Search Results (1,086)

The relocation of residents from high-risk areas is a critical measure to address safety and development issues in the floodplain regions of Henan Province in China. Whether the old villages can be reclaimed as farmland after demolition concerns Henan Province’s ability to maintain its farmland red line. This paper integrated multiple remote sensing indices and proposed a remote sensing identification method for monitoring the progress status of village relocation and reclamation that adapted to data characteristics and application scenarios. Firstly, it addressed the issue of missing target bands in GF-2 (GaoFen-2) by employing a band downscaling method; secondly, it combined building and vegetation indices to identify changes in land cover in the old villages within the floodplain, analyzing the implementation effects of the relocation and reclamation policies. Results showed that using a Random Forest regression model to generate a 4 m resolution shortwave infrared band not only retains the original target band information of Landsat-8 but also enhances the spatial detail of the images. Based on the optimal thresholds of multiple remote sensing indices, combined with human footprint data and POI (Points of Interest) identified village boundaries, the overall accuracy of identifying the progress status of resident relocation and reclamation reached 93.5%. In the floodplain region of Henan, the implementation effect of resident relocation was relatively good, with an old village demolition rate of 77%, yet the farmland reclamation rate was only 23%, indicating significant challenges in land conversion, lagging well behind the pilot program schedule requirements. Overall, this study made two primary contributions. First, to distinguish between rural construction and bare soil, thereby improving the accuracy of construction land extraction, an Enhanced Artifical Surface Index (EASI) was proposed. Second, the monitoring results of land use changes were transformed from pixel-level to village-level, and this framework can be extended to other specific land use change monitoring scenarios, demonstrating broad application potential. Full article

Bare soil (BS) identification through satellite remote sensing can potentially play a critical role in understanding and managing soil properties essential for climate regulation and ecosystem services. From 191 papers, this review synthesises advancements in BS detection methodologies, such as threshold masking and classification algorithms, while highlighting persistent challenges such as spectral confusion and inconsistent validation practices. The analysis reveals an increasing reliance on satellite data for applications such as digital soil mapping, land use monitoring, and environmental impact mapping. While multispectral sensors like Landsat and Sentinel dominate current methodologies, limitations remain in distinguishing BS from spectrally similar surfaces, such as crop residues and urban areas. This review emphasises the critical need for robust validation practices to ensure reliable estimates. By integrating technological advancements with improved methodologies, the potential for accurate, large-scale BS detection can significantly contribute to combating land degradation and supporting global food security and climate resilience efforts. Full article

Conservation Agriculture practices in Organic Farming can enhance the sustainability of these farming systems. However, these practices have economic and environmental implications for farmers, which must be considered. In the present study, eight technical itineraries were compared in tomato cultivation. These differed in how reduced and no-tillage practices were used to manage four soil cover types and to control weeds. The itinerary’s gross salable production (GSP), gross income (GI), and CO₂ emissions were evaluated. In the second growing season, the no-tillage itinerary values of both GSP and GI were lower than those based on reduced tillage (34,681.03 and 71,891.58 EUR ha⁻¹, respectively). The use of cover crops tendentially resulted in an increase in GSP in both growing seasons compared to cultivation on bare soil (8190.00 and 41,959.89 EUR ha⁻¹ in 2020 and 2021, respectively), particularly with clover monoculture and a clover–rye mix in 2020 (25,326.60 and 25,818.97 EUR ha⁻¹, respectively) and with clover monoculture in 2021 (69,310.18 EUR ha⁻¹). A similar trend was also observed for GI. Cover crop adoption was related to a higher CO₂ emissions (642.73 and 234.84 kg ha⁻¹ in 2020 and 353.23 and 213.30 kg ha⁻¹ in 2021, for itineraries based on reduced-tillage and no-tillage, respectively). Further studies could focus on the economic and environmental evaluation of these systems in the same pedoclimatic conditions but over the long term, quantifying the various environmental benefits of cover crops. Full article

This study investigated the impact of conventional ploughing (CT), minimum multitiller tillage (MT), and reduced loosening tillage (RT), with and without straw mulch on Fluvisol properties and crop yields in Croatia over three years (2019–2021). While conservation tillage practices are well studied in arid regions, our study addresses the unique challenges and benefits of these practices in humid conditions. Plots treated with straw mulch (2.75 t/ha) showed significant improvements in soil physical properties compared to bare plots. Penetration resistance (PR) decreased under 3-year mulch application in all tillage systems, with a reduction of up to 28% compared to bare plots. Water-holding capacity (WHC) was significantly higher in mulched MT (52.4%) than in bare CT (41.6%). Aggregate stability increased by 15–20% under mulch, with the highest stability in MT plots. Soil organic matter (SOM) peaked in mulched MT in 2021, reaching 4.5%, compared to 3.6% in bare CT. Yield results varied by crop: soybean yield was unaffected by tillage treatment but increased by 21% under mulch in MT; maize yield was highest in RT without mulch (13.95 t/ha); and spring wheat yield significantly improved in mulched MT (3.83 t/ha), compared to bare plots (1.75 t/ha). These findings highlight the synergistic benefits of non-inversion tillage and straw mulch in enhancing soil quality and crop yields, offering a sustainable management strategy for Central European agroecosystems. Full article

The objective of this study is to model backscattering coefficients over bare soils at intra-plot spatial scales (from almost 80 to 2800 m²), in a context where the plot is the reference spatial scale in most past studies. A statistical modeling approach, based on a random forest algorithm, is proposed to overcome the limits of semi-empirical or physical models pointed out in the literature and to reduce discrepancies observed between the satellite-derived backscattering coefficients and the predicted values. The experimental device was set up on a network of agricultural plots located in southwestern France during the Multispectral Crop Monitoring (MCM) experiment. The dataset combines high spatial resolution satellite images (acquired by TerraSAR-X and Radarsat-2) together with synchronous geo-located measurements of key soil parameters (i.e., top soil moisture, surface roughness, and soil texture) on consistent spatial areas. Backscattering coefficients are estimated at six intra-plot spatial scales (from ~80 to ~2800 m²), showing an exponential increase in modeling performance, and reaching higher levels of accuracy than previous work performed at the plot spatial scale (i.e., 50% of variance explained in the literature, in the best cases). The increase in signal quality with the spatial scale mainly explains the higher performance observed in the 2800 m² area, with a correlation of 0.91 and RMSE of 0.83 dB in the X-band (for backscattering coefficients acquired with the HH polarization state). In the C-band, the values of correlation range from 0.74 to 0.80, and the RMSE from 1.65 to 1.85 dB (depending on the considered polarization state). The results also showed that the developed statistical algorithm is mainly influenced by the surface roughness and the top soil moisture, as semi-empirical or physical-based models. Soil texture does not significantly affect the algorithm. Full article

This study assessed the drought susceptibility in Golestan Province, Northeastern Iran, using land use change modeling and climate projections from the CMIP6 framework, under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for 2030–2050. The development of current (2022) and future drought susceptibility maps was based on agrometeorological sample points and 14 environmental factors—such as land use, precipitation, mean temperature, soil moisture, and remote sensing-driven vegetation indices—used as inputs into a machine learning model, maximum entropy. The model showed a very robust predictive capacity, with AUCs for the training and test data of 0.929 and 0.910, thus certifying the model’s reliability. The current analysis identified major hotspots in Gomishan and Aqqala, where 66.12% and 36.12% of their areas, respectively, exhibited “very high” susceptibility. Projections under the SSP scenarios, particularly SSP5-8.5, indicate that the risk of drought will be the most severe in Maraveh Tappeh, where 72.09% of the area exhibits a “very high” risk. The results revealed that Golestan Province is at a crossroads. Rising temperatures, exceeding 35 °C in summer, combined with declining rainfall, intensify agricultural and hydrological droughts. These aggravated risks are compounded with land use transitions from rangelands to bare land, mostly in Aqqala and Gomishan, besides urban expansion in Bandar-e Torkman and Bandar Gaz, all of which face less groundwater recharge and increased surface runoff. Golestan’s drought vulnerability has both local and regional impacts, with its increased susceptibility affecting neighboring communities and ecosystems. Trade, migration, and ecological stresses linked to declining water resources may emerge as critical challenges, requiring regional collaboration for mitigation. Targeted interventions prioritizing sustainable land use practices, regional cooperation, and collaborative strategies are essential to address and mitigate these cascading risks and safeguard vulnerable communities. Full article

The uncontrolled expansion of mining activities has caused severe environmental impacts in semi-arid regions, endangering fragile ecosystems and water resources. This study aimed to propose a decision-making model to identify land use and land cover changes in the semi-arid region of Pernambuco, Brazil, caused by mining through a spatiotemporal analysis using high-resolution images from the PlanetScope satellite constellation. The methodology consisted of monitoring and evaluating environmental impacts using the k-Nearest Neighbors (kNN) algorithm, spectral indices (Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)), and hydrological data, covering the period from 2018 to 2023. As a result, a 3.28% reduction in vegetated areas and a 6.62% increase in urban areas were identified over five years, suggesting landscape transformation, possibly influenced by the expansion of mining and development activities. The application of kNN yielded an Overall Accuracy (OA) greater than 99% and a Kappa index of 0.98, demonstrating the effectiveness of the adopted methodology. However, challenges were encountered in distinguishing between constructions and bare soil, with the Jeffries–Matusita distance (JMD) analysis indicating a value below 0.34, while the similarity between water and vegetation highlights the need for more comprehensive training data. The results indicated that between 2018 and 2023, there was a marked degradation of vegetation and a significant increase in built-up areas, especially near water bodies. This trend reflects the intense human intervention in the region and reinforces the need for public policies aimed at mitigating these impacts, as well as promoting environmental recovery in the affected areas. This approach proves the potential of remote sensing and machine learning techniques to effectively monitor environmental changes, reinforcing strategies for sustainable management in mining areas. Full article

The accurate prediction of soil organic matter (SOM) content is important for sustainable agriculture and effective soil management. This task is particularly challenging due to the variability in factors influencing SOM distribution across different cultivated land types, as well as the site-specific responses of SOM to remote sensing data and environmental covariates, especially in the black soil region of northeastern China, where SOM exhibits significant spatial variability. This study evaluated the variations on the importance of different remote sensing imagery and environmental covariates in different cultivated land zones. A total of 180 soil samples (0–20 cm) were collected from Youyi County, Heilongjiang Province, China, and multi-year synthetic bare soil images from 2014 to 2022 (focusing on April and May) were acquired using Google Earth Engine. Combining three types of environmental covariates such as drainage, climate and topography, the study area was categorized into dry field and paddy field. Then, the SOM prediction model was constructed using random forest regression method and the accuracy of different strategies was evaluated by 10-fold cross-validation. The findings indicated that, (1) in the overall regression analysis, combining drainage and climate variables and multi-year synthetic remote sensing images of May could attain the highest prediction accuracy, and the importance of environmental covariates was ranked as follows: remote sensing (RS) > climate (CLI) > drainage (DN) > Topography (TP). (2) Zonal regression analysis was conducted with a high degree of precision, as evidenced by an R² of 0.72 and an impressively low RMSE of 0.73%. The time window for remote monitoring of SOM was different for dry field and paddy field. More specifically, the optimal time frames for SOM prediction in dryland were identified as April and May, while those for paddy fields were concentrated in May. (3) In addition, the importance of diverse environmental covariates was observed to vary with the cultivated land types. In regions characterized by intricate topography, such as dry fields, the contributions of remote sensing images and climate variables assumed a heightened importance. Conversely, in paddy fields featuring flat terrain, the roles of climate and drainage variables played a more substantial role in influencing the outcomes. These findings underscore the importance of selecting appropriate environmental inputs for improving SOM prediction accuracy. Full article

As a prominent agricultural country, China has widely implemented returning straw to the field in agricultural production. However, as the decomposition of straw progresses, the physical properties of the soil change, inevitably leading to alterations in the soil surface evaporation model. This study investigated the variations in soil evaporation rate, soil moisture content over 60 days after returning straw to the field, and bare soil through two leaching pond experiments. Through soil moisture retention curves at different degrees of decomposition, this study analyzed the impact of straw decomposition on soil’s water retention capacity. Based on measured data, this study formulated models for the soil surface evaporation resistance of bare soil and varying degrees of straw decomposition. With the comparison and contrast between the models, this study clarified the impact of straw decomposition on soil surface evaporation resistance. The main conclusions are the following: The moisture content of the surface soil decreases exponentially over time and, after 40 days of straw decomposition, the water content of the soil under decomposition is higher than that of bare soil. As the moisture content decreases, the cumulative evaporation from the soil increases linearly. The cumulative evaporation of the decomposed straw soil is lower than that of bare soil, with a relative reduction ranging from 3.08% to 32.2%. The straw decomposition significantly enhances the water retention capacity of the soil in the medium-to-high suction range. The straw decomposition enhances the evaporation resistance of the soil surface, and the greater the degree of decomposition, the more significant the enhancement effect. The research findings not only provide a scientific basis for agricultural water management, but also possess practical implications for guiding farmers to adopt more effective moisture retention measures. Full article

Desertification severely impacts soil environments, necessitating effective control measures to improve sandy soil. On the alpine sandy land of Gonghe Basin, taking bare land containing mobile sand dunes (LD) as a reference, surface soil undergoing four afforestation measures, namely Salix cheilophila + Populus simonii (WLYY), Salix psammophila + Salix cheilophila (SLWL), Artemisia ordosica + Caragana korshinskii (SHNT), and Caragana korshinskii (NT80), was studied, with soil physicochemical properties and enzyme activity measured and the bacterial community structure analyzed using Illumina high-throughput sequencing. Compared to LD, all four afforestation measures significantly reduced the sand content, while increasing soil total carbon, total nitrogen, organic matter, alkali-hydrolyzable nitrogen, and available potassium. WLYY, SLWL, and SHNT significantly increased the surface soil total phosphorus and total potassium. Catalase, sucrase, urease, and alkaline phosphatase activities significantly increased under all four measures. Among them, the highest improvements were observed under SLWL, followed by WLYY. All treatments increased soil bacterial community richness, exhibiting significantly different bacterial community compositions to those in LD. Total phosphorus was the key physicochemical factor affecting the soil bacterial community structure, while enzyme activity was significantly correlated with the relative abundance of most major bacterial phyla. All measures improved the surface soil environment, with SLWL demonstrating the best improvement. The results provide valuable reference for sand prevention and control strategies in alpine sandy areas and offer a theoretical basis for the ecological restoration of sandy soil microenvironments. Full article

The Cerrado domain, one of the richest on Earth, is among the most threatened in South America due to human activities, resulting in biodiversity loss, altered fire dynamics, water pollution, and other environmental impacts. Monitoring this domain is crucial for preserving its biodiversity and ecosystem services. This study aimed to apply machine learning techniques to classify the main vegetation formations of the Cerrado within the IBGE Ecological Reserve, a protected area in Brazil, using high-resolution PlanetScope imagery from 2021 to 2024. Three machine learning methods were evaluated: Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). A post-processing process was applied to avoid misclassification of forest in areas of savanna. After performance evaluation, the SVM method achieved the highest classification accuracy (overall accuracy of 97.51%, kappa coefficient of 0.9649) among the evaluated models. This study identified five main classes: grassland (GRA), savanna (SAV), bare soil (BS), samambaião (SAM, representing the superdominant species Pteridium esculentum), and forest (FOR). Over the three-year period (2021–2024), SAV and GRA formations were dominant in the reserve, reflecting the typical physiognomies of the Cerrado. This study successfully delineated areas occupied by the superdominant species P. esculentum, which was concentrated near gallery forests. The generated maps provide valuable insights into the vegetation dynamics within a protected area, aiding in monitoring efforts and suggesting potential new areas for protection in light of imminent anthropogenic threats. This study demonstrates the effectiveness of combining high-resolution satellite imagery with machine learning techniques for detailed vegetation mapping and monitoring in the Cerrado domain. Full article

Timely access to soil moisture distribution is critical for agricultural production. As an in-orbit L-band synthetic aperture radar (SAR), SAOCOM offers high penetration and full polarization, making it suitable for agricultural soil moisture estimation. In this study, based on the single-temporal coupled water cloud model (WCM) and Oh model, we first modified the WCM (MWCM) to incorporate bare soil effects on backscattering using SAR data, enhancing the scattering representation during crop growth. Additionally, the Oh model was revised to enable retrieval of both the surface layer (0–5 cm) and underlying layer (5–10 cm) soil moisture. SAOCOM data from 19 June 2022, and 23 June 2023 in Bei’an City, China, along with Sentinel-2 imagery from the same dates, were used to validate the coupled MWCM-Oh model individually. The enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and leaf area index (LAI), together with the radar vegetation index (RVI) served as vegetation descriptions. Results showed that surface soil moisture estimates were more accurate than those for the underlying layer. LAI performed best for surface moisture (RMSE = 0.045), closely followed by RVI (RMSE = 0.053). For underlying layer soil moisture, RVI provided the most accurate retrieval (RMSE = 0.038), while LAI, EVI, and NDVI tended to overestimate. Overall, LAI and RVI effectively capture surface soil moisture, and RVI is particularly suitable for underlying layers, enabling more comprehensive monitoring. Full article

Accurate assessment of forage quality is essential for ensuring optimal animal nutrition. Key parameters, such as Leaf Area Index (LAI) and grass coverage, are indicators that provide valuable insights into forage health and productivity. Accurate measurement is essential to ensure that livestock obtain the proper nutrition during various phases of plant growth. This study evaluated machine learning (ML) methods for non-invasive assessment of grassland development using RGB imagery, focusing on ryegrass and Timothy (Lolium perenne L. and Phleum pratense L.). ML models were implemented to segment and quantify coverage of live plants, dead material, and bare soil at three pasture growth stages (leaf development, tillering, and beginning of flowering). Unsupervised and supervised ML models, including a hybrid approach combining Gaussian Mixture Model (GMM) and Nearest Centroid Classifier (NCC), were applied for pixel-wise segmentation and classification. The best results were achieved in the tillering stage, with R² values from 0.72 to 0.97 for Timothy (α = 0.05). For ryegrass, the RGB-based pixel-wise model performed best, particularly during leaf development, with R² reaching 0.97. However, all models struggled during the beginning of flowering, particularly with dead grass and bare soil coverage. Full article

Hopea chinensis is a representative tree species in evergreen monsoon forests in the northern tropics, but it is currently in a critically endangered state due to destruction by human activities and habitat loss. In this study, we measured and analyzed the number of regenerating seedlings and habitat factors in wild populations of H. chinensis by combining field surveys with laboratory analysis. The aim of this study was to clarify the spatial distribution of H. chinensis seedlings and related factors to provide a scientific basis for conserving its germplasm resources and population restoration. In six populations, most size-class seedlings had aggregated distributions at three scales, and the intensity of aggregation decreased as the sample plot scale increased for most size-class seedlings. In the northern foothills of the Shiwandashan Mountains, size class I seedlings tended to be distributed in habitats with a higher rock bareness rate, whereas size class II and III seedlings tended to be distributed in habitats with a higher canopy density, thicker humus layers, and higher soil moisture content. In the southern foothills of the Shiwandashan Mountains, size class I and II seedlings tended to be distributed in habitats with higher available nitrogen contents, and size class III seedlings tended to be distributed in habitats with higher available nitrogen and soil moisture contents. Therefore, in the southern foothills of the Shiwandashan Mountains, the survival rate of H. chinensis seedlings can be improved by artificially adding soil to increase the thickness of the soil layer in stone crevices and grooves, regularly watering the seedlings during the dry season, and appropriately reducing the coverage of the shrub layer. In the northern foothills, the survival rate of H. chinensis seedlings can be enhanced by regularly applying nitrogen fertilizer and watering to increase the available nitrogen and soil moisture contents. Full article

In order to improve the current standard of analysis-ready Synthetic Aperture Radar (SAR) backscatter data, we introduce a machine learning-based approach to estimate the slope of the backscatter–incidence angle relationship from several backscatter statistics. The method requires information from radiometric terrain-corrected gamma nought time series and overcomes the constraints of a limited orbital coverage, as exemplified with the Sentinel-1 constellation. The derived slope estimates contain valuable information on scattering characteristics of different land cover types, allowing for the correction of strong forward-scattering effects over water bodies and wetlands, as well as moderate surface scattering effects over bare soil and sparsely vegetated areas. Comparison of the estimated and computed slope values in areas with adequate orbital coverage shows good overall agreement, with an average RMSE value of 0.1 dB/° and an MAE of 0.05 dB/°. The discrepancy between RMSE and MAE indicates the presence of outliers in the computed slope, which are attributed to speckle and backscatter fluctuations over time. In contrast, the estimated slope excels with a smooth spatial appearance. After correcting backscatter values by normalising them to a certain reference incidence angle, orbital artefacts are significantly reduced. This becomes evident with differences up to 5 dB when aggregating the normalised backscatter measurements over certain time periods to create spatially seamless radar backscatter composites. Without being impacted by systematic differences in the illumination and physical properties of the terrain, these composites constitute a valuable foundation for land cover and land use mapping, as well as bio-geophysical parameter retrieval. Full article