Search Results (494)

Accurate soil maps are essential for soil protection, management, and digital agriculture. However, traditional soil maps often lack the detail required for local applications, while farm-scale surveys are often not economically viable. This study uses legacy soil data and digital soil mapping (DSM) to produce accurate, low-cost maps of key soil properties, namely clay, sand, total lime (CaCO₃), organic carbon (SOC), total nitrogen (TN), and the cation-exchange capacity (CEC). The DSM procedure involved multivariate stepwise regression kriging that uses the terrain attributes and bare soil reflectance composite (SRC) from Sentinel-2 multitemporal images. The procedure to obtain the SRC was carried out following the Soil Composite Mapping Processor (SCMaP) methodology. The Sentinel-2 bands of the SRC showed strong correlations with soil features, making them very suitable explicative variables for regression kriging. In particular, the SWIR bands (b11 and b12) were important covariates in predicting clay, sand, and CEC maps. The accuracy of the regression models was very good for clay, sand, SOC, and CEC (R² > 0.90), while CaCO₃ showed lower accuracy (R² = 0.67). Normalization of SOC, TN, and CaCO₃ did not significantly improve the prediction accuracy, except for SOC, which showed a slight improvement. In addition, a supervised classification approach was applied to predict soil typological units (STUs) using the mapped soil attributes. This methodology demonstrates the potential of SRCs and regression kriging to produce detailed soil property maps to support precision agriculture and sustainable land management. Full article

Land use and land cover change (LULCC) is crucial in sustainable land management. Over the past four decades, the Min River Basin (MRB) has experienced significant LULCC. This study investigated the dynamics of LULCC over these decades (1980–2020) and discusses the key drivers of land use change in different stages. First, we mapped and quantified changes (i.e., LULCC and landscape indices) in forests, croplands, urban areas, and water bodies from 1980 to 2020 using the China National Land Use/Cover Change (CNLUCC) and ArcGIS Pro 2.3. Second, by analyzing existing policies, we categorized four decades of LULCC trends from 1980 to 2020, delineating three distinct stages: (1) the Economic Restoration (ER) stage (1978–1989), when the ecological impacts of LULCC on forests, croplands, and water bodies received limited policy attention; (2) the Construction of Ecological Protection and Economic Development (EPED) stage (1989–2012), which saw a significant increase in forest coverage, primarily driven by various central and provincial environmental conservation policies, such as the Green for Grain and the “Three-Five-Seven Reforestation Project” in Fujian; and (3) the Ecological Civilization (EC) stage (2012–2020), in which policy focus shifted from expanding forest land areas to enhancing the quality of these areas. However, the cropland area has decreased due to urbanization policies and population migration from rural to urban areas, including the above-mentioned forest policies. Thus, this study highlights the complex relationship between different land use land cover policies, as some policies had synergistic effects between the policies and positive outcomes, while other policies showed conflicting outcomes. Our results emphasize the importance of integrated land and water resource management and provide insights for policymakers to balance development and environmental conservation policies in the MRB. Full article

Monitoring the dynamic distribution of irrigated cropland and assessing its cooling effects are essential for advancing sustainable agriculture amid climate change. This study presents an integrated framework for irrigated cropland monitoring and cooling effect assessment. Leveraging dense time series vegetation indices with Google Earth Engine (GEE), we evaluated multiple machine learning algorithms within to identify the most robust approach (random forest algorithm) for mapping irrigated cropland in Inner Mongolia from 2010 to 2020. Furthermore, we developed an effective method to quantify the diurnal, seasonal, and interannual cooling effects of irrigation. Our generated irrigated cropland maps demonstrate high accuracy, with overall accuracy ranging from 0.85 to 0.89. This framework effectively captures regional cropland expansion patterns, revealing a substantial increase in irrigated cropland across Inner Mongolia by 27,466.09 km² (about +64%) between 2010 and 2020, with particularly pronounced growth occurring after 2014. Analysis reveals that irrigated cropland lowered average daily land surface temperature (LST) by 0.25 °C compared to rain-fed cropland, with the strongest cooling effect observed between July and August by approximately 0.64 °C, closely associated with increased evapotranspiration. Our work highlights the potential of satellite-based irrigation monitoring and climate impact analysis, offering a valuable tool for supporting climate-resilient agriculture practices. Full article

Satellite remote sensing enables monitoring of regenerative agriculture practices, such as crop rotation, cover cropping, and conservation tillage to allow tracking and quantification at unprecedented scales. The Monitor system presented here capitalizes on the scope and scale of these data by integrating crop identification, cover cropping, and tillage intensity estimations annually at field scales across the contiguous United States (CONUS) from 2014 to 2023. The results provide the first ever mapping of these practices at this temporal fidelity and spatial scale, unlocking valuable insights for sustainable agricultural management. Monitor incorporates three datasets: CropID, a deep learning transformer model using Sentinel-2 and USDA Cropland Data Layer (CDL) data from 2018 to 2023 to predict annual crop types; the living root data, which use Normalized Difference Vegetation Index (NDVI) data to determine cover crop presence through regional parameterization; and residue cover (RC) data, which uses the Normalized Difference Tillage Index (NDTI) and crop residue cover (CRC) index to assess tillage intensity. The system calculates field-scale statistics and integrates these components to compile a comprehensive field management history. Results are validated with 35,184 ground-truth data points from 19 U.S. states, showing an overall accuracy of 80% for crop identification, 78% for cover crop detection, and 63% for tillage intensity. Also, comparisons with USDA NASS Ag Census data indicate that cover crop adoption rates were within 20% of estimates for 90% of states in 2017 and 81% in 2022, while for conventional tillage, 52% and 25% of states were within 20% of estimates, increasing to 75% and 67% for conservation tillage. Monitor provides a comprehensive view of regenerative practices by crop season for all of CONUS across a decade, supporting decision-making for sustainable agricultural management including associated outcomes such as reductions in emissions, long term yield resiliency, and supply chain stability. Full article

Mixed pixels often hinder accurate cropland mapping from remote sensing images with coarse spatial resolution. Image spatial super-resolution reconstruction technology is widely applied to address this issue, typically transforming coarse-resolution remote sensing images into fine spatial resolution images, which are then used to generate fine-resolution land cover maps using classification techniques. Deep learning has been widely used for image spatial super-resolution reconstruction; however, collecting training samples is often difficult for cropland mapping. Given that the quality of spatial super-resolution reconstruction directly impacts classification accuracy, this study aims to assess the impact of different types of training samples on image spatial super-resolution reconstruction and cropland mapping results by employing a Residual Channel Attention Network (RCAN) model combined with a spatial attention mechanism. Four types of samples were used for spatial super-resolution reconstruction model training, namely fine-resolution images and their corresponding coarse-resolution images, including original Sentinel-2 and degraded Sentinel-2 images, original GF-2 and degraded GF-2 images, histogram-matched GF-2 and degraded GF-2 images, and registered original GF-2 and Sentinel-2 images. The results indicate that the samples acquired by the histogram-matched GF-2 and degraded GF-2 images can resolve spectral band mismatches when simulating training samples from fine spatial resolution imagery, while the other three methods have limitations in their inability to fully address spectral and spatial mismatches. The histogram-matched method yielded the best image quality with PSNR, SSIM, and QNR values of 42.2813, 0.9778, and 0.9872, respectively, and produced the best mapping results, achieving an overall accuracy of 0.9306. By assessing the impact of training samples on image spatial super-resolution reconstruction and classification, this study addresses data limitations and contributes to improving the accuracy of cropland mapping, which is crucial for agricultural management and decision-making. Full article

Rapid urbanization causes a high concentration of human population and economic activities that lead to the changes in landscape and spatial growth of the cities. Landscape features play a key role in understanding the land use and land cover (LULC) dynamics of urban areas. This work aims to analyze and quantify the changes in LULC over 24 years (1999 to 2023) in Lucknow District of India. It focuses on different land use types, including built-up area, cropland, water body, vegetation, and fallow land, using satellite imagery. Multi-temporal Landsat satellite data from the years 1999, 2008, 2015, and 2023 were employed to prepare LULC maps including major classes, namely built-up area, cropland, water body, vegetation, and fallow land. Several landscape metrics, such as number of patches (NP), patch density (PD), largest patch index (LPI), landscape shape index (LSI), edge density (ED), and total edge (TE), were calculated to analyze spatial patterns and changes in LULC categories. The study revealed significant changes in the landscape of Lucknow District, characterized by variations in the extent and distribution of the land use categories. Key findings include a remarkable increase in built-up area from 9.04% in 1999 to 25.91% in 2023 and a decrease in vegetation from 26.01% in 1999 to 11.71% in 2023. The PD and ED showed an increased fragmentation, especially in built-up areas where PD increased from 9.18 patches/100 ha in 1999 to 11.85 patches/100 ha in 2023. The LPI for built-up areas significantly grew, indicating larger continuous urban regions. The findings of this study emphasize the importance of monitoring landscape changes using multi-temporal remote sensing images over urban landscapes. Analyzing landscape metrics helps to understand the ongoing changes in LULC, providing essential information for effective sustainable land management practices. Full article

Climate change is expected to alter the environmental suitability of land use and land cover (LULC) classes globally. In this study, we investigated the potential impacts of climate change on the environmental suitability of the most representative LULC classes in the southern Brazilian semiarid region. We employed the Random Forest algorithm trained with climatic, soil, and topographic data to project future LULC suitability under the Representative Concentration Pathway RCP 2.6 (optimistic) and 8.5 (pessimistic) scenarios. The climate data included the mean annual air temperature and precipitation from the WorldClim2 platform for historical (1970–2000) and future (2061–2080) scenarios. Soil data were obtained from the SoilGrids 2.1 digital soil mapping platform, while topographic data were produced by NASA’s Shuttle Radar Topography Mission (SRTM). Our model achieved an overall accuracy of 60%. Under the worst-case scenario (RCP 8.5), croplands may lose approximately 8% of their suitable area, while pastures are expected to expand by up to 30%. Areas suitable for savannas are expected to increase under both RCP scenarios, potentially expanding into lands historically occupied by forests, grasslands, and eucalyptus plantations. These projected changes may lead to biodiversity loss and socioeconomic disruptions in the study area. Full article

To achieve the regional goal of “double carbon”, it is necessary to map the carbon stock prediction for a wide area accurately and in a timely fashion. This paper introduces a long- and short-term memory network algorithm called the Self-Attention Convolutional Long and Short-Term Memory Network (SA-ConvLSTM). This paper takes the Wuhan urban circle of China as the research object, establishes a carbon stock AI prediction model, constructs a carbon stock change evaluation system, and investigates the correlation between carbon stock change and land use change during urban expansion. The results demonstrate that (1) the overall accuracy of the ConvLSTM and SA-ConvLSTM models improved by 4.68% and 4.70%, respectively, when compared to the traditional metacellular automata prediction methods (OS-CA, Open Space Cellular Automata Model), and for small sample categories such as barren land, shrubs, and grassland, the accuracy of SA-ConvLSTM increased by 17.15%, 43.12%, and 51.37%, respectively; (2) from 1999 to 2018, the carbon stock in the Wuhan urban area showed a decreasing trend, with an overall decrease of 6.49 × 10⁶ MgC. The encroachment of arable land due to rapid urbanization is the main reason for the decrease in carbon stock in the Wuhan urban area. From 2018 to 2023, the predicted value of carbon stock in the Wuhan urban area was expected to increase by 9.17 × 10⁴ MgC, mainly due to the conversion of water bodies into arable land, followed by the return of cropland to forest; (3) the historical spatial error model (SEM) indicates that for each unit decrease in carbon stock change, the Single Land Use Dynamic Degree (SLUDD) of water bodies and impervious surfaces will increase by 119 and 33 units, respectively. For forests, grasslands, and water bodies, the future spatial error model (SEM) indicated that for each unit increase in carbon stock change, the SLUDD would increase by 55, 7, and −305 units, respectively. This study demonstrates that we can use deep neural networks as a new method for predicting land use expansion, revealing the key impacts of land use change on carbon stock change from both historical and future perspectives and providing valuable insights for policymakers. Full article

Urbanization, despite driving regional economic growth, has led to significant disparities in development levels among cities. Many studies have made valuable suggestions for ecological conservation in economically underdeveloped regions. However, for medium-level cities with large economic development needs, the question of how to strike a balance between development and conservation in land development patterns is a critical issue to be addressed. By integrating ecosystem services assessment models and land use prediction models, we proposed a framework for guiding future land-use strategies based on ecosystem service values, using Jiaxing City as a case study. Firstly, we assessed and mapped the current status of ecosystem services value. Then, we simulated the land use distribution pattern and ecosystem services value under three development strategies: inertial development, cropland protection, and ecological development. Eventually, we determined the optimal urban land development pattern. The results showed that the total ecosystem service value for Jiaxing is CNY 124.82 billion, with climate regulation, water conservation, and flood mitigation contributing the most. The ecological development strategy yields the highest service value, with a 0.81% increase compared to the current situation, while the cropland protection and inertial development strategies result in decreases of 0.73% and 10.93%, respectively. Furthermore, the ecological strategy expands high-value service areas, concentrated in the northern river network and southern hilly regions. These findings offer valuable insights for urban planners and policymakers in formulating sustainable strategies and integrating ecosystem service values into economic policies to promote urban development. Full article

Land cover maps with high accuracy are essential for environmental protection and climate change research. The 30-meter-resolution maps, with their better resolution and longer historical records, are extensively utilized to assess changes in land cover and their effects on carbon storage, land–atmosphere energy balance, and water cycle processes. However, current data products use different classification methods, resulting in significant classification inconsistency and triggering serious disagreements among related studies. Here, we compared four mainstream land cover products in China, namely GLC_FCS30, CLCD, Globeland30, and CNLUCC. The result shows that only 50.34% of the classification results were consistent across the four datasets. The differences between pairs of datasets ranged from 21.10% to 37.53%. Importantly, most inconsistency occurs in transitional zones among land cover types sensitive to climate change and human activities. Based on the accuracy evaluation, CLCD is the most accurate land cover product, with an overall accuracy reaching 86.98 ± 0.76%, followed by CNLUCC (81.38 ± 0.87%) and GLC_FCS30 (77.83 ± 0.80%). Globeland30 had the lowest accuracy (75.24 ± 0.91%), primarily due to misclassification between croplands and forests. Misclassification diagnoses revealed that vegetation-related spectral confusion among land cover types contributed significantly to misclassifications, followed by slope, cloud cover, and landscape fragmentation, which affected satellite observation angles, data availability, and mixed pixels. Automated classification methods using the random forest algorithm can perform better than those that depend on traditional human–machine interactive interpretation or object-based approaches. However, their classification accuracy depends more on selecting training samples and feature variables. Full article

In the northwestern Highlands of Ethiopia, a region characterized by diverse ecosystems, significant land use and land cover (LULC) changes have occurred due to a combination of environmental fragility and human pressures. The implications of these changes for ecosystem service values remain underexplored. This study quantifies the impact of LULC changes, with an emphasis on the expansion of plantation forests, on ecosystem service values in monetary terms to promote sustainable land management practices. Using Landsat images and the Random Forest algorithm in R, LULC patterns from 1985 to 2020 were analyzed, with the ecosystem service values estimated using locally adapted coefficients. The Random Forest classification demonstrated a high accuracy, with values of 0.97, 0.98, 0.96, and 0.97 for the LULC maps of 1985, 2000, 2015, and 2020, respectively. Croplands consistently dominated the landscape, accounting for 53.66% of the area in 1985, peaking at 67.35% in 2000, and then declining to 52.86% by 2020. Grasslands, initially the second-largest category, significantly decreased, while wetlands diminished from 14.38% in 1985 to 1.87% by 2020. Conversely, plantation forests, particularly Acacia decurrens, expanded from 0.4% of the area in 2000 to 28.13% by 2020, becoming the second-largest land cover type. The total ecosystem service value in the district declined from USD 219.52 million in 1985 to USD 39.23 million in 2020, primarily due to wetland degradation. However, plantation forests contributed USD 17.37 million in 2020, highlighting their significant role in restoring ecosystem services, particularly in erosion control, soil formation, nutrient recycling, climate regulation, and habitat provision. This study underscores the need for sustainable land management practices, including wetland restoration and sustainable plantation forestry, to enhance ecosystem services and ensure long-term ecological and economic sustainability. Full article

Acquiring the spatiotemporal patterns of cropland disturbance is of great significance for regional sustainable agricultural development and environmental protection. However, effective monitoring of cropland disturbances remains a challenge owing to the complexity of the terrain landscape and the reliability of the training samples. This study integrated automatic training sample generation, random forest classification, and the LandTrendr time-series segmentation algorithm to propose an efficient and reliable medium-resolution cropland disturbance monitoring scheme. Taking the Amur state of Russia in the Amur river basin, a transboundary region between Russia and China in east Asia with rich agriculture resources as research area, this approach was conducted on the Google Earth Engine cloud-computing platform using extensive remote-sensing image data. A high-confidence sample dataset was then created and a random forest classification algorithm was applied to generate the cropland classification probabilities. LandTrendr time-series segmentation was performed on the interannual cropland classification probabilities. Finally, the identification, spatial mapping, and analysis of cropland disturbances in Amur state were completed. Further cross-validation comparisons of the accuracy assessment and spatiotemporal distribution details demonstrated the high accuracy of the dataset, and the results indicated the applicability of the method. The study revealed that 2815.52 km² of cropland was disturbed between 1990 and 2021, primarily focusing on the southern edge of the Amur state. The most significant disturbance occurred in 1991, affecting 1431.48 km² and accounting for 50.84% of the total disturbed area. On average, 87.98 km² of croplands are disturbed annually. Additionally, 2495.4 km² of cropland was identified as having been disturbed at least once during the past 32 years, representing 83% of the total disturbed area. This study introduced a novel approach for identifying cropland disturbance information from long time-series probabilistic images. This methodology can also be extended to monitor the spatial and temporal dynamics of other land disturbances caused by natural and human activities. Full article

Eurasian dryland ecosystems consist mainly of cropland and grassland, and their changes are driven by both natural factors and human activities. This study utilized the normalized difference vegetation index (NDVI), gross primary productivity (GPP) and solar-induced chlorophyll fluorescence (SIF) to analyze the changing characteristics of vegetation activity in Eurasia over the past two decades. Additionally, we integrated the mean annual temperature (MAT), the mean annual precipitation (MAP), the soil moisture (SM), the vapor pressure deficit (VPD) and the terrestrial water storage (TWS) to analyze natural factors’ influence on the vegetation activity from 2003 to 2022. Through partial correlation and residual analysis, we quantitatively described the contributions of both natural and human factors to changes in vegetation activity. The results indicated an overall increasing trend in vegetation activity in Eurasia; the growth rates of vegetation greenness, productivity and photosynthetic capacity were 1.00 × 10⁻³ yr⁻¹ (p < 0.01), 1.30 g C m⁻² yr⁻² (p < 0.01) and 1.00 × 10⁻³ Wm⁻²μm⁻¹sr⁻¹yr⁻¹ (p < 0.01), respectively. Furthermore, we found that soil moisture was the most important natural factor influencing vegetation activity. Human activities were identified as the main driving factors of vegetation activity in the Eurasian drylands. The relative contributions of human-induced changes to NDVI, GPP and SIF were 52.45%, 55.81% and 74.18%, respectively. These findings can deepen our understanding of the impacts of current natural change and intensified human activities on dryland vegetation coverage change in Eurasia. Full article

Reliable and continuous information on cropping intensity is crucial for assessing cropland utilization and formulating policies regarding cropland protection and management. However, there is still a lack of high-resolution cropping intensity maps for recent years, particularly in fragmented agricultural regions. In this study, we combined Landsat-8 and Sentinel-2 imagery to generate cropping intensity maps from 2019 to 2023 at a 10 m resolution for Shaanxi Province, China. First, the satellite imagery was harmonized to construct 10-day composite enhanced vegetation index (EVI) time series. Then, the cropping intensity was determined by counting the number of valid EVI peaks within a year. Assessment based on 578 sample points showed a high level of accuracy, with overall accuracy and Kappa coefficient values exceeding 0.96 and 0.93, respectively. We further analyzed the spatiotemporal patterns of cropping intensity and generated a map of abandoned cropland in Shaanxi. The results indicated that cropland in Shaanxi Province was mainly utilized for single-cropping (52.9% of area), followed by double-cropping (35.2%), with non-cropping accounting for 11.9%. Cropping intensity tended to be lower in the north and higher in the south. Temporally, the average cropping intensity of Shaanxi increased from 1.1 to over 1.3 from 2019 to 2023. Despite this upward trend, large areas of cropland were abandoned in northern Shaanxi. These results demonstrate the potential of utilizing Landsat-8 and Sentinel-2 imagery to identify cropping intensity dynamics in fragmented agricultural regions and to guide more efficient cropland management. Full article

Jointly building the Economic Corridor of the Eurasia Continent (ECEC), which is one of the most important parts of the Silk Road Economic Belt, is a pivotal initiative for fostering regional development. Forests, which serve as a green foundation of economic resilience, underpin this effort. However, there is an imbalance in ecological status due to differences in natural resources and the social economy along the economic corridor. This imbalance has led to alterations in landscapes, yet the specific changes and their underlying relationships are still much less understood. Here, we quantitatively detected changes in the forest landscape and its ecological efforts over the ECEC via widespread, satellite-based and long-term land cover maps released by the European Space Agency (ESA) Climate Change Initiative (CCI). Specifically, the coupling between changes in forest coverage and landscape patterns, e.g., diversity, was further examined. The results revealed that forest coverage fluctuated and declined over the ECEC from 1992 to 2018, with an overall reduction of approximately 9784.8 km² (i.e., 0.25%). Conversions between forests and other land cover types were widely observed. The main displacements occurred between forests and grasslands/croplands (approximately 48%/21%). Moreover, the landscape diversity in the study area increased, as measured by the effective diversity index (EDI), during the study period, despite obvious spatial heterogeneity. Notably, this pattern of landscape diversity was strongly associated with forest displacement and local urban development through coupling analysis, consequently indicating increasing fragmentation rather than biological diversity. This study highlights the coupled relationship between quantitative and qualitative changes in landscapes, facilitating our understanding of environmental protection and policy management. Full article