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supervision actually works, yet a heavy schedule of field visits has not translated into effective monitoring on its own. Earlier work largely stops at the direct link between monitoring technology and farming performance, leaving the pathway joining the two thinly and inconsistently evidenced, especially across the Indonesian agritech landscape. This study therefore weighs how four dimensions of satellite monitoring implementation, covering information availability, information accuracy, monitoring speed, and operational utilisation, shape land monitoring effectiveness, while asking whether data-driven decision making carries that influence. The design is quantitative and causal, drawing on 120 field agronomists enumerated as a census, with estimation through partial least squares path modelling. Every dimension lifted both land monitoring effectiveness and data-driven decision making at a meaningful level; the latter in turn raised effectiveness and conveyed the influence of all four. Each dimension travels a different route, since information availability moves largely along the direct path whereas information accuracy contributes almost wholly through decision making, with monitoring speed standing out overall. Investment therefore needs sorting by mechanism rather than treating remote sensing as one uniform construct.","在Agritech PT XYZ的大规模农业经营中，卫星监测对于实现有效监督至关重要，然而繁重的实地巡查日程并未单独转化为有效的监控效果。以往的研究大多止步于监测技术与农业绩效之间的直接关联，对于连接两者的路径所提供的证据既薄弱又不一致，在印尼农业科技领域尤为如此。因此，本研究评估了卫星监测实施的四个维度——信息可用性、信息准确性、监测速度及操作利用——如何影响土地监测有效性，同时探讨数据驱动的决策是否在其中传递这种影响。研究设计采用定量与因果分析方法，基于对120名田间农艺师的全员普查数据，并通过偏最小二乘路径建模进行估计。每个维度均显著提升了土地监测有效性和数据驱动的决策能力；后者反过来又增强了有效性，并传递了所有四个维度的影响。各维度所走的路径各不相同，因为信息可用性主要沿直接路径发挥作用，而信息准确性几乎完全通过决策过程产生贡献，其中监测速度的整体效应最为突出。因此，投资需按机制进行分类，而非将遥感视为一个统一的整体概念。","Lokawati Jurnal Penelitian Manajemen dan Inovasi Riset","2026-09-05T00:00:00Z",56,{"impact":416,"substance":361,"depth":601,"authority":173,"freshness":69,"relevant":455,"comment":662},"研究卫星监测对农业土地管理有效性的影响，细分维度分析，但发表于地方期刊且时效性低。",[664],{"name":658,"url":655},[666,667,590,668],"智慧农业","农业管理","数据驱动",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[],{"status":596,"locked":581,"version":4,"method":597,"evidence":672},{"candidates":673,"agricultural_evidence":602},[674,679],{"code":32,"score":675,"supported":602,"title":676,"abstract":677,"tags":678},7,[],[606,604],[590],{"code":16,"score":455,"supported":581,"title":680,"abstract":681,"tags":682},[],[],[666],[684,685],"农业管理 卫星遥感 数据驱动 智慧农业","农业管理 卫星遥感","农业管理卫星遥感数据驱动智慧农业-1757","10.61132\u002Flokawati.v4i5.2768",{"doi":687,"openalex_id":689,"authors":690,"venue":658,"cited_by_count":424,"oa_url":697,"card":698,"direction":703,"ingested_from":650},"W7208706175",[691,693,695],{"name":692,"orcid":11},"Rani Mutiarawati",{"name":694,"orcid":11},"Widi Winarso",{"name":696,"orcid":11},"Dewi Sri Woelandari Pantjolo Giningroem","https:\u002F\u002Fjournal.arimbi.or.id\u002Findex.php\u002FLokawati\u002Farticle\u002Fdownload\u002F2768\u002F2708",{"tldr":699,"method":700,"finding":701,"direction":649,"opportunity":702},"研究卫星监测对土地监测有效性的影响，并检验数据驱动决策的中介作用。","对120名农艺师进行普查，采用偏最小二乘路径建模分析。","卫星监测各维度均提升监测有效性，数据驱动决策起中介作用，但各维度路径不同。","可探索不同监测维度在特定作物或区域下的差异化机制，优化遥感投资策略。","智慧农业 \u002F 农业物联网","2026-09-06T23:30:08.482478Z",{"technologies":706,"keywords":798},[707,710,712,714,717,720,723,726,729,731,734,737,739,741,743,745,748,751,753,755,757,759,761,763,766,768,770,772,774,776,779,781,783,785,786,788,790,792,794,796],{"name":708,"count":709},"农业人工智能",534,{"name":606,"count":711},213,{"name":589,"count":713},159,{"name":715,"count":716},"精准农业",149,{"name":718,"count":719},"机器学习",97,{"name":721,"count":722},"农业物联网",64,{"name":724,"count":725},"深度学习",58,{"name":727,"count":728},"农业遥感",55,{"name":730,"count":728},"物联网",{"name":732,"count":733},"GIS",37,{"name":735,"count":736},"计算机视觉",34,{"name":738,"count":321},"数字孪生",{"name":740,"count":321},"边缘计算",{"name":742,"count":361},"作物表型",{"name":744,"count":361},"农业机器人",{"name":746,"count":747},"目标检测",17,{"name":749,"count":750},"无人机遥感",13,{"name":752,"count":173},"区块链",{"name":754,"count":173},"联邦学习",{"name":756,"count":675},"农业传感器",{"name":758,"count":675},"图像识别",{"name":760,"count":675},"大语言模型",{"name":762,"count":675},"高光谱遥感",{"name":764,"count":765},"传感器",6,{"name":767,"count":408},"高光谱",{"name":769,"count":400},"图像分割",{"name":771,"count":400},"多光谱成像",{"name":773,"count":400},"大模型",{"name":775,"count":400},"强化学习",{"name":777,"count":778},"农业大模型",3,{"name":780,"count":69},"AI",{"name":782,"count":69},"人工智能",{"name":784,"count":69},"农产品溯源",{"name":590,"count":69},{"name":787,"count":69},"地理信息系统",{"name":789,"count":69},"基因编辑",{"name":791,"count":69},"多模态",{"name":793,"count":69},"多源遥感",{"name":795,"count":69},"无人机多光谱",{"name":797,"count":455},"生物育种",[799,801,804,806,808,811,813,815,817,819,822,824],{"name":666,"count":800},730,{"name":802,"count":803},"数字乡村",108,{"name":805,"count":377},"土地利用",{"name":807,"count":377},"数字农业",{"name":809,"count":810},"产量预测",38,{"name":812,"count":477},"粮食安全",{"name":814,"count":516},"可解释AI",{"name":210,"count":816},21,{"name":818,"count":508},"病害识别",{"name":820,"count":821},"智能灌溉",19,{"name":823,"count":361},"水资源管理",{"name":825,"count":747},"农业信息化"]