[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3902":3,"related-3902":115},{"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":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":88,"slug":91,"view_count":72,"doi":92,"paper":93,"created_at":114},3902,"ҚАЗАҚСТАННЫҢ ОҢТҮСТІК-ШЫҒЫСЫНДАҒЫ ТӘЛІМДІ ЖЕРЛЕРДІҢ ЭРОЗИЯЛЫҚ ҚАУІПТІЛІГІН ҚАШЫҚТАН ЗОНДТАУ ДЕРЕКТЕРІ МЕН RUSLE МОДЕЛІ НЕГІЗІНДЕ БАҒАЛАУ","https:\u002F\u002Fdoi.org\u002F10.52578\u002F3134-8556-2026-3-14-23","This study assesses potential soil erosion on arable land in the foothill zone of South-East Kazakhstan using the Revised Universal Soil Loss Equation (RUSLE), remote sensing data, and Geographic Information Systems (GIS). The research focuses on a 17.6-hectare agricultural plot located in the Almaty region. Sentinel-2 satellite imagery and the FABDEM_V1 global digital elevation model were used as the primary data sources for spatial analysis. The methodology involved the calculation and integration of the main RUSLE factors, including rainfall erosivity (R), soil erodibility (K), topographic factor (LS), cover-management factor (C), and support practice factor (P). The LS factor was derived from the FABDEM digital elevation model, while the vegetation cover factor (C) was estimated using the Normalized Difference Vegetation Index (NDVI) for two key agricultural periods: April (pre-growing season) and July (post-harvest period). Based on soil mechanical composition and humus content, the K factor was determined as 0.26. The results show that the average annual soil loss amounted to 10.01 t\u002Fha\u002Fyear in April and increased to 13.67 t\u002Fha\u002Fyear in July. The analysis revealed that erosion risk rises by approximately 36% after harvesting due to reduced vegetation cover. The maximum soil loss reached 55.46 t\u002Fha\u002Fyear in the most erosion-prone areas. The findings highlight that the post-harvest period represents the most critical phase for soil degradation in the study area. The study emphasizes the importance of implementing soil conservation measures and demonstrates the effectiveness of integrating RUSLE modeling with remote sensing and GIS technologies for sustainable land management.","本研究利用修正通用土壤流失方程（RUSLE）、遥感数据和地理信息系统（GIS），评估了哈萨克斯坦东南部山麓地带耕地的潜在土壤侵蚀。研究聚焦于阿拉木图地区一块17.6公顷的农业地块。采用Sentinel-2卫星影像和FABDEM_V1全球数字高程模型作为空间分析的主要数据来源。研究方法包括计算并整合RUSLE的主要因子，包括降雨侵蚀力（R）、土壤可蚀性（K）、地形因子（LS）、植被覆盖与管理因子（C）以及水土保持措施因子（P）。LS因子由FABDEM数字高程模型提取，植被覆盖因子（C）则利用归一化植被指数（NDVI）在两个关键农业时期进行估算：4月（生长季前）和7月（收获后）。根据土壤机械组成和腐殖质含量，确定K因子为0.26。结果表明，4月年均土壤流失量为10.01吨\u002F公顷\u002F年，7月增至13.67吨\u002F公顷\u002F年。分析显示，由于植被覆盖减少，收获后侵蚀风险上升约36%。在侵蚀最严重的区域，最大土壤流失量达到55.46吨\u002F公顷\u002F年。研究结果突出表明，收获后时期是研究区土壤退化最为关键的阶段。本研究强调了实施土壤保持措施的重要性，并证明了将RUSLE模型与遥感和GIS技术相结合用于可持续土地管理的有效性。",null,"Agricultural Systems and Natural Resources","2026-09-30T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,17,13,9,1,"基于RUSLE模型与遥感数据评估哈萨克斯坦东南部耕地侵蚀风险，方法规范、数据具体，对干旱区数字农业与水土保持有参考价值，但属区域案例研究，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字农业","遥感","土壤侵蚀","水土保持","RUSLE",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":46},[45,55,62,70,76,83],{"code":33,"score":21,"supported":46,"title":47,"abstract":48,"tags":54},true,[],[49,50,51,52,53],"地理信息","空间分析","GIS","spatial analysis","geographic information",[],{"code":56,"score":57,"supported":46,"title":58,"abstract":59,"tags":61},"I3",7,[],[28,60],"remote sensing",[28],{"code":63,"score":64,"supported":46,"title":65,"abstract":66,"tags":69},"H2",6,[],[67,68],"土壤","soil",[],{"code":71,"score":72,"supported":46,"title":73,"abstract":74,"tags":75},"H6",4,[],[30],[30],{"code":77,"score":78,"supported":46,"title":79,"abstract":80,"tags":82},"I7",3,[],[81],"研究方法",[],{"code":84,"score":22,"supported":14,"title":85,"abstract":86,"tags":87},"I1",[],[],[27],[89,90],"哈萨克斯坦 阿拉木图 RUSLE 土壤侵蚀","Sentinel-2 NDVI 土壤流失","哈萨克斯坦阿拉木图RUSLE土壤侵蚀-3902","10.52578\u002F3134-8556-2026-3-14-23",{"doi":92,"openalex_id":94,"authors":95,"venue":10,"cited_by_count":106,"oa_url":6,"card":107,"direction":111,"ingested_from":113},"W7214985337",[96,98,100,102,104],{"name":97,"orcid":9},"Д. Кайнушева",{"name":99,"orcid":9},"Р. Жапаев",{"name":101,"orcid":9},"Е. Кенжехан",{"name":103,"orcid":9},"Д. Кыдырбаева",{"name":105,"orcid":9},"Ә. Мақұлбек",0,{"tldr":108,"method":109,"finding":110,"direction":111,"opportunity":112},"用RUSLE模型和遥感数据评估哈萨克斯坦东南部耕地土壤侵蚀风险。","RUSLE模型、Sentinel-2影像、FABDEM高程数据与GIS空间分析。","年均土壤流失量4月为10.01 t\u002Fha，7月增至13.67 t\u002Fha，收获后侵蚀风险上升约36%。","农业遥感与作物表型","可结合多时相遥感与机器学习，动态预测收获后侵蚀热点，优化保护性耕作措施。","openalex","2026-10-01T23:30:29.218572Z",{"total":64,"page":22,"page_size":64,"items":116},[117,183,241,326,401,472],{"id":118,"title":119,"url":120,"summary":121,"summary_zh":122,"content":9,"source_name":123,"source_url":120,"published_at":124,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":125,"score_detail":126,"sources":131,"tags":133,"primary_topic":135,"secondary_topics":136,"classification":137,"search_phrases":163,"slug":166,"view_count":106,"doi":167,"paper":168,"created_at":182},5572,"Potential erosion risk assessment of Kanak stream basin (Sivas, Sarkisla) with the ICONA model","https:\u002F\u002Fdoi.org\u002F10.51753\u002Fflsrt.1828829","The rapid development of technology following the Industrial Revolution has increased human domination over nature. Erosion, which plays a critical role in the process of natural shaping of the earth, can accelerate over time because of improper utilization of the natural environment by humans. The main objective of this study is to determine the erosion susceptibility of the Kanak Creek Basin, one of the sub-basins in the upper reaches of the Kizilirmak River, using the ICONA model, whose main parameters are slope, lithology, land use and vegetation cover, and Geographic Information Systems (GIS) and Remote Sensing techniques, and to list the measures that can be taken against erosion. According to the results of the ICONA model potential erosion level map, 48% of the study area has low erosion susceptibility. These areas largely correspond to the vicinity of Sama Mountain, which has volcanic rocks resistant to erosion, and the base of Sarkisla Plain where the slope is very low. The plain floor is largely covered with agricultural lands. The expansion of irrigated agriculture in this region is one of the main measures that can be taken against erosion. Erosion susceptibility increases around Turna Mountain on the northern border of the basin and Cal Mountain in the south due to both the increase in slope and the lack of resistance of the rocks in the region to erosion, and corresponds to 30% of the study area. In 22% of the basin, susceptibility to erosion is at a medium level. Most of these areas consist of natural meadows and pastures. It is essential to take measures against overgrazing in these areas to prevent the complete loss of the already sparse vegetation cover. Planning land use in the basin in a sustainable manner, arranging fields and layers perpendicular to the slope, expanding irrigated agriculture, and preventing overgrazing are of vital importance in terms of combating soil erosion.","工业革命以来技术的迅猛发展增强了人类对自然的支配能力。侵蚀在地球自然塑造过程中起着关键作用，而人类对自然环境的不当利用会加速侵蚀进程。本研究的主要目标是利用ICONA模型（其主要参数为坡度、岩性、土地利用和植被覆盖）以及地理信息系统（GIS）和遥感技术，确定克孜勒河上游子流域之一卡纳克溪流域的侵蚀敏感性，并列出可采取的侵蚀防治措施。根据ICONA模型潜在侵蚀等级图的结果，研究区48%的区域侵蚀敏感性较低。这些区域主要对应于具有抗侵蚀火山岩的萨马山附近以及坡度极低的萨尔基什拉平原底部。平原底部大部分为农业用地覆盖。在该地区扩大灌溉农业是防治侵蚀的主要措施之一。在流域北部边界的图尔纳山和南部的恰尔山周围，由于坡度增大以及该地区岩石抗侵蚀能力不足，侵蚀敏感性增加，占研究区面积的30%。流域22%的区域侵蚀敏感性为中等水平。这些区域大部分为天然草甸和牧场。在这些地区采取措施防止过度放牧至关重要，以避免本已稀疏的植被覆盖完全丧失。以可持续方式规划流域土地利用、沿垂直于坡向布置田块和土层、扩大灌溉农业以及防止过度放牧，对防治土壤侵蚀具有至关重要的意义。","Frontiers in Life Sciences and Related Technologies","2026-10-21T00:00:00Z",61,{"impact":127,"substance":128,"depth":129,"authority":17,"freshness":21,"relevant":22,"comment":130},8,18,14,"该研究以ICONA模型结合GIS与遥感评估土耳其小流域侵蚀风险，方法规范、结论具体，但属区域性案例，公共影响有限，可作为专业参考而非每日精选。",[132],{"name":123,"url":120},[28,134,29,51,30],"土地利用",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":138},{"candidates":139,"agricultural_evidence":46},[140,144,148,152,158],{"code":33,"score":13,"supported":46,"title":141,"abstract":142,"tags":143},[],[49,51,53],[51],{"code":56,"score":57,"supported":46,"title":145,"abstract":146,"tags":147},[],[28,60],[28],{"code":63,"score":64,"supported":46,"title":149,"abstract":150,"tags":151},[],[67,68],[],{"code":153,"score":78,"supported":46,"title":154,"abstract":155,"tags":157},"H3",[],[156],"灌溉",[],{"code":71,"score":159,"supported":14,"title":160,"abstract":161,"tags":162},2,[],[],[134,30],[164,165],"ICONA 模型 侵蚀风险","Kanak 流域 侵蚀","ICONA模型侵蚀风险-5572","10.51753\u002Fflsrt.1828829",{"doi":167,"openalex_id":169,"authors":170,"venue":123,"cited_by_count":106,"oa_url":9,"card":176,"direction":111,"ingested_from":113},"W7223117733",[171,173],{"name":172,"orcid":9},"MERİÇ KURT",{"name":174,"orcid":175},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":177,"method":178,"finding":179,"direction":180,"opportunity":181},"用ICONA模型评估土耳其Kanak流域潜在侵蚀风险并列出防治措施。","ICONA模型结合GIS与遥感，输入坡度、岩性、土地利用与植被覆盖。","48%区域低侵蚀风险，30%高敏感，22%中等，主因坡度与岩性。","农业绿色发展与碳","可将ICONA与遥感时序数据结合，动态监测流域侵蚀并评估农业管理措施效果。","2026-10-22T11:20:39.475037Z",{"id":184,"title":185,"url":186,"summary":187,"summary_zh":188,"content":9,"source_name":189,"source_url":186,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":190,"score_detail":191,"sources":193,"tags":195,"primary_topic":197,"secondary_topics":201,"classification":203,"search_phrases":224,"slug":227,"view_count":159,"doi":228,"paper":229,"created_at":240},4102,"Application of GIS and Remote Sensing Techniques in Geospatial Mapping of Gully Statuses of an Alfisols in Yola South Local Government Area, Adamawa State","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fwjimt.v10.no1.2026.pg155.165","This research work saddled to study the application of GIS and Remote Sensing (RS) Techniques in geospatial mapping of gully erosion statuses of an Alfisols in Yola South LGA. Bole farmland was selected where expansion and development of gully was geospatially mapped out for the period of 20 years (2004-2024) at interval of five (5) years using GIS and RS techniques. The results reveal a concerning trend of increasing gully erosion, with significant growth in affected areas over the years. The highest percentage of increase occurred at 2014-2019 with about 58,069.00 m2 (65.5 %) and in 2019-2024was increased with 14,154.00 m2 (15.97 %). The total area affected in the area within the study period was 88,627.00 m2 caused by heavy rainfall and deforestation. Therefore, there are needs for effective land management practices, policy interventions, and community engagement to mitigate the effects of gully erosion.","本研究旨在探讨GIS与遥感（RS）技术在约拉南部地方政府区淋溶土冲沟侵蚀状况地理空间制图中的应用。选取博莱农田为研究区，利用GIS与遥感技术，以5年为间隔，对2004—2024年共20年间冲沟的扩展与发育进行了地理空间制图。结果表明，冲沟侵蚀呈令人担忧的加剧趋势，受影响面积逐年显著增长。增幅最大的时段为2014—2019年，约增加58 069.00 m²（65.5%）；2019—2024年增加14 154.00 m²（15.97%）。研究期内该区域受影响总面积为88 627.00 m²，主要由强降雨和森林砍伐所致。因此，需要采取有效的土地管理措施、政策干预和社区参与，以减轻冲沟侵蚀的影响。","WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY",54,{"impact":127,"substance":128,"depth":129,"authority":64,"freshness":127,"relevant":22,"comment":192},"基于GIS与遥感的20年沟蚀动态监测，数据具体、结论明确，但属区域案例研究，公共影响有限。",[194],{"name":189,"url":186},[196,28,29,51,30],"数字乡村",{"code":56,"slug":198,"name":199,"path":200,"parent_code":37},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[202],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":204},{"candidates":205,"agricultural_evidence":46},[206,211,215,219],{"code":56,"score":207,"supported":46,"title":208,"abstract":209,"tags":210},15,[60],[28,60],[28],{"code":33,"score":17,"supported":46,"title":212,"abstract":213,"tags":214},[51],[51],[51],{"code":71,"score":22,"supported":14,"title":216,"abstract":217,"tags":218},[],[],[30],{"code":220,"score":22,"supported":14,"title":221,"abstract":222,"tags":223},"I6",[],[],[196],[225,226],"Yola South 沟蚀 遥感","Alfisols 沟蚀 GIS 制图","YolaSouth沟蚀遥感-4102","10.56201\u002Fwjimt.v10.no1.2026.pg155.165",{"doi":228,"openalex_id":230,"authors":231,"venue":189,"cited_by_count":106,"oa_url":234,"card":235,"direction":111,"ingested_from":113},"W7214920253",[232],{"name":233,"orcid":9},"Mohammed Pakra Umar","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FWJIMT\u002FVOL. 10 NO. 1 2026\u002FAPPLICATION OF GIS AND REMOTE 156-165.pdf",{"tldr":236,"method":237,"finding":238,"direction":111,"opportunity":239},"用GIS和遥感技术对尼日利亚Yola South地区20年沟蚀动态进行空间制图与监测。","基于GIS与遥感影像，按5年间隔提取2004-2024年沟蚀范围并计算面积变化。","沟蚀面积持续扩大，2014-2019年增幅最大达65.5%，20年累计影响88,627平方米。","可结合多源高分辨率遥感与机器学习，构建沟蚀风险预警模型并评估保护性耕作等治理措施效果。","2026-10-03T23:30:37.949646Z",{"id":242,"title":243,"url":244,"summary":245,"summary_zh":246,"content":9,"source_name":247,"source_url":244,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":248,"score_detail":249,"sources":252,"tags":254,"primary_topic":257,"secondary_topics":262,"classification":265,"search_phrases":309,"slug":312,"view_count":22,"doi":313,"paper":314,"created_at":325},3901,"Geospatial analysis of irrigation suitability in Nigeria using remote sensing and GIS","https:\u002F\u002Fdoi.org\u002F10.53030\u002Ftjags.1937471","This research offers a comprehensive geospatial evaluation of land suitability for irrigation across Nigeria. It leverages 2020 Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data, processed within the Google Earth Engine (GEE) cloud-based computational environment. Given growing concerns about food security and climate variability, ascertaining areas amenable to irrigation is paramount for fostering sustainable agricultural development within Nigeria. This investigation integrated multi-temporal remote sensing observations, including the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Normalized Difference Water Index - NDWI, to construct an Irrigation Suitability Index (ISI). These datasets underwent analysis at a 500-meter spatial resolution, encompassing the entirety of the nation. Weighted overlay analysis, performed within the GEE environment, generated a nationwide map delineating irrigation potential across five distinct categories: Very Low, Low, Moderate, High, and Very High suitability. The findings reveal suitability scores ranging from a minimum of 0.11 (very low) to a maximum of 1 (very high). Specifically, northern Nigerian states such as Sokoto, Kebbi, Yobe, and Borno demonstrated high to very high suitability. In contrast, southern states, including Lagos, Rivers, Cross River, and Delta, were characterized by very low suitability. The study underscores significant geographical disparities, particularly in arid northern regions which experience greater constraints due to lower precipitation and elevated evapotranspiration. These findings offer actionable directives for agricultural planners, policymakers, and stakeholders seeking to enhance irrigation capacity and strengthen food security resilience amidst evolving climatic conditions.","本研究对尼日利亚全国灌溉土地适宜性进行了综合地理空间评估。研究利用2020年中分辨率成像光谱仪（MODIS）卫星数据，在谷歌地球引擎（GEE）云端计算环境中进行处理。鉴于粮食安全和气候变率日益受到关注，确定适宜灌溉的区域对于促进尼日利亚可持续农业发展至关重要。本研究整合了多时相遥感观测数据，包括归一化植被指数（NDVI）、地表温度（LST）和归一化水体指数（NDWI），以构建灌溉适宜性指数（ISI）。这些数据集以500米空间分辨率进行分析，覆盖全国范围。在GEE环境中进行的加权叠加分析生成了全国灌溉潜力分布图，划分为五个类别：极低、低、中等、高和极高适宜性。研究结果显示，适宜性评分从最低0.11（极低）到最高1（极高）不等。具体而言，尼日利亚北部各州如索科托、凯比、约贝和博尔诺表现出高至极高适宜性。相比之下，南部各州包括拉各斯、河流州、十字河州和三角洲州则以极低适宜性为特征。研究强调了显著的地理差异，特别是干旱的北部地区因降水量较低和蒸散量较高而面临更大限制。这些发现为农业规划者、政策制定者和利益相关者提供了可操作的指导，以在气候条件不断变化的情况下增强灌溉能力并提升粮食安全韧性。","OpenAlex",78,{"impact":128,"substance":250,"depth":19,"authority":20,"freshness":127,"relevant":22,"comment":251},22,"基于MODIS与GEE的全国尺度灌溉适宜性制图，方法规范、结论具体，对农业规划有参考价值，但属他国案例，国内落地性有限。",[253],{"name":247,"url":244},[27,255,28,51,256],"粮食安全","灌溉适宜性",{"code":153,"slug":258,"name":259,"path":260,"parent_code":261},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[263,264],{"code":56,"slug":198,"name":199,"path":200,"parent_code":37},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":266},{"candidates":267,"agricultural_evidence":46},[268,273,277,281,287,293,299,305],{"code":153,"score":19,"supported":46,"title":269,"abstract":270,"tags":272},[258],[156,258,271],"evapotranspiration",[],{"code":56,"score":207,"supported":46,"title":274,"abstract":275,"tags":276},[60],[28,60],[28],{"code":33,"score":21,"supported":46,"title":278,"abstract":279,"tags":280},[51],[],[51],{"code":282,"score":57,"supported":46,"title":283,"abstract":284,"tags":286},"A8",[],[255,285],"food security",[255],{"code":288,"score":78,"supported":46,"title":289,"abstract":290,"tags":292},"H7",[],[291],"干旱",[],{"code":294,"score":78,"supported":46,"title":295,"abstract":296,"tags":298},"H8",[],[297],"可持续农业",[],{"code":300,"score":78,"supported":46,"title":301,"abstract":302,"tags":304},"J1",[],[303],"农业规划",[],{"code":84,"score":22,"supported":14,"title":306,"abstract":307,"tags":308},[],[],[27],[310,311],"尼日利亚 灌溉适宜性 遥感","Google Earth Engine 灌溉","尼日利亚灌溉适宜性遥感-3901","10.53030\u002Ftjags.1937471",{"doi":313,"openalex_id":315,"authors":316,"venue":9,"cited_by_count":106,"oa_url":9,"card":320,"direction":111,"ingested_from":113},"W7214955379",[317],{"name":318,"orcid":319},"Emmanuel M. Menegbo","https:\u002F\u002Forcid.org\u002F0000-0002-6694-3226",{"tldr":321,"method":322,"finding":323,"direction":111,"opportunity":324},"用遥感与GIS评估尼日利亚全国灌溉适宜性并绘制五级分区图。","2020年MODIS数据在GEE中计算NDVI、LST、NDWI并加权叠加。","北部各州适宜性高，南部低，全国适宜性得分0.11至1。","可结合土壤、地下水与作物需水数据，将静态适宜性图升级为动态灌溉决策支持系统。","2026-10-01T23:30:29.159371Z",{"id":327,"title":328,"url":329,"summary":330,"summary_zh":331,"content":9,"source_name":332,"source_url":329,"published_at":333,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":334,"score_detail":335,"sources":338,"tags":340,"primary_topic":343,"secondary_topics":344,"classification":349,"search_phrases":378,"slug":381,"view_count":159,"doi":382,"paper":383,"created_at":400},1768,"Remote Sensing and GIS-Based Spatial Planning of Soil and Water Conservation Measures in a Semi-Arid Watershed Maliwadgaon of Maharashtra, India","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fijecc\u002F2026\u002Fv16i95662","Site-specific planning of water harvesting and soil and water conservation (SWC) measures is important for improving water availability and watershed resilience in semi-arid regions. This study aimed to identify and prioritise suitable locations for SWC structures in the 43 km² Maliwadgaon watershed of Chhatrapati Sambhajinagar district, Maharashtra, India, using an integrated remote sensing (RS) and Geographic Information System (GIS) approach. A 30 m Shuttle Radar Topography Mission (SRTM) Digital Elevation Model and Landsat-8 imagery were used to derive terrain, drainage, land use\u002Fland cover (LULC), and vegetation characteristics. Elevation, slope, drainage density, stream order, dissection index, LULC, and normalized difference vegetation index (NDVI) were integrated through multi-criteria evaluation (MCE) for site-suitability assessment. The watershed has an elevation range of approximately 509–591 m and a dendritic, fourth-order drainage network with a drainage density of 0.991 km\u002Fkm². The MCE identified 40 potential conservation structures: 13 farm ponds, 13 cement nala bunds, 5 percolation tanks, 4 underground barriers, and 5 gully plugs. The proposed locations provide a spatial basis for runoff interception, erosion control, water storage, groundwater recharge, and agricultural water management. The findings demonstrate the practical utility of RS–GIS-based MCE as a decision-support approach for prioritising site-specific SWC interventions in a semi-arid watershed.","在半干旱地区，水资源收集及水土保持（SWC）措施的定点规划对于改善水资源可利用性和增强流域韧性至关重要。本研究旨在利用遥感（RS）与地理信息系统（GIS）集成方法，识别并优先确定印度马哈拉施特拉邦恰特拉帕蒂·桑巴吉纳加尔区Maliwadgaon流域（面积43平方公里）内适宜建设水土保持设施的位置。研究采用30米分辨率的航天飞机雷达地形任务（SRTM）数字高程模型和Landsat-8影像，提取地形、水系、土地利用\u002F土地覆盖（LULC）及植被特征。通过多准则评价（MCE）整合高程、坡度、排水密度、河流等级、切割指数、土地利用\u002F土地覆盖及归一化差异植被指数（NDVI），进行选址适宜性评估。该流域海拔范围约为509–591米，水系呈树枝状，为四级排水网络，排水密度为0.991公里\u002F平方公里。多准则评价识别出40处潜在的水土保持设施：13座田间池塘、13座水泥沟壑拦水坝、5座渗滤池、4座地下屏障及5处冲沟堵截设施。所提出的选址为径流拦截、侵蚀控制、蓄水、地下水补给及农业水资源管理提供了空间依据。研究结果表明，基于遥感与地理信息系统的多准则评价作为一种决策支持方法，在半干旱流域优先确定定点水土保持干预措施方面具有实际应用价值。","International Journal of Environment and Climate Change","2026-09-05T00:00:00Z",60,{"impact":17,"substance":128,"depth":336,"authority":17,"freshness":159,"relevant":22,"comment":337},16,"研究利用遥感和GIS技术在半干旱流域进行水土保持措施选址，方法实用，但时效性较低。",[339],{"name":332,"url":329},[341,28,51,30,342],"农业水资源","半干旱区",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[345,346],{"code":56,"slug":198,"name":199,"path":200,"parent_code":37},{"code":63,"slug":68,"name":347,"path":348,"parent_code":261},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"status":40,"locked":14,"version":41,"method":42,"evidence":350},{"candidates":351,"agricultural_evidence":46},[352,356,360,365,370,374],{"code":33,"score":128,"supported":46,"title":353,"abstract":354,"tags":355},[51],[49,51,53],[51],{"code":56,"score":207,"supported":46,"title":357,"abstract":358,"tags":359},[60],[28,60],[28],{"code":63,"score":361,"supported":46,"title":362,"abstract":363,"tags":364},11,[68],[68],[],{"code":153,"score":72,"supported":46,"title":366,"abstract":367,"tags":369},[],[368],"水资源",[341],{"code":71,"score":72,"supported":46,"title":371,"abstract":372,"tags":373},[],[30],[30],{"code":288,"score":78,"supported":46,"title":375,"abstract":376,"tags":377},[],[291],[],[379,380],"农业水资源 半干旱区 水土保持 遥感","农业水资源 半干旱区","农业水资源半干旱区水土保持遥感-1768","10.9734\u002Fijecc\u002F2026\u002Fv16i95662",{"doi":382,"openalex_id":384,"authors":385,"venue":332,"cited_by_count":106,"oa_url":329,"card":395,"direction":111,"ingested_from":113},"W7208812908",[386,389,391,393],{"name":387,"orcid":388},"Prasannajit D. Gangurde","https:\u002F\u002Forcid.org\u002F0009-0002-9266-8931",{"name":390,"orcid":9},"Sanjay N. Pawar",{"name":392,"orcid":9},"M. S. Pendke",{"name":394,"orcid":9},"Anilkumar M. Kamble",{"tldr":396,"method":397,"finding":398,"direction":111,"opportunity":399},"利用遥感和GIS对半干旱流域进行水土保持措施的空间规划与选址。","集成SRTM DEM、Landsat-8影像，通过多准则评价进行选址。","识别出40个潜在保护结构，包括池塘、堤坝等，用于水资源管理。","可扩展至其他半干旱流域，结合高分辨率影像和机器学习优化选址精度。","2026-09-06T23:30:27.290464Z",{"id":402,"title":403,"url":404,"summary":405,"summary_zh":406,"content":9,"source_name":247,"source_url":404,"published_at":407,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":334,"score_detail":408,"sources":410,"tags":412,"primary_topic":416,"secondary_topics":417,"classification":419,"search_phrases":448,"slug":451,"view_count":106,"doi":452,"paper":453,"created_at":471},5321,"GIS and remote sensing based multicriteria analysis for identifying potential agricultural land: A case study on Savar Upazila, Bangladesh","https:\u002F\u002Fdoi.org\u002F10.3329\u002Fbrds.v26i1.94265","Despite of gradually decreasing its contribution to gross domestic product (GDP), agriculture, however, is the backbone of the Bangladeshi economy. Farming is the primary mean of rural livelihoods in Bangladesh but agricultural land is slowly declining day by day. Although, at present, Bangladesh is self-sufficient in terms of producing food, especially rice and some other major agricultural products, it is predicted that, in near future, the country is going to face immense challenges due to climate related threats. The research aims to identify the potential lands for suitable farming in Savar Upazila (sub-district) under Dhaka district applying a geographic information system (GIS) and remote sensing (RS) based multicriteria analysis. Relevant biophysical characteristics of soil, terrain, and landuse and land cover (LULC) were considered. Soil and evelvation data was collected from Soil Resource Development Institute (SRDI)and Shuttle Radar Topography Mission (SRTM), respectively, whereas satellite images were collected from Landsat-8. The maximum likelihood supervised classification technique was used for LULC classification, and the multicriteria evaluation (MCE) and analytical hierarchy process (AHP) approach was employed to identify suitable areas for farming. The research finds the LULC classes are agricultural land, built-up area, fallow land, vegetation cover, waterbodies, and wetland are 12.42%, 31.01%, 25.69%, 25.98%, 4.81%, and 0.09%, respectively. This research also finds that agricultural land suitability was classified as highly suitable, moderately suitable, marginally suitable, and not suitable are 24.28%, 29.53%, 24.66%, and 21.53%, respectively. In contrast, the current agricultural land usage accounts for just 12.42% of the total land area in this research area. The Kappa accuracy has been measured at a value of 0.88 and an overall accuracy value of 0.90 which indicates that the LULC classification is done almost perfectly. This study offered information at the local level that farmers may utilize to decide on cropping patterns and appropriateness. Bangladesh Rural Development Studies, 26(1), 2023 (13-26)","尽管农业在国内生产总值（GDP）中的贡献逐渐下降，但它仍是孟加拉国经济的支柱。农耕是孟加拉国农村生计的主要方式，但农业用地正日益减少。虽然目前孟加拉国在粮食生产方面，尤其是稻米和其他一些主要农产品方面已实现自给自足，但据预测，在不久的将来，该国将因气候相关威胁而面临巨大挑战。本研究旨在运用地理信息系统（GIS）和遥感（RS）基础上的多准则分析，确定达卡县萨瓦尔乡（分区）适宜耕作的土地潜力。研究考虑了土壤、地形以及土地利用与土地覆盖（LULC）等相关生物物理特征。土壤数据来自土壤资源开发研究所（SRDI），高程数据来自航天飞机雷达地形测绘任务（SRTM），卫星影像则来自Landsat-8。土地利用与土地覆盖分类采用最大似然监督分类技术，适宜耕作区的识别则运用多准则评价（MCE）和层次分析法（AHP）。研究发现，土地利用与土地覆盖类别中，农业用地、建成区、休耕地、植被覆盖、水体及湿地分别占12.42%、31.01%、25.69%、25.98%、4.81%和0.09%。研究还发现，农业用地适宜性分为高度适宜、中度适宜、勉强适宜和不适宜，分别占24.28%、29.53%、24.66%和21.53%。相比之下，当前农业用地仅占本研究区土地总面积的12.42%。Kappa精度值为0.88，总体精度值为0.90，表明土地利用与土地覆盖分类近乎完美。本研究提供了地方层面的信息，可供农民在决定种植模式和适宜性时参考。《孟加拉国农村发展研究》，第26卷第1期，2023年（第13-26页）","2026-10-18T00:00:00Z",{"impact":127,"substance":128,"depth":207,"authority":13,"freshness":21,"relevant":22,"comment":409},"方法规范、数据可靠的区域农用地适宜性案例研究，但属孟加拉地方尺度，对国内三农信息化仅有方法借鉴价值，不宜进入每日精选。",[411],{"name":247,"url":404},[28,51,413,414,415],"农用地适宜性","土地覆被","孟加拉农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[418],{"code":56,"slug":198,"name":199,"path":200,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":420},{"candidates":421,"agricultural_evidence":46},[422,426,430,437,444],{"code":33,"score":128,"supported":46,"title":423,"abstract":424,"tags":425},[51],[49,51,53],[51],{"code":56,"score":207,"supported":46,"title":427,"abstract":428,"tags":429},[60],[28,60],[28],{"code":431,"score":64,"supported":46,"title":432,"abstract":433,"tags":436},"A1",[],[434,435],"稻米","rice",[],{"code":438,"score":64,"supported":46,"title":439,"abstract":440,"tags":443},"G",[],[441,442],"农村发展","rural development",[],{"code":63,"score":64,"supported":46,"title":445,"abstract":446,"tags":447},[],[67,68],[],[449,450],"Savar Upazila 农用地适宜性","Landsat-8 土地覆被分类","SavarUpazila农用地适宜性-5321","10.3329\u002Fbrds.v26i1.94265",{"doi":452,"openalex_id":454,"authors":455,"venue":465,"cited_by_count":106,"oa_url":404,"card":466,"direction":111,"ingested_from":113},"W7222895468",[456,458,460,462],{"name":457,"orcid":9},"Raju Ahmed",{"name":459,"orcid":9},"Shaikh Mehdee Mohammad",{"name":461,"orcid":9},"Ummul Momanin Coalee",{"name":463,"orcid":464},"Khandakar Hasan Mahmud","https:\u002F\u002Forcid.org\u002F0000-0003-1222-0791","The Bangladesh Rural Development Studies",{"tldr":467,"method":468,"finding":469,"direction":111,"opportunity":470},"用GIS与遥感多准则分析评估孟加拉Savar地区潜在农业用地适宜性。","Landsat-8监督分类提取土地利用，结合土壤、地形数据用AHP与MCE做适宜","仅12.42%为农地，高、中、边缘、不适宜分别占24.28%、29.53%、24.66%、21.53","可引入时序遥感与气候情景，动态评估适宜性变化并耦合作物种植决策支持。","2026-10-19T23:30:22.906704Z",{"id":473,"title":474,"url":475,"summary":476,"summary_zh":477,"content":9,"source_name":478,"source_url":475,"published_at":479,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":480,"score_detail":481,"sources":484,"tags":486,"primary_topic":491,"secondary_topics":492,"classification":495,"search_phrases":536,"slug":539,"view_count":159,"doi":9,"paper":540,"created_at":550},5013,"REMOTE SENSING AND GIS-BASED ASSESSMENT OF SOIL SALINITY IN IRRIGATED LANDS: METHODS, CHALLENGES AND PROSPECTS FOR PRECISION AGRICULTURE","http:\u002F\u002Fwww.arabianjournalofscience.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F1223","Soil salinization is a major land degradation process affecting the sustainability of irrigated agriculture, particularly in arid and semi-arid regions. Conventional soil sampling provides essential information about salinity levels but may be insufficient for continuous monitoring of large agricultural territories because of spatial variability, sampling costs, and temporal limitations. Remote sensing (RS) and geographic information systems (GIS) offer complementary approaches for identifying spatial patterns of salinity, monitoring land conditions, and supporting agricultural management. This article examines the scientific and methodological foundations of integrating multispectral satellite imagery, spectral indices, geospatial analysis, and machine learning for soil salinity assessment in irrigated lands. Particular attention is given to Landsat and Sentinel-2 imagery, the interpretation of vegetation and soil reflectance indicators, the role of electrical conductivity measurements in model calibration, and the limitations associated with vegetation cover, soil moisture, and seasonal variability. The analysis indicates that reliable salinity assessment requires the integration of satellite observations with field measurements and environmental variables rather than the isolated application of spectral indices. The article proposes a methodological framework for developing spatially explicit salinity assessment systems applicable to irrigated agricultural landscapes, including those of Central Asia. The framework provides a basis for improving monitoring strategies and supporting evidence-based land management without assuming unverified local measurement results.","土壤盐渍化是影响灌溉农业可持续性的主要土地退化过程，在干旱和半干旱地区尤为突出。传统土壤采样可提供盐分水平的基本信息，但由于空间变异性、采样成本和时间限制，可能不足以对大面积农业区域进行持续监测。遥感（RS）和地理信息系统（GIS）为识别盐渍化空间格局、监测土地状况和支持农业管理提供了互补性方法。本文探讨了整合多光谱卫星影像、光谱指数、地理空间分析和机器学习进行灌溉土地土壤盐渍化评估的科学与方法学基础。文章特别关注Landsat和Sentinel-2影像、植被与土壤反射率指标的解译、电导率测量在模型校准中的作用，以及与植被覆盖、土壤湿度和季节变异性相关的局限性。分析表明，可靠的盐渍化评估需要将卫星观测与野外测量和环境变量相结合，而非孤立地应用光谱指数。本文提出了一个方法学框架，用于开发适用于灌溉农业景观（包括中亚地区）的空间显式盐渍化评估系统。该框架为改进监测策略和支持基于证据的土地管理提供了基础，且不以未经核实的当地测量结果为假设前提。","Arabian journal of science.","2026-10-13T00:00:00Z",74,{"impact":336,"substance":482,"depth":19,"authority":20,"freshness":127,"relevant":22,"comment":483},20,"系统综述遥感与GIS在灌溉农田盐渍化评估中的方法框架与挑战，对盐碱地治理和精准农业有参考价值，但属综述性论文，非突破性成果。",[485],{"name":478,"url":475},[487,488,28,489,490],"智慧农业","精准农业","盐碱地治理","土壤监测",{"code":56,"slug":198,"name":199,"path":200,"parent_code":37},[493,494],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":63,"slug":68,"name":347,"path":348,"parent_code":261},{"status":40,"locked":14,"version":41,"method":42,"evidence":496},{"candidates":497,"agricultural_evidence":46},[498,503,507,511,516,523,527,531],{"code":56,"score":128,"supported":46,"title":499,"abstract":500,"tags":502},[60],[28,60,501],"multispectral",[28],{"code":33,"score":19,"supported":46,"title":504,"abstract":505,"tags":506},[51],[49,50,51,53],[],{"code":63,"score":207,"supported":46,"title":508,"abstract":509,"tags":510},[68],[67,68],[489],{"code":84,"score":13,"supported":46,"title":512,"abstract":514,"tags":515},[513],"precision agriculture",[],[487,488],{"code":517,"score":64,"supported":46,"title":518,"abstract":519,"tags":522},"I2",[],[520,521],"机器学习","machine learning",[],{"code":153,"score":78,"supported":46,"title":524,"abstract":525,"tags":526},[],[156],[],{"code":288,"score":78,"supported":46,"title":528,"abstract":529,"tags":530},[],[291],[],{"code":77,"score":78,"supported":46,"title":532,"abstract":533,"tags":535},[],[534],"methodological framework",[],[537,538],"遥感 GIS 土壤盐渍化","Landsat Sentinel-2 盐分监测","遥感GIS土壤盐渍化-5013",{"doi":9,"openalex_id":541,"authors":542,"venue":478,"cited_by_count":106,"oa_url":475,"card":545,"direction":111,"ingested_from":113},"W7221843179",[543],{"name":544,"orcid":9},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":546,"method":547,"finding":548,"direction":111,"opportunity":549},"综述遥感与GIS评估灌溉地土壤盐渍化的方法、挑战与前景，并提出空间显式评估框架。","整合多光谱卫星影像、光谱指数、GIS空间分析与机器学习，结合电导率校准。","可靠盐渍化评估需卫星观测与实地测量及环境变量融合，而非孤立使用光谱指数。","可探索多源遥感与机器学习融合的盐渍化动态监测，并验证于中亚等干旱灌区。","2026-10-15T23:30:32.401626Z"]