[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2059":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":23,"tags":25,"view_count":31,"doi":32,"paper":33,"created_at":58},2059,"Spatiotemporal patterns of crop water productivity under changing water–salinity conditions in an arid irrigation district of the upper Yellow River basin","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agwat.2026.110758","Improving agricultural production under tightening water constraints requires a clear understanding of long-term changes in crop water productivity (CWP). This study integrated remote sensing, Mann-Kendall trend analysis, and GeoDetector to characterize the spatiotemporal patterns and trends of yield and CWP for wheat, maize, and sunflower and to examine factors associated with CWP variability in the Hetao Irrigation District (Hetao), the largest irrigation district in the upper Yellow River basin, during 2000–2021. Results showed that wheat yield remained generally stable, while maize and sunflower yields increased, with district-wide slopes of at least 0.1500 and 0.0153 t ha⁻ 1 yr⁻ 1 , respectively. The multi-year mean CWP values were 1.56, 2.36, and 0.97 kg m⁻ 3 for wheat, maize, and sunflower, respectively. Maize CWP increased markedly (slope ≥ 0.0383 kg m⁻ 3 yr⁻ 1 ), reaching 3.44 kg m⁻ 3 in 2021, while sunflower CWP increased from 0.76 to 1.08 kg m⁻ 3 and wheat CWP remained largely stable. GeoDetector analysis showed that annual maximum Normalized Difference Vegetation Index (NDVI max,a ), as an indicator of crop growth status, and Normalized Difference Salinity Index (NDSI), as an indicator of surface salinity conditions, had the highest q values for CWP. Their interaction showed the strongest enhancement effect among the selected factor combinations, with Δ q values of 0.232, 0.222, and 0.194 for wheat, maize, and sunflower, respectively. The explanatory power of NDSI increased significantly for wheat (slope = 0.0009, p \u003C 0.05), whereas the q values of both NDVI max,a and NDSI both decreased significantly for maize and sunflower ( p \u003C 0.05). Sub-district-scale analysis further revealed contrasting crop-specific trends, with wheat CWP decreasing most strongly in Jiefangzha and maize CWP increasing markedly in Wulate. These results reveal crop-specific and spatially heterogeneous responses of CWP to changing water–salinity conditions and support differentiated management of irrigation, salinity, and cropping structure in large arid irrigation districts.","在日益严峻的水资源约束下提高农业生产水平，需要清晰认识作物水分生产力（CWP）的长期变化。本研究综合遥感、Mann-Kendall趋势分析和GeoDetector方法，刻画了2000—2021年河套灌区（河套，黄河上游最大的灌区）小麦、玉米和向日葵产量与CWP的时空格局及趋势，并考察了与CWP变异相关的因素。结果表明，小麦产量总体保持稳定，而玉米和向日葵产量增加，全灌区斜率分别至少为0.1500和0.0153 t ha⁻¹ yr⁻¹。小麦、玉米和向日葵的多年平均CWP分别为1.56、2.36和0.97 kg m⁻³。玉米CWP显著增加（斜率≥0.0383 kg m⁻³ yr⁻¹），2021年达到3.44 kg m⁻³；向日葵CWP从0.76增至1.08 kg m⁻³；小麦CWP基本稳定。GeoDetector分析表明，作为作物生长状况指标的年最大归一化植被指数（NDVI max,a）和作为地表盐分状况指标的归一化差异盐分指数（NDSI）对CWP的q值最高。二者的交互作用在所选因子组合中表现出最强的增强效应，小麦、玉米和向日葵的Δ q值分别为0.232、0.222和0.194。NDSI对小麦的解释力显著增强（斜率=0.0009，p\u003C0.05），而玉米和向日葵的NDVI max,a和NDSI的q值均显著下降（p\u003C0.05）。分灌域尺度分析进一步揭示了作物特异性趋势的差异：小麦CWP在解放闸下降最为明显，玉米CWP在乌拉特显著增加。这些结果揭示了CWP对变化的水盐条件具有作物特异性和空间异质性响应，可为大型干旱灌区的灌溉、盐分和种植结构差异化管理提供支持。",null,"Agricultural Water Management","2026-09-09T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,23,14,8,1,"基于遥感与GeoDetector的22年长序列研究，揭示河套灌区作物水分生产力对水盐变化的响应，方法扎实、结论可靠，对干旱灌区差异化灌溉与盐分管理有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"盐碱地治理","遥感监测","灌溉管理","作物水分生产力","河套灌区",0,"10.1016\u002Fj.agwat.2026.110758",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":6,"card":51,"direction":55,"ingested_from":57},"W7212045637",[36,39,42,45,48],{"name":37,"orcid":38},"Xinyi Li","https:\u002F\u002Forcid.org\u002F0009-0009-7047-5400",{"name":40,"orcid":41},"Xu Xu","https:\u002F\u002Forcid.org\u002F0000-0002-5450-1287",{"name":43,"orcid":44},"Chen Sun","https:\u002F\u002Forcid.org\u002F0009-0006-4641-0912",{"name":46,"orcid":47},"Dongyang Ren","https:\u002F\u002Forcid.org\u002F0000-0002-6238-0753",{"name":49,"orcid":50},"Quanzhong Huang","https:\u002F\u002Forcid.org\u002F0000-0001-6306-785X",{"tldr":52,"method":53,"finding":54,"direction":55,"opportunity":56},"基于遥感与趋势分析，揭示河套灌区2000-2021年三种作物水分生产力的时空格局及水盐驱动机制。","遥感反演、Mann-Kendall趋势检验与GeoDetector因子探测，覆盖","玉米水分生产力显著提升至3.44 kg\u002Fm³，NDVI最大值与盐分指数交互作用对CWP解释力最强。","农业遥感与作物表型","可结合多源遥感与机器学习，构建水盐胁迫下作物水分生产力的分区预测与灌溉决策模型。","openalex","2026-09-10T23:30:27.873848Z"]