[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2064":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":22,"tags":24,"view_count":30,"doi":31,"paper":32,"created_at":52},2064,"Spatial modeling of subsurface soil texture in semi-arid regions: Evaluating pure machine learning against hybrid regression kriging using Sentinel-1 and Sentinel-2 data","https:\u002F\u002Fdoi.org\u002F10.12912\u002F27197050\u002F235746","Accurate mapping of subsurface soil texture is important for sustainable land management and precision agriculture in semi-arid environments, where soil observations are often spatially sparse.This study evaluates a multitemporal Digital Soil Mapping framework combining wet-season Sentinel-1 synthetic aperture radar (SAR) and dry-season Sentinel-2 optical observations for mapping clay, silt, and sand fractions at 30-40 cm depth in the Tal Kaif district of northern Iraq.Four hyperparameter-optimized machine-learning algorithms -Random Forest (RF), extreme gradient boosting (XGBoost), support vector regression (SVR), and artificial neural network (ANN\u002F MLP) -were benchmarked using strict five-fold out-of-fold validation.Pure machine-learning models showed limited independent predictive performance for all three fractions, with negative R² values and RPD values below 1.0.The best pure-ML models achieved RMSE values of 9.726% for clay, 9.201% for silt, and 6.890% for sand.Residual variograms demonstrated moderate to strong spatial dependence, supporting the incorporation of spatially structured residuals through Regression Kriging (RK).Hybrid RK produced fraction-specific effects: RMSE decreased slightly for clay (9.726% to 9.689%) and more clearly for sand (6.890% to 6.702%), whereas silt performance deteriorated slightly (9.201% to 9.334%).The results therefore provide partial support for the hypothesis that geostatistical modelling can complement pure machine learning, but do not demonstrate a universal improvement from RK.The principal limitation is the indirect relationship between surface remote-sensing observations and subsurface texture at 30-40 cm depth, together with the sparse sampling network.Nevertheless, the resulting spatially continuous maps identified a predominance of fine-textured classes, particularly silty clay loam and silty clay, providing spatial information relevant to irrigation planning, precision agriculture, and soil-management decisions.The study contributes evidence that integrating environmental feature-space modelling with spatial residual structure can provide a more spatially informed framework for subsurface soil-texture mapping in data-sparse semi-arid environments, while highlighting the need for fraction-specific evaluation and independent validation in future studies.","在半干旱环境中，地下土壤质地的精确制图对可持续土地管理和精准农业具有重要意义，而这类地区的土壤观测数据通常在空间上较为稀疏。本研究评估了一种多时相数字土壤制图框架，该框架结合湿季Sentinel-1合成孔径雷达（SAR）和干季Sentinel-2光学观测数据，用于绘制伊拉克北部Tal Kaif地区30–40 cm深度黏粒、粉粒和砂粒含量。研究采用严格的五折交叉验证（out-of-fold）对四种经超参数优化的机器学习算法——随机森林（RF）、极端梯度提升（XGBoost）、支持向量回归（SVR）和人工神经网络（ANN\u002FMLP）——进行了基准测试。纯机器学习模型对三种粒级的独立预测性能均有限，R²为负值，RPD低于1.0。最佳纯机器学习模型的RMSE分别为：黏粒9.726%、粉粒9.201%、砂粒6.890%。残差变异函数表现出中等至强的空间依赖性，支持通过回归克里金（RK）纳入具有空间结构的残差。混合RK产生了粒级特异性效果：黏粒的RMSE略有下降（9.726%降至9.689%），砂粒下降更为明显（6.890%降至6.702%），而粉粒性能略有恶化（9.201%升至9.334%）。因此，研究结果部分支持了地统计建模可以补充纯机器学习的假设，但并未证明RK能带来普遍性改善。主要局限性在于地表遥感观测与30–40 cm深度地下质地之间的间接关系，以及采样网络稀疏。尽管如此，所生成的空间连续图件识别出细质地类别占主导，尤其是粉质黏壤土和粉质黏土，为灌溉规划、精准农业和土壤管理决策提供了相关空间信息。本研究提供了证据表明，将环境特征空间建模与空间残差结构相结合，可为数据稀疏的半干旱环境中地下土壤质地制图提供更具空间信息的框架，同时强调未来研究需要进行粒级特异性评估和独立验证。",null,"Ecological Engineering & Environmental Technology","2026-09-08T00:00:00Z","论文",10,false,60,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":17,"relevant":20,"comment":21},8,18,16,1,"基于Sentinel-1\u002F2多时相遥感的半干旱区地下土壤质地制图研究，方法对比严谨但结论为部分支持，属细分领域方法学进展，公共价值有限。",[23],{"name":10,"url":6},[25,26,27,28,29],"机器学习","精准农业","遥感","数字土壤","土壤制图",0,"10.12912\u002F27197050\u002F235746",{"doi":31,"openalex_id":33,"authors":34,"venue":10,"cited_by_count":30,"oa_url":44,"card":45,"direction":49,"ingested_from":51},"W7211972432",[35,38,41],{"name":36,"orcid":37},"Riyad Hazem Zubair","https:\u002F\u002Forcid.org\u002F0009-0000-0947-2184",{"name":39,"orcid":40},"Muntadher Aidi Shareef","https:\u002F\u002Forcid.org\u002F0000-0002-9089-652X",{"name":42,"orcid":43},"Abdelmalek Toumi","https:\u002F\u002Forcid.org\u002F0000-0001-8415-0871","https:\u002F\u002Fwww.ecoeet.com\u002Fpdf-235746-144067?filename=Spatial-modeling-of-subsu.pdf",{"tldr":46,"method":47,"finding":48,"direction":49,"opportunity":50},"用Sentinel-1\u002F2多时相数据结合纯机器学习与回归克里金，制图半干旱区30-40cm土壤质地。","Sentinel-1 SAR与Sentinel-2光学数据，RF\u002FXGBoost","纯ML预测能力有限（R²为负），回归克里金仅对黏粒和砂粒略有改善，粉粒反而变差。","农业遥感与作物表型","表层遥感与深层土壤质地关系间接，可探索深度函数建模、多源数据融合与分粒级独立验证方法。","openalex","2026-09-10T23:30:30.335801Z"]