Growing City of a Tropical Country Using Remote-Sensing Indices: The Example of Matara City, Sri Lanka

Urbanization has undeniably improved human living conditions but has also significantly altered the natural landscape, leading to increased Urban Heat Island (UHI) effects. While many studies have examined these impacts in other countries, research on this topic in Sri Lanka remains limited. This study aimed to evaluate the effects of changes in built-up areas (BAs) and Vegetation Cover (VC) on UHI and environmental criticality (EC) in Matara cityCity, Sri Lanka, utilizing Landsat data. This study employed the commonly used remote-sensing (RS) indices such as the land surface temperature (LST), the UHI Index, and the Environmental Criticality Index (ECI). Various techniques were utilized including supervised image classification, Urban–Rural Gradient Zone (URGZ) analysis, grid-based analysis, UHI profiles, and regression analysis. The results revealed that built-up areas increased by 12.21 km2, while vegetation cover decreased by 9.94 km2, and this urban expansion led to a 2.7 °C rise in mean LST over 26 years. By 2023, newly developed BA showed the highest LST and environmental criticality, with mean LST values ranging from 25 °C to 21 °C in URGZs 1 to 15 near the city center, and lower values of 15 °C to 16 °C in URGZs 40 to 47 further from the core. The correlation analysis highlighted a strong positive relationship between the NDBI and LST, underscoring the significant impact of BA expansion on LST. Consequently, high-density built-up areas are experiencing high environmental criticality. To minimize these effects, planning agencies should prioritize green urban planning strategies, particularly in high LST and environmental criticality zones. This approach can also be applied to other cities to assess the UHI and LST phenomena, with the goal of protecting the natural environment and promoting the health of urban dwellers.

Analysis of vegetation dynamics from 2001 to 2020 in China's Ganzhou rare earth mining area using time series remote sensing and SHAP-enhanced machine learning

Rare earth mining, essential for modern industries and economic growth, often leads to severe environmental degradation. Previous research has explored the ecological impacts of rare earth mining but has not fully investigated the intricate interplay and subdivisions of environmental and anthropogenic factors driving vegetation changes over extended periods. This study addresses this gap by employing time series remote sensing and SHAP-enhanced machine learning to analyze vegetation dynamics in China's Ganzhou rare earth mining area from 2001 to 2020. Using the kNDVI derived from Landsat data, we identified three distinct vegetation trajectory types: pro-environment, des-environment, and res-environment. An ensemble machine learning model combined with SHAP analysis revealed the cropland area proportion, PM10 levels, and shrubland area proportion as the most influential factors affecting vegetation across all mining types. Additionally, after 2012, the palmer drought severity index and downward surface shortwave radiation emerged as positive contributors to vegetation health, while population pressure had a more substantial negative influence in des-environment areas. Our findings highlight spatial heterogeneity in vegetation recovery patterns and highlight the complex interactions among land cover changes, air quality, climate factors, and human activities in shaping vegetation dynamics. This study provides valuable insights for developing targeted, context-specific restoration strategies in rare earth mining areas, contributing to more sustainable mining practices and global environmental management.

Analysing Surface Heat Fluxes Variation with Imperviousness and Land Surface Temperature from Landsat Data

Analysing Surface Heat Fluxes Variation with Imperviousness and Land Surface Temperature from Landsat Data

Effects of the long-term rice expansion on ecosystem carbon budget in the typical agricultural area of Northeast China

Extensive rice expansion in northeast China has significantly altered land use and land cover (LULC) changes. However, the impact of long-term rice expansion on regional carbon budget dynamics remains unclear. A major obstacle in addressing this gap is the absence of agricultural LULC information with high spatiotemporal resolution. Here, we selected the Sanjiang Plain experienced dramatic rice expansion as the study area, presenting a framework that integrates high-resolution crop distribution data with a process-based model to quantify the impact of rice expansion on regional carbon budgets. Specifically, we employed a robust deep-learning network for crop mapping based on Landsat data, to reconstruct the spatiotemporal dynamics of rice expansion from 1985 to 2020. We then incorporated these long-term crop maps into the Denitrification-Decomposition (DNDC) model, to explore the effects of rice expansion on the regional carbon budget over this period. Analysis results showed that the rice planting area expanded by 708.64 km2/yr, resulting in more than a tenfold increase over the past 36 years. Spatially, rice planting expanded from the southwest to the northeast and from the interior to the exterior. This expansion has resulted in approximately 275 Mt. of CO2 and 6.92 Mt. of CH4 greenhouse gas emissions, altering the dynamics of the regional carbon budget and shifting the ecosystem from a carbon sink to a carbon source since 2016. Although the long-term expansion of rice increased soil respiration and CH4 emissions, it also enhanced soil carbon sequestration through agricultural management practices. These findings greatly enhance our understanding of the ecosystem carbon cycle's response to long-term agricultural LULC changes, providing more accurate data support and scientific evidence for developing low-carbon agricultural policies.

Monitoring northern Greenland proglacial river discharge from space

Large volumes of meltwater produced on the northern Greenland Ice Sheet (GrIS) are directly routed into proglacial rivers, forming continuous supraglacial-proglacial catchments. Thereby, estimating proglacial river discharge is crucial for better understanding of northern Greenland hydrology and mass balance. We propose a method for estimating proglacial river discharge solely from space by combining Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), ArcticDEM, and Harmonized Landsat and Sentinel-2 (HLS) data. Firstly, we use the modified normalized difference water index to extract proglacial river water masks from 30 m HLS imagery time series and calculate river effective width (We). Secondly, we derive near-dry riverbed cross-sectional curves from ICESat-2 ATL06 data. Thirdly, we intersect proglacial river water masks with riverbed cross-sectional curves to calculate the mean depth, wetted perimeter, cross-sectional area, and hydraulic radius, and combine ArcticDEM to estimate the channel bed slope. Finally, with these hydraulic geometry estimates, we calculate proglacial discharge and analyze its uncertainty via error propagation. We apply this method to estimate the proglacial discharge (Qs) of the Denmark catchment (area ∼ 3254 km2) in northern Greenland during the 2020–2021 melt seasons and compare Qs with the surface meltwater runoff (Qm) simulated by two regional climate models (RCMs, including MARv3.12 and RACMO2.3p2), and validate the accuracy and spatial transferability of the method with in-situ proglacial discharge (Qin-situ) of the Watson River in southwestern Greenland. The results show that: (1) the satellite-estimated We and Qs exhibit significant seasonal variations, with the average We of 579 ± 371 m for 2020, 505 ± 394 m for 2021, and a maximum of 2040 m, and Qs has the average value of 207.6 ± 134.1 m3/s for 2020, 210.4 ± 243.2 m3/s for 2021, and a maximum of 1509.4 ± 190.3 m3/s; (2) the satellite-estimated Qs is positively correlated with the RCM-simulated Qm (R2 = 0.82 and 0.69 for MAR and RACMO, respectively), indicating that RCMs can reflect the overall seasonal variations of proglacial discharge reasonably well; (3) the RCM-simulated Qm is considerably higher than our satellite-estimated Qs, with the bias, RMSE, and RRMSE for MAR (RACMO) being 116.6 ± 5.9 m3/s (130.3 ± 5.9 m3/s), 174.7 ± 6.7 m3/s (208.9 ± 6.1 m3/s), and 83 ± 4 % (100 ± 4 %), respectively, and (4) our satellite-based method can be successfully applied to the Watson River in southwestern Greenland and the resultant Qs matches well with Qin-situ (RRMSE = 27 %). In conclusion, multi-temporal and multi-source satellite observations facilitate the estimation of proglacial river discharge and provide an approach to directly estimate GrIS ice surface meltwater runoff.

A novel optimized spectral-based data driven approach for ecoregion burned scar detection

ABSTRACT Climate change has led to an increase in fire frequency in some areas on Earth. Satellite-derived spectral indices such as the Burn Area Index (BAI), the Normalized Burn Ratio (NBR), and their various derivatives are important for monitoring fires. The Burn Area Index for Sentinel-2 (BAIS2), which relies on the spectral bands of Sentinel-2, has a limitation due to its dependence on the red-edge spectral region unique to this satellite. To address this limitation, two new spectral indices (the Optimized Burned Area Index (OBAI) and the Optimized Burned Area Thermal Index (OBATI)) for burned scar mapping have been proposed based on the spectral separability concept and inter-band correlations. These indices aimed to improve the effectiveness of burned scar mapping by utilizing a wider range of spectral bands, including Landsat-8 thermal band and visible, near infrared (NIR), and mid infrared (MIR) regions of either Landsat-8 or Sentinel-2 sensors, which are more responsive to burned scars. We used spatial and spectral accuracy assessments to compare the effectiveness of the standard burned scar mapping indices (BASI2 and NBR) with the newly developed indices in discriminating the burned scars in several world regions. Based on Emergency Management Service fire perimeters, the vector distance algorithm (VDA) revealed that the accuracy of the fire perimeters extracted from the newly developed index, OBAI, was more reliable than the perimeters extracted using the BAIS2 and NBR. The BAIS2 index had the lowest performance in terms of spectral sensitivity compared to the other indices in the detection of burned scars. The performance of the developed optical index was high once the Landsat-8 data used to calculate it compared to Sentinel-2 data. Our findings could be used to further optimize global burned scar products derived from spaceborne data.

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.

Agricultural land use change and its drivers in the Sudanese Ethiopian borderland: the case of Al-Fashaga region

ABSTRACT The Al-Fashaga region, situated along the Sudanese-Ethiopian border, has experienced significant land use and land cover (LULC) changes over the past three decades due to large-scale mechanized agriculture and conflict-related dynamics. This study examines LULC transitions from 1990 to 2020 using multi-temporal Landsat data, Google Earth imagery, field surveys, and key informant interviews. Findings reveal a substantial increase in agricultural land, now covering over 75% of the region, predominantly at the expense of natural vegetation, which has declined to 13.7%. The 2020 Tigray conflict in Ethiopia triggered shifts in land control, enabling Sudanese farmers to reclaim abandoned lands, and illustrating the complex interrelations between conflict, land tenure, and resource use. This dynamic has exacerbated environmental degradation and heightened socio-political tensions over fertile borderlands. The study emphasizes the urgency of conflict-sensitive land management strategies to balance agricultural productivity and environmental sustainability. Recommendations include strengthening land tenure security, adopting sustainable farming practices, and fostering cross-border collaboration to address resource-based conflicts. These findings offer critical insights into how geopolitical tensions influence land use in border regions, contributing to policies aimed at sustainable land management, conflict resolution, and peacebuilding in similarly affected areas.

Examining the nexus of social vulnerability, land cover dynamics, and heat exposure in Reno, Nevada, USA

Intense heat is a persistent urban challenge whose impacts are detrimental to human health. Heat-related effects disproportionately impact underserved populations. Modification of urban landscapes through increased imperviousness intensifies surface temperatures, leading to heightened heat exposure risks. While climate adaptation efforts have advanced, they are inadequate in addressing the uncertainties of climate change and the long-term risks of climate-related hazards. In addition, despite the numerous heat vulnerability studies across U.S. cities, the City of Reno is largely understudied. To address these gaps, the research examined the relationship between the spatiotemporal patterns of social vulnerability, changes in biophysical properties, and the heat hazard in Reno, Nevada. We utilized CDC census data to map the Social Vulnerability Index (SVI) and Landsat satellite data from 1990 to 2023 to analyze Land Surface Temperature (LST) trends for a temporal comparative study of heat patterns. Additionally, we employed the Normalized Difference Vegetation Index (NDVI) for vegetation extent. The zonal statistics tool helped assess the influence of different land use features on surface temperatures. The results showed that regions identified as social vulnerability hotspots often coincided with areas highly exposed to extreme temperatures and vice versa. Our findings also revealed an extension of heat vulnerability hotspots from the urban core to suburban regions. We observed a decline in mean LST values in regions covered by vegetation and a rise in mean surface temperatures in regions encompassed with imperviousness. These findings underscore the need for increased vegetation for heat mitigation.

Land surface temperature trends derived from Landsat imagery in the Swiss Alps

Abstract. The warming of high mountain regions caused by climate change is leading to glacier retreat, decreasing snow cover, and thawing permafrost, all of which have far-reaching effects on ecosystems and societies. Landsat Collection 2 provides multi-decadal land surface temperature (LST) data, principally suited for large-scale monitoring at high spatial resolution. In this study, we assess the potential to extract LST trends using Landsat 5, 7, and 8 time series. We conduct a comprehensive comparison of both LST and LST trends with data from 119 ground stations of the Intercantonal Measurement and Information System (IMIS) network, located at high elevations in the Swiss Alps. The direct comparison of Landsat and IMIS LST yields robust satellite data with a mean accuracy and precision of 0.26 and 4.68 K, respectively. For LST trends derived from a 22.6-year record length, as imposed by the IMIS data, we obtain a mean accuracy and precision of −0.02 and 0.13 K yr−1, respectively. However, we find that Landsat LST trends are biased due to unstable diurnal acquisition times, especially for Landsat 5 and 7. Consequently, LST trend maps derived from 38.5-year Landsat data exhibit systematic variations with topographic slope and aspect that we attribute to changes in direct shortwave radiation between different acquisition times. We discuss the origin of the magnitude and spatial variation of the LST trend bias in comparison with modeled changes in direct shortwave radiation and propose a simple approach to estimate the LST trend bias. After correcting for the LST trend bias, the remaining LST trend values average between 0.07 and 0.10 K yr−1. Furthermore, the comparison of Landsat- and IMIS-derived LST trends suggests the existence of a clear-sky bias, with an average value of 0.027 K yr−1. Despite these challenges, we conclude that Landsat LST data offer valuable high-resolution records of spatial and temporal LST variations in mountainous terrain. In particular, changes in the mountain cryosphere, such as glacier retreat, glacier debris cover evolution, and changes in snow cover, are preserved in the LST trends and potentially contribute to improved prediction of permafrost temperatures with large spatial coverage. Our study highlights the significance of understanding and addressing biases in LST trends for reliable monitoring in such challenging terrains.

Vegetation disturbance and regrowth dynamics in shifting cultivation landscapes

Shifting cultivation, an age-old agricultural practice, is a major factor in forest cover change across Southeast Asia, where repeated cycles of vegetation disturbance and regrowth lead to far-reaching environmental and socio-economic impacts. The present study aims to assess the spatio-temporal patterns of vegetation disturbance and regrowth caused by shifting cultivation in Tripura state of India, over the past three decades, utilizing temporal segmentation of time-series Landsat data. The study analyzed vegetation disturbance and regrowth patterns in a shifting cultivation landscape from 1991 to 2020 using normalized burn ratio trends through LandTrendr, validated by the TimeSync tool. Six land use and land cover classes, viz., forests and trees outside forests, rubber plantation, shifting cultivation, water bodies, agriculture, and settlements, were mapped with an accuracy of 83.08% using a random forest classifier. This classification enabled the identification of the effective study area, which included forest areas with shifting cultivation patches. The analysis revealed that 2533.96 km2 of the study area remained undisturbed, 568.43 km2 experienced low-magnitude disturbance, 1501.11 km2 were moderately disturbed, and 184.82 km2 were highly disturbed. The shifting cultivation cycles in the study area show considerable variation. Low-magnitude disturbance indicates a single slash-and-burn event in the past three decades, moderate-magnitude disturbance involves fallow periods of over 10 years, and high-magnitude disturbance occurs with fallow periods of less than 8 years. The study revealed a shift from traditional shifting cultivation to more permanent agricultural practices in parts of Tripura. In response to incentives for commercial crops, cultivators are increasingly adopting long-term cultivation, including the growing of pineapple, areca nut, and rubber, as well as intercropping with papaya, banana, lemon, tapioca, pepper, and ginger. These plots are, however, abandoned if the yields become economically unviable. The expansion of monoculture cultivation significantly reduces the available area for shifting cultivation, thereby compelling jhum cultivators to revisit their jhum patches more frequently. This study is a first-of-its-kind attempt in the Indian context and sheds light on changing patterns in an age-old agricultural practice.

Mapping coconut plantation in Western Agro-Climatic zone using object-based classification and machine learning technique

Coconut (Cocos nucifera), a key crop for over 10 million farming families in India, is vital in the agricultural economies of southern states like Tamil Nadu. However, traditional methods of monitoring coconut plantations are challenging due to the crop's geographical dispersion and seasonal variations. This study focuses on mapping coconut plantations in the Western Agro-Climatic Zone of Tamil Nadu using Object-Based Classification (OBC) and machine learning techniques. A ten-year time series of Landsat 7 optical satellite data (2012-2013 and 2022-2023) was employed, combined with ground truth surveys across the region. The study utilized Support Vector Machine (SVM) and Random Forest (RF) classifiers, with RF demonstrating superior accuracy. The RF classifier achieved an accuracy of 91.7% in 2012-2013 and 90.3% in 2022-2023, outperforming SVM, which hovered around 70%. The research also conducted a change detection analysis, revealing a net increase of 3,270 hectares of coconut plantations over the decade, with the Coimbatore district contributing the most significant growth of 2,560 hectares. This study underscores the effectiveness of integrating OBC and machine learning, mainly RF, for accurate and efficient mapping of coconut plantations using Landsat satellite data.

Using Google Earth Engine open-source code for land surface temperature estimation from Landsat data in Chuong My district, Hanoi city, Vietnam

Land surface temperature (LST) in association with urbanization has significantly influenced on local climates and terrestrial surface processes. By using multi-temporal Landsat data in the GEE platform, the study has found that there have been changes in land covers due to the main drivers of industrial development, urban and built-up expansion. This study estimated land surface temperature (LST) in Chuong My district from Landsat 5, 7, 8 and 9 thermal infrared sensors, using different surface emissivity sources. The Google Earth Engine (GEE), an advanced earth science data and analysis platform, offers the estimation of LST products, covering the time period from 2000 to 2024 in study site. RF algorithm for land cover classification and mapping is suggested to be applied in Chuong My district, with overall accuracy of land cover mapping in 2024 being 91.9% with Kappa coefficient of 0.89. The period 2000-2010 showed that 327.0 ha of land were converted to land for industrial development, urban and residential land, while the period 2010-2024 continued to witness a continued conversion of land to other land use purposes, with a decrease in agricultural land area (65.7 ha) and an increase in industrial land use (65.2 ha), but the rate of conversion was much lower than the previous period (2000-2024). The periods of 2000-2010 and 2010-2024 showed that there have been significant changes in land surface temperature. Most of the surface temperature in the entire region increased by over 20C, especially in the period 2003-2015 when a total area of 1424 ha had the temperature increased by above 30C. This is a consequence of the process of urbanization and industrialization, which began to take place in 2008, along with the process of changing the land cover status of the study area. Further studies are needed to better understand the thermal conductivity of surface materials and to plan how to reduce LST in such areas.

Modeling Land Use and Land Cover Changes and Its Atmospheric Pollutant Concentration in the Coal Mining Area of Ramgarh District of Jharkhand, India, Using Multi‐Layer Perceptron Neural Networks (MLPNN)

ABSTRACTLand use refers to anthropogenic phenomena in the natural environment; humans utilize land resources for their developmental activities. On the other hand, the ecosystems of land use and land cover alter the natural world—the artificial infrastructure leads toward a busted concrete jungle instead of a green footprint. The global green footprint is continually shrinking owing to overutilization of natural resources. The present research examines the land use pattern that changes from 1990 to 2021 and projected projections for 2041 and 2061 in the Ramgarh District. The study also focuses on how artificial modifications alter the concentration level of pollutants in the atmosphere. The Landsat data utilized for 1990, 2000, 2011, and 2021 were incorporated into the LULC map using supervised classification and for analysis of future predictions for 2041 and 2061 using an ANN‐based on MLPNNs (multi‐layer perceptron neural networks) for Ramgarh District. It also focuses on the trend and patterns of atmospheric pollutants from data using NASA‐GIOVANNI MERRA‐2. The current study reveals that in 1990, water bodies, coal mining, vegetation, built‐up, agriculture, and barren land were 3.01%, 2.24%, 54.07%, 3.64%, 36.85%, and 0.18 %. However, in 2021, water bodies decreased to 1.61%, vegetation to 45.47%, barren land to 0.65%, and an increasing tendency was observed in built‐up areas to 6.65%, coal mining to 2.43%, and farmland to 43.19%. A significant trend in atmospheric pollutants, such as CO2, SO2, SO4, NO2, and dust, is observed in the Ramgarh district. The importance of this study is to attain the maximum level of environmental sustainability; it would also encourage the local level planning fitted during the extraction of natural resources.

Monitoring water clarity of lakes in the Middle-Lower Yangtze Plain using Landsat observations (1984–2023)

Using the improved Quasi-Analytical Algorithm (QAA) and Landsat data, we documented the long-term changes in water clarity of the 17 largest lakes in the Middle-Lower Yangtze Plain from 1984 to 2023. A comprehensive dataset with over 4600 water clarity maps, reconstructed from 2511 Landsat series images, was compiled. The water clarity changes of these 17 lakes have a clear seasonal variation pattern, with the highest in summer and the lowest in winter. Over the past 40 years, water clarity of 59 % lakes shows a downward trend, with Junshan Lake showing the highest decrease rate of −0.0231 m/yr. 41 % lakes water clarity are showing an upward trend. From 1984 to 1990, the highest average ZSD (Secchi disk depth) values were recorded in Changhu Lake, Junshan Lake, Honghu Lake, Futou Lake, and Gehu Lake. These lakes, however, experienced the most significant declines compared to period from 2021 to 2023. Chaohu Lake, Taihu Lake, Caizi Lake, and Nanyi Lake, which had the lowest average ZSD during 1984–1990, showed the most substantial increases. Analyzing the monthly distribution of water level and ZSD data reveals that water clarity exhibits different seasonal variations in relation to water levels. The comprehensive dataset and analysis provide a crucial scientific basis for informed water resource management and policy-making in the region.

Mitigating coarse spatial reconstruction to generate missing bands for the HLS dataset

Abstract. The Harmonized LandSat-Sentinel (HLS) dataset has significantly advanced Earth Observation by integrating data from Landsat and Sentinel satellites. However, challenges persist in achieving spectral band parity between LandSat and Sentinel-derived HLS products. This paper presents an extended investigation aimed at enhancing spatial reconstruction accuracy to enable spectral band parity within HLS products. Building upon our previous work, which utilized generative neural networks to address partial feature mismatches between S30 and L30 products, we introduce a refined approach that fully integrates a Self-Supervised Learning (SSL)-pretrained encoder into a U-Net architecture. This method aims to access multi-scale features and improve spatial reconstruction accuracy, addressing the limitations in spatial resolution observed in our earlier study. Our methodology incorporates a comprehensive ablation study to assess various SSL-pretrained backbone architectures. Preliminary results demonstrate significant improvements in spatial reconstruction accuracy compared to our previous work. The adapted U-Net architecture, leveraging SSL-pretrained encoders, shows enhanced capability in capturing intricate spatial features within the HLS dataset. Our experiments demonstrate a substantial improvement in spatial resolution and feature reconstruction for L30 products, particularly in bands not natively present in Landsat data, paving the way for more accurate multi-sensor analyses.

Sustainable Development of Production–Living–Ecological Spaces: Insights from a 30-Year Remote Sensing Analysis

The rapid pace of urbanization and industrialization has reshaped land use patterns globally, particularly within the interconnected domains of ‘production, living, and ecological spaces’ (PLES). Understanding the spatiotemporal evolution of these spaces is crucial for guiding sustainable development. Although a number of previous studies have explored aspects of their dynamics and driving factors, further investigation is needed to fully understand their long-term spatiotemporal evolution and the broader influences of socio-economic and environmental forces. This study aims to fill that important gap by leveraging advanced remote sensing techniques to analyze PLES transformations over a 30-year period. Using Henan Province, China, as a testbed, this study applies high-resolution Landsat data, land use transition matrices, dynamic degree analysis, Principal Component Analysis (PCA), and multiple linear regressions to uncover trends and underlying drivers. The results reveal a substantial reduction in production spaces by 3394.62 km² steady growth in living spaces by 4459.41 km² and complex, non-linear changes in ecological spaces, which decreased by 1067.43 km². Key driving forces, such as economic growth, urbanization, and fiscal policies are identified and discussed. These insights provide a robust framework for sustainable land use planning, with broader implications for rapidly urbanizing regions worldwide.