[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1392":3,"related-1392":103},{"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,"primary_topic":29,"secondary_topics":35,"classification":36,"search_phrases":76,"slug":79,"view_count":80,"doi":81,"paper":82,"created_at":102},1392,"Beyond soil quality mapping: integrating fertility indices and TOPSIS for sustainable land management in arid areas","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1930186","Introduction Soil ranks among a nation’s most valuable natural assets, and reliable understanding of local soil resources is essential for effective land-use planning and rural development. Methods This study set out to evaluate soil fertility and quality across different land management practices in Sohag Governorate, Egypt, combining geoscience methods with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). GPS-located sampling covered 34 points spanning old cultivated, recently cultivated, and bare soils, and standard laboratory procedures were used to determine their physical and chemical properties. TOPSIS was then applied to each sample to derive a current fertility status and an overall soil quality index (SQI). Results and discussion The soil aggregate stability index (SASI) averaged 1.81 (range 1.14–2.57) in old cultivated soils, 1.36 (0.2–3.62) in recently cultivated soils, and 0.4 (0.08–1.67) in bare soils; the soil-sealing index (IB) averaged 1.32 (0.17–4.35), 1.40 (0.11–5.10) and 0.57 (0.44–1.09) for the same three groups, and the Forestier index (IF) averaged 1.74 (0.46–4.39), 1.69 (0.36–5.28) and 1.03 (0.66–1.64). Four fertility classes emerged from this analysis, occupying 4.21, 15.22, 15.36 and 65.21% of the surveyed land. SQI itself ranged from 36.36 to 52.00% in old cultivated soils, 28.13–53.57% in recently cultivated soils, and 2.94–25.00% in bare soils, resolving into three quality regions: a moderate-quality zone spanning old and recently cultivated land (55.64% of the study area), a poor-quality zone across parts of the recently cultivated and bare soils (35.47%), and a very-poor-quality zone concentrated in bare soils (8.89%). Conclusion Together, these results provide decision-makers and land managers a practical, spatially explicit basis for prioritizing soil conservation work. In arid agricultural contexts, identifying and mapping fertility-depleted zones in this manner creates the evidence base necessary for targeted rehabilitation, which is crucial for closing the yield gap, reducing dependence on food imports, and enhancing national food security.","引言 土壤是一个国家最宝贵的自然资源之一，对当地土壤资源的可靠认识对于有效的土地利用规划和农村发展至关重要。方法 本研究旨在结合地球科学方法与逼近理想解排序法（TOPSIS），评估埃及索哈杰省不同土地管理方式下的土壤肥力与质量。GPS定位采样覆盖34个样点，涵盖老耕作土壤、新耕作土壤和裸地土壤，并采用标准实验室方法测定其物理和化学性质。随后对每个样本应用TOPSIS方法，得出当前肥力状况和综合土壤质量指数（SQI）。结果与讨论 土壤团聚体稳定性指数（SASI）在老耕作土壤中平均为1.81（范围1.14–2.57），在新耕作土壤中为1.36（0.2–3.62），在裸地土壤中为0.4（0.08–1.67）；土壤封闭指数（IB）在以上三组中平均分别为1.32（0.17–4.35）、1.40（0.11–5.10）和0.57（0.44–1.09）；福雷斯蒂尔指数（IF）平均分别为1.74（0.46–4.39）、1.69（0.36–5.28）和1.03（0.66–1.64）。分析得出四个肥力等级，分别占调查土地面积的4.21%、15.22%、15.36%和65.21%。SQI本身在老耕作土壤中范围为36.36%–52.00%，在新耕作土壤中为28.13%–53.57%，在裸地土壤中为2.94%–25.00%，可划分为三个质量区域：覆盖老耕作和新耕作土地的中等质量区（占研究区面积的55.64%）、覆盖部分新耕作和裸地土壤的差质量区（35.47%），以及集中在裸地土壤中的极差质量区（8.89%）。结论 综合来看，这些结果为决策者和土地管理者提供了实用且具有空间明确性的优先开展土壤保护工作的依据。在干旱农业背景下，以这种方式识别和绘制肥力耗竭区域，为有针对性的修复提供了必要的证据基础，这对于缩小产量差距、减少对粮食进口的依赖以及增强国家粮食安全至关重要。",null,"Frontiers in Sustainable Food Systems","2026-09-01T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":18,"authority":17,"freshness":19,"relevant":20,"comment":21},12,18,7,1,"论文提出结合TOPSIS的土壤质量评价方法，为干旱区土地可持续管理提供空间决策依据，具有一定方法创新和区域应用价值。",[23],{"name":10,"url":6},[25,26,27,28],"农业遥感","土地管理","干旱区","土壤质量",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":37,"locked":14,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":44},[42,49,57,64,71],{"code":30,"score":43,"supported":44,"title":45,"abstract":46,"tags":48},14,true,[31],[47,31],"土壤",[],{"code":50,"score":51,"supported":44,"title":52,"abstract":53,"tags":56},"A8",6,[],[54,55],"粮食安全","food security",[],{"code":58,"score":51,"supported":44,"title":59,"abstract":60,"tags":63},"G",[],[61,62],"农村发展","rural development",[],{"code":65,"score":66,"supported":44,"title":67,"abstract":68,"tags":70},"H7",4,[],[69],"干旱",[27],{"code":72,"score":20,"supported":14,"title":73,"abstract":74,"tags":75},"I3",[],[],[25],[77,78],"农业遥感 土地管理 土壤质量 干旱区","农业遥感 土地管理","农业遥感土地管理土壤质量干旱区-1392",3,"10.3389\u002Ffsufs.2026.1930186",{"doi":81,"openalex_id":83,"authors":84,"venue":10,"cited_by_count":93,"oa_url":94,"card":95,"direction":99,"ingested_from":101},"W7204930798",[85,87,89,91],{"name":86,"orcid":9},"Mohamed S. Shokr",{"name":88,"orcid":9},"Abdel-rahman A. Mustafa",{"name":90,"orcid":9},"Ahmed S. Abuzaid",{"name":92,"orcid":9},"Asmaa O. El-Sheikh",0,"https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1930186\u002Fpdf",{"tldr":96,"method":97,"finding":98,"direction":99,"opportunity":100},"结合地学方法与TOPSIS评估埃及干旱区不同土地利用下的土壤肥力与质量，绘制质量分区图。","GPS定位采样34点，实验室测定理化性质，用TOPSIS计算肥力状态与土壤质量指","老耕地质量中等，新耕地和裸地质量差，肥力贫瘠区占65%，需针对性修复。","农业遥感与作物表型","可扩展至遥感影像与机器学习结合，实现大范围土壤质量快速评估与动态监测。","openalex","2026-09-02T23:30:08.121321Z",{"total":51,"page":20,"page_size":51,"items":104},[105,197,278,365,437,502],{"id":106,"title":107,"url":108,"summary":109,"summary_zh":110,"content":9,"source_name":111,"source_url":108,"published_at":112,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":113,"score_detail":114,"sources":121,"tags":123,"primary_topic":128,"secondary_topics":134,"classification":140,"search_phrases":168,"slug":171,"view_count":93,"doi":172,"paper":173,"created_at":196},4898,"An Interpretable Deep Learning Framework for Spatial Prediction of Soil Salinity Using Multi-Source Remote Sensing and Environmental Data: A Case Study of Cangzhou, Hebei Province, China","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206465","Soil salinity poses a significant challenge to agricultural productivity and sustainable land management in coastal and semi-arid regions. Soil salinity in coastal agricultural regions is controlled by complex interactions among surface conditions, groundwater processes, soil properties, and terrain characteristics. Accurate spatial prediction requires integrating remote sensing observations with environmental information that represents these processes. This study presents a multi-source deep learning framework that integrates Sentinel-2-derived spectral indices with environmental covariates for regional soil salinity spatial prediction under the environmental conditions of the 2024 sampling period. SHapley Additive exPlanations (SHAP) is employed to quantify the contribution of environmental variables within the prediction framework and improve model interpretability. A convolutional neural network (CNN) incorporating a squeeze-and-excitation (SE) attention mechanism is developed as a nonlinear feature representation framework for integrating heterogeneous predictors, while the Crested Porcupine Optimizer (CPO) is used to optimize model hyperparameters. The proposed framework achieved the highest predictive performance among the benchmark models, with an R2 of 0.785, an RMSE of 0.928, and an MAE of 0.720. Integrating environmental covariates with spectral features further improves the prediction performance, increasing the R2 from 0.701 to 0.785. The results indicate the potential of integrating multi-source environmental information with interpretable deep learning for spatial prediction of near-surface soil salinity under similar coastal agricultural conditions.","土壤盐渍化对沿海和半干旱地区的农业生产力和可持续土地管理构成了重大挑战。沿海农业区域的土壤盐渍化受地表条件、地下水过程、土壤属性和地形特征之间复杂相互作用的控制。准确的空间预测需要将遥感观测与表征这些过程的环境信息相结合。本研究提出了一种多源深度学习框架，该框架将Sentinel-2衍生的光谱指数与环境协变量相结合，用于2024年采样期环境条件下的区域土壤盐渍化空间预测。采用SHapley加法解释（SHAP）方法量化预测框架中环境变量的贡献并提高模型可解释性。开发了一种融合压缩-激励（SE）注意力机制的卷积神经网络（CNN）作为非线性特征表示框架，用于整合异质性预测因子，同时使用冠豪猪优化器（CPO）优化模型超参数。所提出的框架在基准模型中取得了最高的预测性能，R²为0.785，RMSE为0.928，MAE为0.720。将环境协变量与光谱特征相结合进一步提高了预测性能，R²从0.701提升至0.785。结果表明，在类似的沿海农业条件下，将多源环境信息与可解释深度学习相结合用于近地表土壤盐渍化空间预测具有潜力。","Sensors","2026-10-13T00:00:00Z",75,{"impact":115,"substance":116,"depth":117,"authority":118,"freshness":119,"relevant":20,"comment":120},16,21,17,13,8,"方法新颖、指标可靠，将多源遥感与可解释深度学习结合用于盐渍化空间预测，对沿海农业区土壤治理有参考价值。",[122],{"name":111,"url":108},[124,25,125,126,127],"智慧农业","可解释AI","深度学习","土壤盐渍化",{"code":129,"slug":130,"name":131,"path":132,"parent_code":133},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[135,139],{"code":72,"slug":136,"name":137,"path":138,"parent_code":133},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":14,"version":38,"method":39,"evidence":141},{"candidates":142,"agricultural_evidence":44},[143,148,155,159,163],{"code":129,"score":115,"supported":44,"title":144,"abstract":146,"tags":147},[145],"deep learning",[126,145],[125,126],{"code":72,"score":149,"supported":44,"title":150,"abstract":152,"tags":154},15,[151],"remote sensing",[153,151],"遥感",[25],{"code":30,"score":43,"supported":44,"title":156,"abstract":157,"tags":158},[31],[47,31],[],{"code":65,"score":80,"supported":44,"title":160,"abstract":161,"tags":162},[],[69],[],{"code":164,"score":20,"supported":14,"title":165,"abstract":166,"tags":167},"I1",[],[],[124],[169,170],"沧州 土壤盐渍化 遥感","Sentinel-2 土壤盐分 预测","沧州土壤盐渍化遥感-4898","10.3390\u002Fs26206465",{"doi":172,"openalex_id":174,"authors":175,"venue":111,"cited_by_count":93,"oa_url":108,"card":191,"direction":99,"ingested_from":101},"W7221715654",[176,179,182,184,187,189],{"name":177,"orcid":178},"Mingyuan Ye","https:\u002F\u002Forcid.org\u002F0009-0001-6434-5505",{"name":180,"orcid":181},"Peng Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2549-6765",{"name":183,"orcid":9},"Junchi Ma",{"name":185,"orcid":186},"Bingxia Liu","https:\u002F\u002Forcid.org\u002F0000-0001-7958-7112",{"name":188,"orcid":9},"Jiahao Qi",{"name":190,"orcid":9},"Yuan Jing",{"tldr":192,"method":193,"finding":194,"direction":99,"opportunity":195},"构建融合多源遥感与环境变量的可解释深度学习框架，预测沧州土壤盐分空间分布。","Sentinel-2光谱指数+环境协变量，CNN-SE注意力，CPO优化超参数，","环境协变量与光谱特征结合将R²从0.701提升至0.785，模型性能最优。","可迁移至其他滨海盐渍区，并探索时序动态预测与多深度土壤盐分协同反演。","2026-10-14T23:30:27.313838Z",{"id":198,"title":199,"url":200,"summary":201,"summary_zh":202,"content":9,"source_name":203,"source_url":200,"published_at":204,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":205,"score_detail":206,"sources":209,"tags":211,"primary_topic":215,"secondary_topics":220,"classification":223,"search_phrases":257,"slug":260,"view_count":261,"doi":262,"paper":263,"created_at":277},3804,"Evaluating Reference Evapotranspiration and Soil Moisture as Predictors for Machine Learning-Based Actual Evapotranspiration Estimation: Model Performance and Explainability","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fatmos17100941","Accurate estimation of actual evapotranspiration (ETa) is essential for sustainable water resources management and agricultural planning. This study investigated how the availability of reference evapotranspiration (ET0) and soil moisture (SM) affects ETa prediction performance and predictor importance using Random Forest (RF), Bagging Trees (BT), Least Squares Boosting (LSBoost), Generalized Additive Models (GAM), and Multiple Linear Regression (MLR). Global solar radiation (Rs), land surface temperature (LST), normalized difference vegetation index (NDVI), and SM were evaluated under two primary scenarios, with and without ET0. Models were developed using 80% of the data, with five-fold cross-validation conducted within the training subset, and final performance was evaluated on an independent 20% test subset. Model interpretability was assessed using SHAP, Permutation Feature Importance (PFI), Partial Dependence Plot (PDP), and temporal signed-SHAP analyses, while an additional SM-ablation experiment quantified the incremental predictive contribution of soil moisture. In Scenario I, ET0 was the dominant predictor, and RF and BT achieved the highest independent-test performance (R2 = 0.875; RMSE = 0.398–0.399 mm day−1). Excluding ET0 reduced predictive performance, with RF, LSBoost, and BT achieving R2 values of approximately 0.80 in Scenario II, while the predictor-importance structure shifted primarily toward Rs, followed by SM, LST, and NDVI. Removal of SM further reduced performance in both scenarios, with substantially greater deterioration when ET0 was unavailable: R2 decreased by 0.025–0.044 in Scenario I and by 0.051–0.099 in Scenario II. Under the SM-excluded Scenario I configuration, which more closely aligned the predictor information with previous ANN- and SAFER-based studies, R2 values ranged from 0.820 to 0.832, indicating that differences in ETa prediction performance cannot be attributed solely to model structure but also depend strongly on predictor information content. Overall, ET0 provides substantial atmospheric-demand information for ETa prediction, whereas Rs assumes the dominant predictive role, and SM provides particularly important complementary information when ET0 is unavailable. The combined performance, ablation, and explainability analyses demonstrate the importance of considering predictor composition together with model structure when developing and comparing ETa estimation approaches.","准确估算实际蒸散发（ETa）对于可持续水资源管理和农业规划至关重要。本研究探讨了参考蒸散发（ET0）和土壤水分（SM）的可用性如何影响ETa预测性能及预测因子重要性，采用了随机森林（RF）、Bagging Trees（BT）、最小二乘提升（LSBoost）、广义加性模型（GAM）和多元线性回归（MLR）方法。在全球太阳辐射（Rs）、地表温度（LST）、归一化差异植被指数（NDVI）和SM的条件下，评估了包含和不包含ET0两种主要情景。模型使用80%的数据进行开发，在训练子集内进行五折交叉验证，最终性能在独立的20%测试子集上评估。模型可解释性通过SHAP、排列特征重要性（PFI）、偏依赖图（PDP）和时间符号SHAP分析进行评估，同时通过额外的SM消融实验量化了土壤水分的增量预测贡献。在情景I中，ET0是主导预测因子，RF和BT取得了最高的独立测试性能（R2 = 0.875；RMSE = 0.398–0.399 mm day−1）。排除ET0降低了预测性能，在情景II中RF、LSBoost和BT的R2值约为0.80，同时预测因子重要性结构主要转向Rs，其次是SM、LST和NDVI。去除SM进一步降低了两种情景下的性能，且在ET0不可用时性能下降幅度更大：情景I中R2下降了0.025–0.044，情景II中下降了0.051–0.099。在排除SM的情景I配置下（该配置使预测因子信息更接近此前的ANN和SAFER研究），R2值范围为0.820至0.832，表明ETa预测性能的差异不能仅归因于模型结构，还强烈依赖于预测因子信息含量。总体而言，ET0为ETa预测提供了大量大气需求信息，而Rs承担了主导预测作用，SM在ET0不可用时提供了特别重要的补充信息。综合性能、消融和可解释性分析表明，在开发和比较ETa估算方法时，需将预测因子组成与模型结构一并考虑。","Atmosphere","2026-09-27T00:00:00Z",73,{"impact":17,"substance":207,"depth":18,"authority":118,"freshness":119,"relevant":20,"comment":208},22,"方法对比与可解释性分析扎实，对农业水资源管理和灌溉决策有参考价值，但属专业论文，公共影响有限。",[210],{"name":203,"url":200},[124,25,212,213,214],"机器学习","土壤墒情","蒸散发估算",{"code":216,"slug":217,"name":218,"path":219,"parent_code":34},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",[221,222],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"code":129,"slug":130,"name":131,"path":132,"parent_code":133},{"status":37,"locked":14,"version":38,"method":39,"evidence":224},{"candidates":225,"agricultural_evidence":44},[226,233,237,243,249,253],{"code":216,"score":117,"supported":44,"title":227,"abstract":229,"tags":232},[228],"evapotranspiration",[230,231,228],"水资源","蒸散发",[],{"code":30,"score":149,"supported":44,"title":234,"abstract":235,"tags":236},[31],[47,31],[213],{"code":129,"score":238,"supported":44,"title":239,"abstract":241,"tags":242},9,[240],"machine learning",[],[212],{"code":244,"score":80,"supported":44,"title":245,"abstract":246,"tags":248},"J1",[],[247],"农业规划",[],{"code":164,"score":20,"supported":14,"title":250,"abstract":251,"tags":252},[],[],[124],{"code":72,"score":20,"supported":14,"title":254,"abstract":255,"tags":256},[],[],[25],[258,259],"参考蒸散发 土壤水分 机器学习","ET0 实际蒸散发 随机森林","参考蒸散发土壤水分机器学习-3804",2,"10.3390\u002Fatmos17100941",{"doi":262,"openalex_id":264,"authors":265,"venue":203,"cited_by_count":93,"oa_url":200,"card":271,"direction":275,"ingested_from":101},"W7214588993",[266,269],{"name":267,"orcid":268},"Halil Karahan","https:\u002F\u002Forcid.org\u002F0000-0001-5346-5686",{"name":270,"orcid":9},"Devrim Alkaya",{"tldr":272,"method":273,"finding":274,"direction":275,"opportunity":276},"评估ET0与土壤湿度对机器学习估算实际蒸散的影响及可解释性。","用RF、BT、LSBoost、GAM、MLR结合SHAP、PFI、PDP及SM消","ET0主导预测，缺失时Rs主导且SM补充价值显著增大。","农业人工智能与决策模型","可探索缺ET0条件下融合遥感与土壤湿度的可解释ETa估算框架。","2026-09-29T23:30:57.729880Z",{"id":279,"title":280,"url":281,"summary":282,"summary_zh":283,"content":9,"source_name":284,"source_url":281,"published_at":285,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":286,"score_detail":287,"sources":289,"tags":291,"primary_topic":295,"secondary_topics":296,"classification":298,"search_phrases":328,"slug":331,"view_count":261,"doi":332,"paper":333,"created_at":364},3370,"A National Depth-Resolved Soil-Moisture-to-Electromagnetic Proxy Database for Hydrogeophysical Monitoring","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fessd-2026-563","Abstract. Soil moisture is a key control on drought, recharge, crop water availability, agricultural water management, and land-atmosphere exchange, but its depth-resolved spatial variability remains difficult to monitor over large areas. Non-invasive methods such as ground-penetrating radar (GPR), electrical resistivity, and electromagnetic surveys can support soil-moisture estimation, yet their interpretation depends strongly on soil texture and hydraulic-retention properties. This study develops a national six-depth soil-moisture-to-electromagnetic proxy database to support future GPR- and resistivity-based root-zone monitoring and decision support. We used multi-year soil-moisture, porosity, saturation, texture, and hydraulic-retention data from 117 monitoring stations across Hungary at 10, 20, 30, 45, 60, and 75 cm depth. Dielectric permittivity, EM-wave velocity, electrical conductivity, attenuation, and apparent resistivity were derived as proxy variables from observed soil moisture using established petrophysical relationships, including the Topp equation and an Archie-type formulation. The proxy database identified a consistent 30-45 cm buffering zone, where soil moisture increased to 22.25%, dielectric permittivity peaked at 12.29, EM-wave velocity reached a minimum of 0.0935 m ns⁻¹, and apparent resistivity decreased to 545 Ω m. Texture strongly shaped the translated EM response: sandy soils were drier and more resistive, whereas clayey soils retained more water and showed higher dielectric response. Hydraulic-retention variables provided calibration-relevant prior information for explaining why similar EM or resistivity signals may represent different true soil-moisture values across soil types. The framework provides a transferable national calibration layer for drought-monitoring agencies, irrigation planners, precision-agriculture users, and future drone-based geophysical surveys in regions with comparable monitoring and soil databases.","摘要：土壤水分是干旱、补给、作物可用水量、农业水资源管理和陆气交换的关键控制因素，但其深度分辨的空间变异性在大范围区域仍难以监测。探地雷达（GPR）、电阻率和电磁勘探等非侵入式方法可支持土壤水分估算，但其解译在很大程度上取决于土壤质地和水力保持特性。本研究构建了一个全国尺度的六深度土壤水分—电磁代用指标数据库，以支持未来基于GPR和电阻率的根区监测与决策支持。我们使用了匈牙利全国117个监测站多年在10、20、30、45、60和75 cm深度的土壤水分、孔隙度、饱和度、质地和水力保持数据。基于观测土壤水分，利用成熟的地球物理关系（包括Topp方程和Archie型公式），推导出介电常数、电磁波速度、电导率、衰减和视电阻率作为代用变量。该代用指标数据库识别出一致的30–45 cm缓冲带，其中土壤水分增至22.25%，介电常数峰值达12.29，电磁波速度最低为0.0935 m ns⁻¹，视电阻率降至545 Ω m。质地对转换后的电磁响应具有强烈影响：砂质土壤更干、电阻率更高，而黏质土壤持水更多并表现出更高的介电响应。水力保持变量为解释为何相似的电磁或电阻率信号在不同土壤类型中可能代表不同真实土壤水分值提供了与校准相关的先验信息。该框架为干旱监测机构、灌溉规划者、精准农业用户以及未来在具有可比监测和土壤数据库区域开展的无人机地球物理调查提供了一个可迁移的全国校准层。","Earth System Science Data Discussions","2026-09-23T00:00:00Z",80,{"impact":18,"substance":207,"depth":18,"authority":118,"freshness":238,"relevant":20,"comment":288},"基于匈牙利117个站点六层深度构建的土壤水分—电磁代理数据库，为根区墒情遥感与精准灌溉决策提供可迁移校准层，方法新颖、数据扎实，对农业信息化有参考价值。",[290],{"name":284,"url":281},[25,292,293,213,294],"精准农业","遥感监测","智慧灌溉",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[297],{"code":216,"slug":217,"name":218,"path":219,"parent_code":34},{"status":37,"locked":14,"version":38,"method":39,"evidence":299},{"candidates":300,"agricultural_evidence":44},[301,305,310,315,319,324],{"code":30,"score":149,"supported":44,"title":302,"abstract":303,"tags":304},[31],[47,31],[213],{"code":216,"score":13,"supported":44,"title":306,"abstract":307,"tags":309},[],[308,230,217],"灌溉",[294],{"code":65,"score":51,"supported":44,"title":311,"abstract":312,"tags":314},[],[69,313],"drought",[],{"code":164,"score":66,"supported":44,"title":316,"abstract":317,"tags":318},[],[292],[292],{"code":129,"score":80,"supported":44,"title":320,"abstract":321,"tags":323},[],[322],"decision support",[],{"code":72,"score":261,"supported":14,"title":325,"abstract":326,"tags":327},[],[],[25,293],[329,330],"匈牙利 土壤水分 电磁代理数据库","探地雷达 根区土壤水分 监测","匈牙利土壤水分电磁代理数据库-3370","10.5194\u002Fessd-2026-563",{"doi":332,"openalex_id":334,"authors":335,"venue":284,"cited_by_count":93,"oa_url":281,"card":359,"direction":275,"ingested_from":101},"W7214076150",[336,339,341,343,346,348,350,353,356],{"name":337,"orcid":338},"Diaa Sheishah","https:\u002F\u002Forcid.org\u002F0000-0003-2050-6474",{"name":340,"orcid":9},"Enas Abdelsamei",{"name":342,"orcid":9},"Viktoria Blanka-Vegi",{"name":344,"orcid":345},"Károly Barta","https:\u002F\u002Forcid.org\u002F0000-0001-9450-5277",{"name":347,"orcid":9},"Ahmed M. Ali",{"name":349,"orcid":9},"Khaldoun Abualhin",{"name":351,"orcid":352},"Djamil Al‐Halbouni","https:\u002F\u002Forcid.org\u002F0000-0003-2254-3914",{"name":354,"orcid":355},"Wouter Arnoud Dorigo","https:\u002F\u002Forcid.org\u002F0000-0001-8054-7572",{"name":357,"orcid":358},"György Sípos","https:\u002F\u002Forcid.org\u002F0000-0001-6224-2361",{"tldr":360,"method":361,"finding":362,"direction":99,"opportunity":363},"构建匈牙利全国六深度土壤水分-电磁代理数据库，支持根区水文地球物理监测。","利用117站多年土壤水分、质地与水力参数，经Topp和Archie模型推导电磁代","发现30-45 cm缓冲带水分与介电常数峰值，质地显著影响电磁响应解释。","可扩展至无人机地球物理调查与多区域迁移校准，解决土壤质地导致的电磁信号歧义。","2026-09-24T23:30:36.864649Z",{"id":366,"title":367,"url":368,"summary":369,"summary_zh":370,"content":9,"source_name":371,"source_url":368,"published_at":372,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":373,"score_detail":374,"sources":376,"tags":378,"primary_topic":382,"secondary_topics":383,"classification":388,"search_phrases":413,"slug":416,"view_count":417,"doi":418,"paper":419,"created_at":436},3191,"Spatial prediction of soil organic carbon stocks in Sudanese clay soils using regression kriging","https:\u002F\u002Fdoi.org\u002F10.3389\u002Fsjss.2026.16733","Soil organic carbon (SOC) stocks are a critical component of terrestrial carbon pools, influencing soil quality, agricultural productivity, and climate change mitigation. This study aimed to map and improve spatial estimation of SOC stocks in Sudan’s Blue Nile clay soils using regression kriging (RK). The model integrated 554 spatially unique soil profiles with nine environmental covariates: precipitation, temperature, relative humidity, normalized difference vegetation index (NDVI), land use\u002Fcover, bare soil index (BSI), digital elevation model (DEM), LS-factor, and aspect. Spectral indices were derived from Landsat 9 imagery (April 2024), while climate and terrain data were obtained from CHIRPS\u002FWorldClim and SRTM (30 m). RK performance was robust, with spatial cross-validation R 2 = 0.72, RMSE = 8.4 Mg C ha −1 (29% of mean observed stock), and mean bias = −0.8 Mg C ha −1 . Predicted SOC stocks (0–30 cm) ranged from 12.4 to 51.2 Mg C ha −1 (mean 28.6 Mg C ha −1 ). NDVI, clay content, and topographic wetness index were the most influential predictors. Agricultural lands exhibited the highest stocks (51.2 Mg C ha −1 ), while bare lands had the lowest (14.2 Mg C ha −1 ). This study (1) applies spatially explicit validation for SOC mapping in Sudan’s Blue Nile region, (2) harmonizes legacy and contemporary soil data using equivalent soil mass correction, and (3) provides high-resolution SOC maps for climate-resilient agricultural planning. Findings support soil carbon management and climate mitigation in semi-arid regions.","土壤有机碳（SOC）储量是陆地碳库的重要组成部分，影响土壤质量、农业生产力及气候变化减缓。本研究旨在利用回归克里金（RK）方法对苏丹青尼罗河黏土区SOC储量进行制图并改进其空间估算。该模型整合了554个空间独立土壤剖面与9个环境协变量：降水、温度、相对湿度、归一化植被指数（NDVI）、土地利用\u002F覆盖、裸土指数（BSI）、数字高程模型（DEM）、LS因子和坡向。光谱指数源自Landsat 9影像（2024年4月），气候与地形数据分别来自CHIRPS\u002FWorldClim和SRTM（30 m）。RK表现稳健，空间交叉验证R²=0.72，RMSE=8.4 Mg C ha⁻¹（为实测储量均值的29%），平均偏差=−0.8 Mg C ha⁻¹。预测SOC储量（0–30 cm）范围为12.4–51.2 Mg C ha⁻¹（均值28.6 Mg C ha⁻¹）。NDVI、黏粒含量和地形湿度指数是最具影响力的预测因子。农地储量最高（51.2 Mg C ha⁻¹），裸地最低（14.2 Mg C ha⁻¹）。本研究（1）对苏丹青尼罗河地区SOC制图采用空间显式验证，（2）利用等效土壤质量校正协调历史与当代土壤数据，（3）为气候韧性农业规划提供高分辨率SOC图。研究结果支持半干旱地区的土壤碳管理与气候减缓。","Spanish Journal of Soil Science","2026-09-22T00:00:00Z",68,{"impact":119,"substance":116,"depth":117,"authority":118,"freshness":238,"relevant":20,"comment":375},"基于554个土壤剖面与多源遥感协变量的回归克里金制图研究，方法规范、验证充分，对半干旱区土壤碳管理与气候适应型农业规划有参考价值，但属区域性学术成果，公共影响有限。",[377],{"name":371,"url":368},[25,379,153,380,381],"气候变化","土壤碳汇","数字土壤制图",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[384],{"code":65,"slug":385,"name":386,"path":387,"parent_code":34},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":37,"locked":14,"version":38,"method":39,"evidence":389},{"candidates":390,"agricultural_evidence":44},[391,395,400,404,408],{"code":30,"score":43,"supported":44,"title":392,"abstract":393,"tags":394},[31],[47,31],[],{"code":65,"score":13,"supported":44,"title":396,"abstract":397,"tags":399},[],[379,69,398],"climate change",[379],{"code":244,"score":80,"supported":44,"title":401,"abstract":402,"tags":403},[],[247],[],{"code":72,"score":261,"supported":14,"title":405,"abstract":406,"tags":407},[],[],[25,153],{"code":409,"score":20,"supported":14,"title":410,"abstract":411,"tags":412},"H4",[],[],[380],[414,415],"苏丹青尼罗河 土壤有机碳 回归克里金","Landsat 9 土壤有机碳 空间预测","苏丹青尼罗河土壤有机碳回归克里金-3191",5,"10.3389\u002Fsjss.2026.16733",{"doi":418,"openalex_id":420,"authors":421,"venue":371,"cited_by_count":93,"oa_url":368,"card":430,"direction":435,"ingested_from":101},"W7213971196",[422,424,426,428],{"name":423,"orcid":9},"Faroug A.H. Jadalla",{"name":425,"orcid":9},"Kolapo O. Oluwasemire",{"name":427,"orcid":9},"Abd Elmagid A. Elmobarak",{"name":429,"orcid":9},"Mohammed A. M. Mohammed Zein",{"tldr":431,"method":432,"finding":433,"direction":99,"opportunity":434},"用回归克里金结合多源环境协变量预测苏丹青尼罗河粘土区土壤有机碳储量。","554个土壤剖面与9个环境协变量，Landsat 9、CHIRPS\u002FWorldC","模型R²=0.72，NDVI、粘土含量和地形湿度指数影响最大，农地碳储量最高。","可引入时序遥感与机器学习提升半干旱区SOC动态预测，并耦合农业管理措施评估固碳潜力。","数字乡村与农业信息化","2026-09-22T23:30:31.028178Z",{"id":438,"title":439,"url":440,"summary":441,"summary_zh":442,"content":9,"source_name":443,"source_url":440,"published_at":444,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":445,"score_detail":446,"sources":448,"tags":450,"primary_topic":454,"secondary_topics":455,"classification":457,"search_phrases":481,"slug":484,"view_count":261,"doi":485,"paper":486,"created_at":501},3184,"Soil Compaction and Irrigation Management: Implications of Soil Hydraulic Changes for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16181853","Soil compaction is one of the main forms of physical soil degradation affecting agricultural productivity. Although its effects on soil structure and crop growth have been extensively investigated, their implications for irrigation management have received little attention. This review synthesises current knowledge on how soil compaction modifies the hydraulic functioning of agricultural soils and discusses the consequences for irrigation scheduling and precision irrigation. The available evidence shows that soil compaction reduces water infiltration and saturated hydraulic conductivity while altering soil water retention and plant-available water. Surface sealing and crust formation can further restrict infiltration, particularly when bare soil is exposed to high-energy rainfall or sprinkler irrigation. These changes modify water movement, redistribution and storage within the soil profile, affecting root development, plant physiological responses and crop productivity. The magnitude of these effects depends on soil texture, soil water status, crop species and environmental conditions. Conventional irrigation scheduling generally assumes stable soil hydraulic properties and may not adequately represent compacted soils. Recent advances in soil moisture sensing, crop modelling, remote sensing and decision support systems provide new opportunities to incorporate soil structural variability into irrigation management. Combining precision irrigation with preventive traffic management and practices that enhance soil structural and biological resilience may improve irrigation efficiency, optimise water use and enhance the long-term sustainability of irrigated agricultural systems.","土壤压实是影响农业生产力的土壤物理退化的主要形式之一。尽管其对土壤结构和作物生长的影响已得到广泛研究，但其对灌溉管理的影响却鲜受关注。本文综述了关于土壤压实如何改变农业土壤水力功能的现有知识，并探讨了其对灌溉制度和精准灌溉的影响。现有证据表明，土壤压实降低了水分入渗和饱和导水率，同时改变了土壤持水性和植物有效水量。地表封闭和结皮的形成会进一步限制入渗，尤其是当裸露土壤暴露于高能量降雨或喷灌时。这些变化改变了土壤剖面中水分的运动、再分布和储存，进而影响根系发育、植物生理响应和作物生产力。这些影响的程度取决于土壤质地、土壤水分状况、作物种类和环境条件。传统灌溉制度通常假设土壤水力性质稳定，可能无法充分反映压实土壤的情况。土壤水分传感、作物建模、遥感和决策支持系统方面的最新进展，为将土壤结构变异性纳入灌溉管理提供了新的机遇。将精准灌溉与预防性机械作业管理及增强土壤结构和生物韧性的措施相结合，有望提高灌溉效率、优化水资源利用，并增强灌溉农业系统的长期可持续性。","Agronomy","2026-09-20T00:00:00Z",78,{"impact":18,"substance":116,"depth":117,"authority":43,"freshness":119,"relevant":20,"comment":447},"系统综述土壤压实对土壤水力特性与灌溉管理的影响，提出将土壤结构变异纳入精准灌溉决策，对智慧农业与节水灌溉有参考价值。",[449],{"name":443,"url":440},[124,25,451,452,453],"精准灌溉","土壤墒情监测","土壤压实",{"code":216,"slug":217,"name":218,"path":219,"parent_code":34},[456],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":14,"version":38,"method":39,"evidence":458},{"candidates":459,"agricultural_evidence":44},[460,464,468,473,477],{"code":216,"score":18,"supported":44,"title":461,"abstract":462,"tags":463},[217],[308,230,217],[451],{"code":30,"score":43,"supported":44,"title":465,"abstract":466,"tags":467},[31],[47,31],[],{"code":164,"score":238,"supported":44,"title":469,"abstract":471,"tags":472},[470],"precision agriculture",[],[124],{"code":72,"score":19,"supported":44,"title":474,"abstract":475,"tags":476},[],[153,151],[25],{"code":129,"score":80,"supported":44,"title":478,"abstract":479,"tags":480},[],[322],[],[482,483],"土壤压实 精准灌溉","土壤水力特性 灌溉管理","土壤压实精准灌溉-3184","10.3390\u002Fagronomy16181853",{"doi":485,"openalex_id":487,"authors":488,"venue":443,"cited_by_count":93,"oa_url":440,"card":495,"direction":99,"ingested_from":101},"W7213866178",[489,492],{"name":490,"orcid":491},"Alessia Cogato","https:\u002F\u002Forcid.org\u002F0000-0001-8354-7324",{"name":493,"orcid":494},"Lucia Bortolini","https:\u002F\u002Forcid.org\u002F0000-0001-6863-4542",{"tldr":496,"method":497,"finding":498,"direction":499,"opportunity":500},"综述土壤压实改变水力特性对灌溉管理的影响，提出将结构变异纳入精准灌溉。","文献综述，整合土壤水力、传感、作物模型与遥感技术。","压实降低入渗与导水率，传统灌溉调度假设不成立，需纳入结构变异。","智慧农业 \u002F 农业物联网","可开发融合土壤压实空间变异与实时传感的精准灌溉决策模型，填补结构退化下的调度空白。","2026-09-22T23:30:25.358657Z",{"id":503,"title":504,"url":505,"summary":506,"summary_zh":507,"content":9,"source_name":508,"source_url":505,"published_at":509,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":510,"score_detail":511,"sources":514,"tags":516,"primary_topic":519,"secondary_topics":520,"classification":521,"search_phrases":545,"slug":548,"view_count":261,"doi":549,"paper":550,"created_at":567},3060,"Geospatial Intelligence for Peri-Urban Land-Use Conflicts: Evaluating Agricultural Suitability against Rapid Urbanisation using the Analytical Hierarchy Process and Cloud Computing","https:\u002F\u002Fdoi.org\u002F10.59543\u002F6mpcwr41","This paper presents a geospatial multi-criteria evaluation of agricultural potential in the suburban region of Bapatla using eight physical and land-use characteristics: elevation, slope, road accessibility, proximity to water bodies, Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), Land Use\u002FLand Cover (LULC), and soil texture, processed using Google Earth Engine. The Analytic Hierarchy Process (AHP) was used to determine the relative weights of each criterion. NDVI received the highest weight (26.33%), followed by LST, slope, and proximity to water bodies (14.96% each), while elevation received the lowest weight (4.6%) due to the region's flat terrain. Weighted overlay analysis classified the 142.25 km² study area into Suitable (108.90 km²; 76.55%), Not Suitable (32.60 km²; 22.92%), and Highly Suitable (0.75 km²; 0.53%) categories. Suitable areas are mainly distributed across the southern and peripheral agricultural zones, whereas unsuitable areas are concentrated within Bapatla Urban and its surroundings. The limited extent of highly suitable land highlights the scarcity of optimal agricultural sites. The results reveal land-use conflicts driven primarily by urbanisation rather than environmental constraints. The AHP-weighted suitability map provides an evidence-based tool for agricultural land conservation, water-resource management, and sustainable urban expansion.","本文基于八项自然与土地利用特征，对巴帕特拉（Bapatla）郊区农业潜力进行了地理空间多准则评价，这些特征包括：海拔、坡度、道路可达性、距水体远近、地表温度（LST）、归一化植被指数（NDVI）、土地利用\u002F土地覆盖（LULC）以及土壤质地，并利用Google Earth Engine进行处理。采用层次分析法（AHP）确定各准则的相对权重。NDVI权重最高（26.33%），其次为LST、坡度和距水体远近（均为14.96%），而海拔因该地区地形平坦权重最低（4.6%）。加权叠加分析将142.25 km²的研究区划分为适宜（108.90 km²；76.55%）、不适宜（32.60 km²；22.92%）和高适宜（0.75 km²；0.53%）三类。适宜区主要分布于南部及外围农业区，而不适宜区集中于巴帕特拉城区及其周边。高适宜土地面积有限，凸显了优质农业用地的稀缺性。结果表明，土地利用冲突主要由城市化驱动，而非环境限制。基于AHP的适宜性地图为农业用地保护、水资源管理和可持续城市扩张提供了循证工具。","Journal of Urban Intelligence and Smart Systems","2026-09-19T00:00:00Z",70,{"impact":17,"substance":512,"depth":117,"authority":118,"freshness":119,"relevant":20,"comment":513},20,"该论文利用遥感与AHP方法评估城郊农业用地冲突，方法新颖、数据详实，对农业土地保护有参考价值，但属细分领域研究，影响范围有限。",[515],{"name":508,"url":505},[124,25,517,153,518],"农业信息化","土地利用",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":522},{"candidates":523,"agricultural_evidence":44},[524,528,532,536,540],{"code":30,"score":51,"supported":44,"title":525,"abstract":526,"tags":527},[],[47,31],[],{"code":216,"score":80,"supported":44,"title":529,"abstract":530,"tags":531},[],[230],[],{"code":164,"score":261,"supported":14,"title":533,"abstract":534,"tags":535},[],[],[517,124],{"code":72,"score":261,"supported":14,"title":537,"abstract":538,"tags":539},[],[],[25,153],{"code":541,"score":20,"supported":14,"title":542,"abstract":543,"tags":544},"H6",[],[],[518],[546,547],"Bapatla 农业用地 城市化","Google Earth Engine 农业适宜性","Bapatla农业用地城市化-3060","10.59543\u002F6mpcwr41",{"doi":549,"openalex_id":551,"authors":552,"venue":508,"cited_by_count":93,"oa_url":561,"card":562,"direction":499,"ingested_from":101},"W7213644047",[553,555,557,559],{"name":554,"orcid":9},"Sreerama Naik Naik S R",{"name":556,"orcid":9},"T K Prasad",{"name":558,"orcid":9},"Feba Jose Jasmine",{"name":560,"orcid":9},"Jayapal G","https:\u002F\u002Fjuiss.org\u002Findex.php\u002Fjuiss\u002Farticle\u002Fdownload\u002F366\u002F231",{"tldr":563,"method":564,"finding":565,"direction":99,"opportunity":566},"用AHP与云平台评估印度Bapatla城郊农业适宜性，揭示城市化引发的土地利用冲突。","Google Earth Engine处理8个因子，AHP加权叠加分析142.2","76.55%区域适宜农业，但高度适宜仅0.53%，冲突主因是城市化而非环境限制。","可引入时序遥感与动态城市扩张模拟，构建城郊农业保护与城市增长协同优化模型。","2026-09-21T23:30:09.448414Z"]