[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2954":3,"related-2954":46},{"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":18,"tags":20,"search_phrases":24,"slug":27,"view_count":15,"doi":28,"paper":29,"created_at":45},2954,"Quantifying the spatiotemporal dynamics and drivers of urban blue spaces in the Beijing–Tianjin–Hebei region using remote sensing","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.indic.2026.101525","Urban blue spaces are essential for water security, ecological restoration, and urban livability, yet long-term assessments often emphasize changes in area while broader landscape-pattern changes remain less well understood. Using the Beijing–Tianjin–Hebei (BTH) region as a case study, this study examined changes in blue-space area and the Composite Landscape Index (CI) from 2000 to 2023 and compared their associations with demographic, socioeconomic, and environmental factors. Blue-space area declined significantly across the region, with a clear change point in 2009, accompanied by increasing fragmentation and declining structural connectivity. CI also decreased significantly at the regional scale, whereas the city-level trends of blue-space area and CI were not always aligned. Population, vegetation conditions (FVC and NDVI), and urbanization jointly accounted for approximately 84%–87% of the model-based importance for both blue-space area and CI, although their relative importance differed between the two variables. These findings show that blue-space area and CI describe different dimensions of long-term blue-space change, and that regional trends may mask substantial city-level differences. By distinguishing areal change from landscape-pattern change across regional and city scales, this study broadens the perspective of urban blue-space assessment, advances the understanding of its multidimensional dynamics, and provides a basis for long-term monitoring, cross-city comparison, and differentiated assessment in rapidly urbanizing regions.","城市蓝空间对水安全、生态修复和城市宜居性至关重要，但长期评估往往侧重于面积变化，而更广泛的景观格局变化仍认识不足。本研究以京津冀（BTH）地区为案例，考察了2000—2023年蓝空间面积和复合景观指数（Composite Landscape Index，CI）的变化，并比较了二者与人口、社会经济和环境因素的关联。整个区域的蓝空间面积显著下降，并在2009年出现明显突变点，同时伴随破碎化加剧和结构连通性下降。CI在区域尺度上也显著下降，而城市尺度的蓝空间面积与CI趋势并不总是一致。人口、植被状况（FVC和NDVI）和城市化共同解释了蓝空间面积和CI基于模型重要性的约84%—87%，但二者的相对重要性存在差异。这些发现表明，蓝空间面积和CI描述了长期蓝空间变化的不同维度，区域趋势可能掩盖了城市间的显著差异。通过区分区域和城市尺度上的面积变化与景观格局变化，本研究拓宽了城市蓝空间评估的视角，增进了对其多维动态的理解，并为快速城市化地区的长期监测、跨城市比较和差异化评估提供了依据。",null,"Environmental and Sustainability Indicators","2026-09-16T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"研究京津冀城市蓝空间的时空动态与驱动因素，属城市生态与遥感领域，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23],"京津冀","遥感监测","城市蓝空间",[25,26],"京津冀 城市蓝空间 遥感","城市蓝空间 遥感监测 京津冀","京津冀城市蓝空间遥感-2954","10.1016\u002Fj.indic.2026.101525",{"doi":28,"openalex_id":30,"authors":31,"venue":10,"cited_by_count":15,"oa_url":37,"card":38,"direction":42,"ingested_from":44},"W7213412393",[32,34],{"name":33,"orcid":9},"Weina Zhen",{"name":35,"orcid":36},"Donghui Shi","https:\u002F\u002Forcid.org\u002F0000-0001-5301-368X","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2665972726004149\u002Fpdf",{"tldr":39,"method":40,"finding":41,"direction":42,"opportunity":43},"基于遥感量化京津冀城市蓝空间2000-2023年面积与景观格局变化及其驱动因素。","遥感提取蓝空间面积与综合景观指数，结合人口、社会经济和植被因子建模。","蓝空间面积显著下降且2009年出现拐点，破碎化加剧，区域趋势掩盖城市间差异。","农业遥感与作物表型","可延伸至蓝空间与农业灌溉、水安全及城乡水生态协同的多尺度遥感监测研究。","openalex","2026-09-19T23:30:37.135491Z",{"total":47,"page":48,"page_size":47,"items":49},6,1,[50,103,152,196,244,312],{"id":51,"title":52,"url":53,"summary":54,"summary_zh":55,"content":9,"source_name":56,"source_url":53,"published_at":57,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":58,"score_detail":59,"sources":66,"tags":68,"search_phrases":73,"slug":76,"view_count":15,"doi":77,"paper":78,"created_at":102},2955,"Precision agriculture applied to coffee cultivation in agroforestry systems","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10457-026-01651-z","Abstract The search for more sustainable agricultural production systems is a goal of both agroforestry systems (AFS) and precision agriculture (PA). In this context, integrating PA technologies into coffee cultivation under AFS presents the potential to increase productive efficiency and sustainability. Thus, the objective of this study was to evaluate whether the variability of environmental conditions in coffee agroforestry systems can be estimated using technologies associated with PA. The studies were conducted on three coffee farms in Coimbra and Araponga, Minas Gerais, Brazil. For management zone delineation, combinations of apparent soil electrical conductivity (ECa), Normalized Difference Vegetation Index (NDVI), and Digital Terrain Model (DTM) were used. Microclimatic variability was evaluated using weather stations, considering different agroforestry management practices and altitude conditions in mountain coffee cultivation. Canopy cover was estimated by digital image processing techniques applied to aerial images obtained by an Unmanned Aerial Vehicle (UAV). The combination ECa + NDVI was efficient in delineating zones with distinct physical and chemical attributes. Agroforestry management practices and altitude influenced temperature and relative humidity. Tree canopy estimation proved viable to support management. It is concluded that PA is strategic for coffee cultivation in AFS, favoring site-specific and more sustainable management.","摘要 寻求更可持续的农业生产系统是农林业系统（AFS）和精准农业（PA）的共同目标。在此背景下，将精准农业技术整合到农林业系统下的咖啡种植中，具有提高生产效率和可持续性的潜力。因此，本研究旨在评估是否可以利用与精准农业相关的技术来估算咖啡农林业系统中环境条件的变异性。研究在巴西米纳斯吉拉斯州科英布拉和阿拉蓬加的三个咖啡农场进行。为划定管理区，采用了表观土壤电导率（ECa）、归一化差异植被指数（NDVI）和数字地形模型（DTM）的组合。利用气象站评估微气候变异性，考虑了山地咖啡种植中不同的农林业管理措施和海拔条件。通过将数字图像处理技术应用于无人机（UAV）获取的航空图像来估算冠层覆盖度。ECa + NDVI的组合能够有效划定具有不同物理和化学属性的区域。农林业管理措施和海拔影响了温度和相对湿度。树木冠层估算被证明可用于支持管理。结论是，精准农业对农林业系统下的咖啡种植具有战略意义，有利于因地制宜且更可持续的管理。","Agroforestry Systems","2026-09-18T00:00:00Z",71,{"impact":60,"substance":61,"depth":62,"authority":63,"freshness":64,"relevant":48,"comment":65},12,21,17,13,8,"巴西咖啡农林复合系统中应用精准农业技术的研究，方法新颖、结论可靠，对智慧农业与遥感应用有参考价值，但属区域性研究，影响范围有限。",[67],{"name":56,"url":53},[69,70,22,71,72],"智慧农业","精准农业","农林复合系统","咖啡种植",[74,75],"巴西 咖啡 农林复合系统 精准农业","无人机 冠层覆盖 NDVI 咖啡","巴西咖啡农林复合系统精准农业-2955","10.1007\u002Fs10457-026-01651-z",{"doi":77,"openalex_id":79,"authors":80,"venue":56,"cited_by_count":15,"oa_url":53,"card":95,"direction":101,"ingested_from":44},"W7213541962",[81,83,86,89,92],{"name":82,"orcid":9},"Wagner Silva dos Santos",{"name":84,"orcid":85},"Francisco de Assis de Carvalho Pinto","https:\u002F\u002Forcid.org\u002F0000-0002-8279-9535",{"name":87,"orcid":88},"André Luiz de Freitas Coelho","https:\u002F\u002Forcid.org\u002F0000-0002-7595-9713",{"name":90,"orcid":91},"Daniel Marçal de Queiroz","https:\u002F\u002Forcid.org\u002F0000-0003-0987-3855",{"name":93,"orcid":94},"Bruno Nery Fernandes Vasconcelos","https:\u002F\u002Forcid.org\u002F0000-0001-6298-9748",{"tldr":96,"method":97,"finding":98,"direction":99,"opportunity":100},"评估精准农业技术能否估算农林复合咖啡系统的环境变异，以支持可持续管理。","用ECa、NDVI、DTM划分管理区，气象站测微气候，无人机图像估树冠覆盖。","ECa+NDVI可有效划分土壤属性差异区，农林管理和海拔影响温湿度，树冠估算可行。","智慧农业 \u002F 农业物联网","可探索多源遥感与物联网融合的实时管理区动态划分，并验证其对咖啡产量与碳汇的长期效应。","数字乡村与农业信息化","2026-09-19T23:30:40.775897Z",{"id":104,"title":105,"url":106,"summary":107,"summary_zh":108,"content":9,"source_name":109,"source_url":106,"published_at":57,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":110,"score_detail":111,"sources":115,"tags":117,"search_phrases":122,"slug":125,"view_count":15,"doi":126,"paper":127,"created_at":151},2946,"Forecasting cocoa export volumes in Indonesia using remote sensing, climate variability, and economic indicators: A comparative modeling approach","https:\u002F\u002Fdoi.org\u002F10.1177\u002F18747655261486415","Accurate forecasting of agricultural exports is essential for supporting production planning, trade management, and evidence-based policymaking. However, forecasting cocoa exports remains challenging because export volumes are influenced by interconnected environmental, climatic, and economic factors. This study proposes an integrated forecasting framework for Indonesian cocoa export volumes by incorporating remote sensing indicators, climatic indices, and economic variables into statistical, machine learning, deep learning, and hybrid forecasting approaches. The exogenous variables consist of Enhanced Vegetation Index (EVI), Land Surface Temperature (LST), rainfall, cocoa price, Oceanic Niño Index (ONI), Dipole Mode Index (DMI), and exchange rate. The study evaluates benchmark models (Naïve, Seasonal Naïve, and ETS), statistical time-series models (ARIMAX and SARIMAX), machine learning and deep learning models (XGBoost and LSTM), and a hybrid SARIMAX–XGBoost model. Model performance is evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) over an independent 17-month test horizon from November 2023 to March 2025. XGBoost achieved the overall best forecasting performance, obtaining the lowest test RMSE of 2,609,038 kg, MAE of 2,096,098 kg, and MAPE of 7.56%. Its advantage over ARIMAX, SARIMAX, and the SARIMAX–XGBoost hybrid was supported by pairwise comparisons using the modified Diebold–Mariano (DM) and Kolmogorov–Smirnov Predictive Accuracy (KSPA) tests. The comparison with LSTM showed no statistically significant difference according to the DM test, although the KSPA test indicated a significant difference. These findings demonstrate that XGBoost provided the strongest out-of-sample forecasting performance among the evaluated models.","农业出口的准确预测对于支持生产规划、贸易管理和循证政策制定至关重要。然而，可可出口预测仍然具有挑战性，因为出口量受到环境、气候和经济因素相互交织的影响。本研究提出了一种印度尼西亚可可出口量的集成预测框架，将遥感指标、气候指数和经济变量纳入统计、机器学习、深度学习和混合预测方法中。外生变量包括增强植被指数（EVI）、地表温度（LST）、降雨量、可可价格、海洋尼诺指数（ONI）、偶极子模态指数（DMI）和汇率。本研究评估了基准模型（Naïve、Seasonal Naïve和ETS）、统计时间序列模型（ARIMAX和SARIMAX）、机器学习和深度学习模型（XGBoost和LSTM）以及SARIMAX–XGBoost混合模型。模型性能通过均方根误差（RMSE）、平均绝对误差（MAE）和平均绝对百分比误差（MAPE）在2023年11月至2025年3月的独立17个月测试期内进行评估。XGBoost取得了总体最佳的预测性能，测试RMSE最低为2,609,038 kg，MAE为2,096,098 kg，MAPE为7.56%。其相对于ARIMAX、SARIMAX和SARIMAX–XGBoost混合模型的优势得到了使用修正Diebold–Mariano（DM）检验和Kolmogorov–Smirnov预测精度（KSPA）检验的成对比较的支持。与LSTM的比较显示，根据DM检验无统计学显著差异，尽管KSPA检验表明存在显著差异。这些发现表明，在所评估的模型中，XGBoost提供了最强的样本外预测性能。","Statistical Journal of the IAOS",73,{"impact":60,"substance":112,"depth":62,"authority":63,"freshness":113,"relevant":48,"comment":114},22,9,"将遥感、气候指数与经济变量整合进可可出口预测框架，XGBoost 表现最优，方法新颖且结论可靠，对农产品贸易信息化有参考价值。",[116],{"name":109,"url":106},[118,22,119,120,121],"机器学习","可可出口","农业预测","气候指数",[123,124],"印尼 可可 出口 预测","遥感 气候 可可 出口","印尼可可出口预测-2946","10.1177\u002F18747655261486415",{"doi":126,"openalex_id":128,"authors":129,"venue":109,"cited_by_count":15,"oa_url":9,"card":145,"direction":42,"ingested_from":44},"W7213558756",[130,133,135,137,140,143],{"name":131,"orcid":132},"Erna Nurmawati","https:\u002F\u002Forcid.org\u002F0009-0002-3385-673X",{"name":134,"orcid":9},"Neli Agustina",{"name":136,"orcid":9},"Robert Kurniawan",{"name":138,"orcid":139},"Prana Ugiana Gio","https:\u002F\u002Forcid.org\u002F0000-0002-8155-005X",{"name":141,"orcid":142},"Rayhan Abyasa","https:\u002F\u002Forcid.org\u002F0009-0006-0476-7999",{"name":144,"orcid":9},"Aditya Hari Kurnia Putra",{"tldr":146,"method":147,"finding":148,"direction":149,"opportunity":150},"融合遥感、气候与经济指标，比较多种模型预测印尼可可出口量，XGBoost表现最佳。","用EVI、LST、降水、ONI、DMI、价格、汇率等变量，比较ARIMAX、SA","XGBoost预测精度最高，MAPE为7.56%，显著优于统计模型和混合模型。","农业人工智能与决策模型","可探索多源遥感与气候指数融合的混合模型，提升农产品出口预测精度与可解释性。","2026-09-19T23:30:33.176995Z",{"id":153,"title":154,"url":155,"summary":156,"summary_zh":157,"content":9,"source_name":158,"source_url":155,"published_at":57,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":159,"score_detail":160,"sources":164,"tags":166,"search_phrases":171,"slug":174,"view_count":15,"doi":175,"paper":176,"created_at":195},2943,"SPATIO-TEMPORAL DYNAMICS OF LAND USE AND LAND COVER IN A GRANITE QUARRYING AREA IN MANÉAH, GUINEA: A REMOTE SENSING AND GIS-BASED ANALYSIS FROM 1980 TO 2024","https:\u002F\u002Fdoi.org\u002F10.30574\u002Fgscarr.2026.28.3.0222","Quarrying is a major driver of transformation in peri-urban areas, particularly in spaces experiencing both rapid urbanization and increasing pressure on natural resources. This study analyzes the spatio-temporal dynamics of land cover in the urban commune of Manéah, Guinea, between 1980, 2020, and 2024, in order to characterize the territorial changes associated with the development of extractive activities and urban expansion. The methodological approach relies on the use of multi-temporal satellite imagery, its processing in a GIS environment, supervised classification of the main land cover classes, diachronic analysis of changes, construction of a transition matrix, and evaluation of the quality of the classifications using the Kappa index. The results highlight a profound transformation of the landscape. Between 1980 and 2024, the proportion of built-up areas increased from 7.22% to 32.67%, a rise of 25.45 percentage points, while quarries and construction sites, absent in 1980, represented 2.05% of the territory in 2024, after reaching 7.50% in 2020. Conversely, cultivated land and fallow land increased overall by 18.58 percentage points. In contrast, natural formations experienced a sharp decline: shrub savannas decreased from 40.50% to 1.73%, bodies of water from 31.46% to 11.30%, and mangroves from 8.85% to 0.84%. The Kappa values of 0.76, 0.79, and 0.82 indicate a satisfactory agreement between the classifications. These changes reflect the increasing human impact on the territory, resulting from the combined effects of urbanization, agriculture, and extractive activities. The change matrix also confirms a structural recomposition of the territory, with built-up areas increasing from 963.406 ha to 4,357.55 ha, while shrub savannas decreased from 5,401.819 ha to only 230.389 ha over the entire study period. The study thus highlights the need to further integrate remote sensing data and GIS tools into land-use planning, environmental monitoring, and the spatial management of quarries in Manéah.","采石活动是城郊地区转型的主要驱动力，尤其是在同时经历快速城市化和自然资源压力加剧的区域。本研究分析了几内亚马内阿（Manéah）城市公社在1980年、2020年和2024年的土地覆盖时空动态，以刻画与采掘活动发展和城市扩张相关的领土变化。研究方法依赖于多时相卫星影像的使用、在GIS环境中的处理、主要土地覆盖类别的监督分类、变化的历史对比分析、转移矩阵的构建，以及利用Kappa指数评估分类质量。结果揭示了景观的深刻转变。1980年至2024年间，建成区比例从7.22%增至32.67%，上升了25.45个百分点；而采石场和建筑工地1980年尚不存在，2020年达到7.50%，2024年占领土的2.05%。相反，耕地和休耕地总体增加了18.58个百分点。相比之下，自然 formations 急剧减少：灌木草原从40.50%降至1.73%，水体从31.46%降至11.30%，红树林从8.85%降至0.84%。Kappa值分别为0.76、0.79和0.82，表明分类之间具有令人满意的一致性。这些变化反映了人类对领土日益加剧的影响，这是城市化、农业和采掘活动共同作用的结果。变化矩阵也证实了领土的结构性重组，建成区从963.406公顷增至4，357.55公顷，而灌木草原在整个研究期内从5，401.819公顷降至仅230.389公顷。因此，本研究强调了进一步将遥感数据和GIS工具纳入马内阿的土地利用规划、环境监测和采石场空间管理的必要性。","GSC Advanced Research and Reviews",60,{"impact":64,"substance":161,"depth":162,"authority":13,"freshness":113,"relevant":48,"comment":163},18,15,"基于多时相遥感与GIS的矿区土地利用变化研究，方法规范、数据翔实，但地域性强、与国内农业信息化关联有限，可作为遥感应用案例参考。",[165],{"name":158,"url":155},[167,22,168,169,170],"城镇化","土地利用","GIS","矿区生态",[172,173],"Manéah 几内亚 土地利用","遥感 GIS 采石场 土地覆盖","Manéah几内亚土地利用-2943","10.30574\u002Fgscarr.2026.28.3.0222",{"doi":175,"openalex_id":177,"authors":178,"venue":158,"cited_by_count":15,"oa_url":155,"card":190,"direction":42,"ingested_from":44},"W7213550535",[179,181,183,186,188],{"name":180,"orcid":9},"Sogbè KEITA",{"name":182,"orcid":9},"Mamady Minata CONDÉ",{"name":184,"orcid":185},"Ibrahima Thiam","https:\u002F\u002Forcid.org\u002F0000-0001-8553-1043",{"name":187,"orcid":9},"Lucien SOLIE",{"name":189,"orcid":9},"Alpha Issaga Pallé Diallo",{"tldr":191,"method":192,"finding":193,"direction":42,"opportunity":194},"基于遥感与GIS分析几内亚Manéah采石区1980-2024年土地利用与覆盖的时空变化。","多时相卫星影像、GIS监督分类、变化矩阵与Kappa精度评价。","建设用地由7.22%升至32.67%，灌丛草原由40.50%降至1.73%，采石场2024年占2.0","可延伸至采石与城市化对周边农地、红树林的耦合影响及生态修复监测研究。","2026-09-19T23:30:32.836654Z",{"id":197,"title":198,"url":199,"summary":200,"summary_zh":201,"content":9,"source_name":202,"source_url":199,"published_at":57,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":203,"score_detail":204,"sources":208,"tags":210,"search_phrases":215,"slug":218,"view_count":15,"doi":219,"paper":220,"created_at":243},2941,"Drought dynamics and climatic drivers in the Tarim Basin using remote sensing indices and pixel-wise machine learning","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-72142-5","Abstract Drought characterization in hyper-arid endorheic basins requires multi-index approaches that capture distinct hydrometeorological processes. This study investigates spatio-temporal drought dynamics in the Tarim Basin (TB)—China’s largest inland arid region—using two complementary remote sensing indices: the Temperature Vegetation Dryness Index (TVDI) for landscape-scale moisture and the Crop Water Stress Index (CWSI) for agricultural drought. Based on 2000–2024 remote sensing and meteorological data, we employed a pixel-wise Random Forest framework with spatial cross-validation and permutation importance analysis to quantify climatic drivers across the TB. Results reveal a fundamental “core-periphery” dichotomy: TVDI identifies persistent extreme drought in the Taklamakan Desert core, while CWSI reveals alleviating water stress in peripheral oasis farmlands (73.21% showing significant decrease, p \u003C 0.05). Despite regional warming-wetting trends, TVDI exhibited an insignificant decrease (54.72% of the basin), contrasting with CWSI's significant agricultural drought alleviation. Vapor Pressure Deficit (VPD)—a key atmospheric dryness indicator—exhibited high relative permutation importance for both drought indices (72–75%), considerably exceeding the values obtained for precipitation (6–8%) within the Tarim Basin. Secondary drivers diverge by land surface type: TVDI responds to Relative Humidity (8.2%) and Precipitation (6.1%), while CWSI is modulated by Land Surface Temperature (9.4%) and Sunshine Hours (7.8%). Partial correlation analyses controlling for topography and temperature confirm VPD’s independent effect on drought severity. Large-scale climate oscillations, particularly the Arctic Oscillation (AO) and ENSO-PDO interactions, significantly modulate interannual drought variability (r = 0.74–0.75, p \u003C 0.01). This study provides the first pixel-scale quantification of the relative dominance of atmospheric water demand over precipitation in driving drought evolution in the Tarim Basin, with VPD contributing 72–75% of the total permutation importance compared to 6–8% for precipitation. This quantitative benchmark offers actionable parameters for drought monitoring systems in arid regions and underscores the need to integrate VPD and large-scale climate signals into early warning frameworks.","摘要 极端干旱内流盆地的干旱特征刻画需要能够捕捉不同水文气象过程的多指标方法。本研究利用两个互补的遥感指数——用于景观尺度土壤湿度的温度植被干旱指数（TVDI）和用于农业干旱的作物水分胁迫指数（CWSI）——探讨了塔里木盆地（TB）——中国最大的内陆干旱区——干旱的时空动态。基于2000—2024年遥感与气象数据，我们采用逐像元随机森林框架，结合空间交叉验证和置换重要性分析，量化了塔里木盆地气候驱动因子的作用。结果揭示了一种根本性的“核心—边缘”二分格局：TVDI识别出塔克拉玛干沙漠核心区持续存在的极端干旱，而CWSI则显示外围绿洲农田的水分胁迫正在缓解（73.21%呈显著下降，p \u003C 0.05）。尽管区域呈现暖湿化趋势，TVDI却表现出不显著的下降（占流域面积的54.72%），这与CWSI所反映的农业干旱显著缓解形成对比。饱和水汽压差（VPD）——一个关键的大气干燥度指标——对两个干旱指数均表现出较高的相对置换重要性（72%—75%），远超塔里木盆地降水所对应的值（6%—8%）。次要驱动因子因地表类型而异：TVDI响应相对湿度（8.2%）和降水（6.1%），而CWSI受地表温度（9.4%）和日照时数（7.8%）调控。控制地形和温度后的偏相关分析证实了VPD对干旱严重程度的独立影响。大尺度气候振荡，尤其是北极涛动（AO）和ENSO-PDO相互作用，显著调控着年际干旱变率（r = 0.74—0.75，p \u003C 0.01）。本研究首次在像元尺度上量化了大气需水量相对于降水在驱动塔里木盆地干旱演变中的相对主导地位，其中VPD贡献了总置换重要性的72%—75%，而降水仅贡献6%—8%。这一定量基准为干旱区干旱监测系统提供了可操作的参数，并凸显了将VPD和大尺度气候信号纳入预警框架的必要性。","Scientific Reports",81,{"impact":161,"substance":205,"depth":161,"authority":206,"freshness":64,"relevant":48,"comment":207},23,14,"首次在像元尺度量化VPD对干旱的主导作用，方法新颖、数据跨度长，对干旱预警系统建设有实质参考价值。",[209],{"name":202,"url":199},[211,212,22,213,214],"农业遥感","气候变化","干旱预警","塔里木盆地",[216,217],"塔里木盆地 遥感 干旱","TVDI CWSI 干旱监测","塔里木盆地遥感干旱-2941","10.1038\u002Fs41598-026-72142-5",{"doi":219,"openalex_id":221,"authors":222,"venue":202,"cited_by_count":15,"oa_url":199,"card":238,"direction":42,"ingested_from":44},"W7213539719",[223,225,227,229,232,234,236],{"name":224,"orcid":9},"Mutallip Sattar",{"name":226,"orcid":9},"Alim Abbas",{"name":228,"orcid":9},"Sardar Parhat",{"name":230,"orcid":231},"Alimujiang Yasen","https:\u002F\u002Forcid.org\u002F0000-0002-9860-7921",{"name":233,"orcid":9},"Muhemaiti Wahafu",{"name":235,"orcid":9},"Akida Salam",{"name":237,"orcid":9},"Batur Bake",{"tldr":239,"method":240,"finding":241,"direction":42,"opportunity":242},"基于遥感指数与逐像元机器学习，量化塔里木盆地2000—2024年干旱动态及气候驱动因子。","TVDI与CWSI双指数，逐像元随机森林、空间交叉验证与置换重要性分析。","干旱呈核心—边缘分异，VPD贡献72–75%远超降水的6–8%，主导干旱演变。","可将VPD与大尺度气候振荡纳入干旱预警，并拓展至其他干旱内陆盆地的逐像元归因研究。","2026-09-19T23:30:32.698746Z",{"id":245,"title":246,"url":247,"summary":248,"summary_zh":249,"content":9,"source_name":250,"source_url":247,"published_at":251,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":252,"score_detail":253,"sources":255,"tags":257,"search_phrases":262,"slug":265,"view_count":15,"doi":266,"paper":267,"created_at":311},2937,"Modeling the effects of livestock rotation frequency on forage production and soil carbon using a spatially explicit grazing distribution","https:\u002F\u002Fdoi.org\u002F10.1080\u002F17583004.2026.2732593","Livestock grazing strongly influences terrestrial ecosystems and carbon (C) dynamics, but outcomes depend on grazing management. High-frequency rotation (HFR) grazing may reduce the uneven grazing distribution commonly associated with low-frequency rotation (LFR), which can alter forage production and soil organic carbon (SOC). However, most ecosystem models do not explicitly represent uneven grazing distribution, limiting their ability to evaluate management effects. We enhanced the MEMS ecosystem model by incorporating discrete spatial units to simulate grazing distribution driven by key environmental drivers, including forage availability and quality, and distance to water. Using remote sensing-derived enhanced vegetation index (EVI), we verified the simulated grazing distribution at an experimental rangeland site in Oklahoma. We tested the model’s sensitivity to rotation frequency under different management (stocking rate, timing, and duration) and weather (typical, dry, and wet) scenarios. Simulated light and moderate grazing generally sustained forage production and SOC regardless of rotation frequency. However, under heavy grazing, the 30-year projection showed HFR reduced localized overgrazing and sustained higher pasture average forage productivity and SOC stocks than LFR. Long-term experimental data are needed to validate these results. Our study offers guidance for designing grazing management experiments to address this critical knowledge gap and advance C management strategies.","牲畜放牧对陆地生态系统和碳（C）动态具有强烈影响，但其结果取决于放牧管理方式。高频轮牧（HFR）可能减少通常与低频轮牧（LFR）相关的不均匀放牧分布，从而改变饲草产量和土壤有机碳（SOC）。然而，大多数生态系统模型并未明确表征不均匀放牧分布，限制了其评估管理效果的能力。我们通过引入离散空间单元增强了MEMS生态系统模型，以模拟由关键环境驱动因素（包括饲草可获得性和质量，以及与水源的距离）驱动的放牧分布。利用遥感反演的增强植被指数（EVI），我们在俄克拉荷马州一个实验性牧场验证了模拟的放牧分布。我们测试了模型在不同管理（载畜率、时间和持续时间）和天气（典型、干旱和湿润）情景下对轮牧频率的敏感性。模拟的轻度与中度放牧无论轮牧频率如何，通常均能维持饲草生产和SOC。然而，在重度放牧下，30年预测显示HFR减少了局部过度放牧，并比LFR维持了更高的牧场平均饲草生产力和SOC储量。需要长期实验数据来验证这些结果。我们的研究为设计放牧管理实验提供了指导，以弥补这一关键知识空白并推进碳管理策略。","Carbon Management","2026-09-17T00:00:00Z",75,{"impact":60,"substance":112,"depth":161,"authority":206,"freshness":113,"relevant":48,"comment":254},"改进MEMS模型显式模拟放牧空间分布，揭示高频轮牧在重度放牧下维持牧草生产与土壤碳的优势，方法新颖但尚缺长期实测验证，属细分领域有价值研究。",[256],{"name":250,"url":247},[258,22,259,260,261],"土壤有机碳","草地碳汇","轮牧管理","生态模型",[263,264],"MEMS模型 放牧分布","俄克拉荷马 轮牧 土壤碳","MEMS模型放牧分布-2937","10.1080\u002F17583004.2026.2732593",{"doi":266,"openalex_id":268,"authors":269,"venue":250,"cited_by_count":15,"oa_url":304,"card":305,"direction":99,"ingested_from":44},"W7213466883",[270,272,275,277,280,282,285,287,290,292,294,296,298,300,302],{"name":271,"orcid":9},"Yao Zhang",{"name":273,"orcid":274},"Rafael S. Santos","https:\u002F\u002Forcid.org\u002F0000-0003-4829-0644",{"name":276,"orcid":9},"Emma Hamilton",{"name":278,"orcid":279},"Paige Stanley","https:\u002F\u002Forcid.org\u002F0000-0002-4444-3351",{"name":281,"orcid":9},"Hao Yang",{"name":283,"orcid":284},"Keith Paustian","https:\u002F\u002Forcid.org\u002F0000-0001-5262-5596",{"name":286,"orcid":9},"Erica L. Patterson",{"name":288,"orcid":289},"Stephen M. Ogle","https:\u002F\u002Forcid.org\u002F0000-0003-1899-7446",{"name":291,"orcid":9},"Isabella C.F. Maciel",{"name":293,"orcid":9},"Guilhermo F. S. Congio",{"name":295,"orcid":9},"Hugh Aljoe",{"name":297,"orcid":9},"Alejandro Romero-Ruiz",{"name":299,"orcid":9},"Jeff J. Goodwin",{"name":301,"orcid":9},"Jason Rowntree",{"name":303,"orcid":9},"M. Francesca Cotrufo","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F17583004.2026.2732593?needAccess=true",{"tldr":306,"method":307,"finding":308,"direction":309,"opportunity":310},"改进MEMS模型模拟放牧分布，评估轮牧频率对牧草生产和土壤碳的影响。","增强MEMS模型空间离散单元，用EVI遥感验证，模拟不同放牧和天气情景。","重度放牧下高频轮牧减少局部过牧，维持更高牧草生产力和土壤有机碳。","农业绿色发展与碳","需长期实验验证模型结果，并探索不同气候和放牧管理下土壤碳动态的普适性机制。","2026-09-19T23:30:13.860699Z",{"id":313,"title":314,"url":315,"summary":316,"summary_zh":9,"content":317,"source_name":318,"source_url":9,"published_at":57,"category":319,"cover_url":9,"hotness":13,"is_selected":14,"score":320,"score_detail":321,"sources":324,"tags":326,"search_phrases":331,"slug":334,"view_count":15,"doi":9,"paper":9,"created_at":335},2898,"苏垦农发神农慧种农业AI大模型规模化落地:天空地一体化闭环,百万亩自有农田实景数据","https:\u002F\u002Fcaifuhao.eastmoney.com\u002Fnews\u002F20260918101757264727920","苏垦农发9月18日发文,公司依托百万亩自有连片高标准农田,持续产出真实大田数据训练神农慧种农业AI智能体,实现天空地一体化数据闭环:空中多光谱无人机集群常态化农田巡测;地面全域四情监测传感器、北斗智能农机、智能灌溉终端;云端苏垦智云平台+神农慧种AI模型,形成采集数据→AI分析研判→输出水肥植保方案→农机落地执行完整闭环。苏垦智云是全国农林牧渔领域唯一入选工信部信创典型案例的农业数字化平台。","[在东方财富看资讯行情，选东方财富证券一站式开户交易>>](https:\u002F\u002Facttg.eastmoney.com\u002Fpub\u002Fwebtg_hskh_act_zixun_01_01_01_0)\n\n（国内A股找不到第二家，像苏垦农发依托百万亩自有连片高标准农田，持续产出真实大田数据训练神农慧种农业AI智能体；苏垦实现天空地一体化数据闭环，苏垦智云是农林牧渔唯一工信部信创典型案例，智慧农业+低空经济双主线落地。）\n\n- 空中：多光谱无人机集群开展农田巡测；\n\n![Image 1](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002FB9B393E75E9CCC55E11A2686662765C9_w1080h720.jpg)\n\n- 地面：农田四情监测传感器、北斗智能农机；\n\n![Image 2](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F4D780511671CDAB50B4A1CCFE057703D_w1339h892.jpg)\n\n- 云端：苏垦智云平台与神农慧种AI模型，形成「采集数据→AI分析研判→输出水肥植保方案→农机落地执行」完整闭环。\n\n![Image 3](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F1059027DFA2A605F6861D7C69D6D4309_w1440h1080.jpg)\n\n百万亩自有连片农田源源不断产出真实田间数据，持续迭代优化AI模型。国内很多农业AI企业仅拥有小片试验田，唯有苏垦农发拥有大规模现代农业实景数据用于农业模型训练。\n\n苏垦智云平台，也是全国农林牧渔领域唯一入选工信部信创典型案例的农业数字化平台。苏垦农发一一智慧农业与低空经济的天空地一体化闭环落地，AI大田规模化实体应用！\n\n苏垦农发打造天空地一体化智慧农业完整体系，AI大田并非实验室试验，而是在百万亩自有连片农田实现规模化落地运行。\n\n1、空中：多光谱无人机集群常态化农田巡测，低空遥感采集作物长势、病虫害、墒情数据；依托农业农村部低空技术创新重点实验室，主攻低空多模态农情感知。\n\n2、地面：全域农田“四情”监测传感器、北斗智能农机、智能灌溉终端，实时采集土壤、苗情、虫情、气象数据。\n\n3、云端：苏垦智云平台 神农慧种农业AI智能体，形成完整闭环：采集田间数据→AI模型分析研判→输出水肥、植保作业处方→下发农机执行落地。\n\n4、核心稀缺壁垒：手握百万亩自有连片高标准农田，源源不断产出真实大田实景数据，持续迭代训练神农慧种AI模型。\n\n国内绝大多数农业AI企业，仅拥有小片试验田做演示；苏垦是少数拥有大规模真实农业场景用于模型训练与生产验证的实体龙头。\n\n5、苏垦智云一体化平台，也是全国农林牧渔领域唯一入选工信部信创典型案例的农业数字化平台，国产化底层架构，是农业数字化可复制的标杆样板。配套全国首个农业农村部农业低空技术创新重点实验室（苏垦为依托单位、河海大学共建），同步布局低空经济与智慧农业新质生产力。\n\n2026-09-18 11:16:07 作者更新了以下内容\n\n全球领先的风险咨询公司Verisk Maplecroft 在周四（9月17日）发布的一份报告中表示，极端天气灾害将加剧亚洲的粮食安全风险，并可能在印度、印尼和菲律宾等脆弱的国家引发动荡。\n\n![Image 4](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F9ACF9C92E7B35A242970CDC4D55B8DA9_w1080h15645.jpg)\n\n![Image 5](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002FFE1BF075831D4DAEB94ADAE924123EE9_w1080h2400.jpg)\n\n2026-09-18 21:02:06 作者更新了以下内容\n\n苏垦农发一一 AI赋能农业真实落地案例：临海农场——国内首个10万亩级无人值守巡田农场（核心标杆）\n\n地点：江苏盐城临海农场，苏垦智慧农业科技园\n\n1. 空中低空遥感AI巡田\n\n![Image 6](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002FE4E4DDE3ED1EEF061A2D631622A46F73_w1424h800.jpg)\n\n多光谱无人机集群常态化巡航，采集苗情、墒情、病虫害影像数据，AI自动识别长势差异、病斑，生成热力图；替代人工徒步巡田，十几分钟就能完成万亩农田普查。依托农业农村部低空技术创新重点实验室，开展低空多模态农情感知研究。\n\n2. AI智能光伏远程灌溉系统\n\n![Image 7](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F9FF2DAAEBED53D7BB8C825B388C102BD_w1424h800.jpg)\n\n万亩稻田布设太阳能智能闸门，通过土壤墒情传感器采集数据，AI分析土壤缺水程度，手机APP一键远程开关水渠闸门。\n\n量化效果：过去管500亩农田，人工开关闸门半天；现在2分钟完成全部闸门调控，灌溉效率提升20倍，每亩节约管水人工成本约30元。\n\n3. AR眼镜AI虫害识别\n\n![Image 8](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F9C7DFBF3F14BDE7E73309CA953C0C8E7_w1424h800.jpg)\n\n农技人员佩戴AR眼镜在田间巡查，拍摄虫体，AI毫秒级识别稻飞虱等害虫种类、统计虫口密度，识别准确率＞95%，自动推送防治方案，新手农技员也能快速判别田间虫害。\n\n4. AI变量施肥无人机作业\n\n![Image 9](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002FC257CCA1B1E8AE1621C403E96DF222EE_w1424h800.jpg)\n\nAI读取水稻营养、长势数据，为每一块条田生成独立追肥处方，无人机分区精准施肥，一地一策，实现肥药双减，农药化肥年均用量下降约3%。\n\n5. 北斗智能农机 AI收割决策\n\n![Image 10](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F00A2AE87C3E170BFBFE610FABE2556A5_w1424h800.jpg)\n\n北斗导航插秧机、无人收割机，AI根据成熟度、含水率数据，指导分块错峰收割，减少粮食收割损耗。\n\n[恭喜解锁12个月手机L2专属领取资格，立即领取>>](https:\u002F\u002Facttg.eastmoney.com\u002Fpub\u002Fwebtg_hskh_act_zixun_01_01_01_0)\n\n暗盘资金榜已更新!这些个股\u002F板块可以关注>\n\n追加内容\n\n本文作者可以追加内容哦 !\n\n**郑重声明：**用户在社区发表的所有信息将由本网站记录保存，仅代表作者个人观点，与本网站立场无关，不对您构成任何投资建议，据此操作风险自担。**请勿相信代客理财、免费荐股和炒股培训等宣传内容，远离非法证券活动。请勿添加发言用户的手机号码、公众号、微博、微信及QQ等信息，谨防上当受骗！**\n\n[![Image 11](https:\u002F\u002Favator.eastmoney.com\u002Fqface\u002F9825094237066000\u002F360)](https:\u002F\u002Fi.eastmoney.com\u002F9825094237066000)\n\n总收益 20日收益 日收益\n------\n\n历史收益率走势(%)\n\nChart\n\n代码 名称 最新价 涨跌幅\n[查看更多](http:\u002F\u002Figuba.eastmoney.com\u002F9825094237066000)\n\n浪客视频\n\n![Image 12](https:\u002F\u002Fnp-newspic.dfcfw.com\u002Fdownload\u002FD25261481966621695940_w340h340.jpg)\n\n![Image 13](https:\u002F\u002Fgbapi.eastmoney.com\u002Fshareopt\u002Fweb\u002Fweb_click.gif?id=20260918101757264727920&type=20&version=200&product=EastMoney&plat=Web&deviceid=caifuhao)\n\n郑重声明：东方财富网发布此信息的目的在于传播更多信息，与本站立场无关。东方财富网不保证该信息（包括但不限于文字、视频、音频、数据及图表）全部或者部分内容的准确性、真实性、完整性、有效性、及时性、原创性等。相关信息并未经过本网站证实，不对您构成任何投资建议，据此操作，风险自担。","东方财富财富号\u002F苏垦农发","报道",69,{"impact":112,"substance":161,"depth":206,"authority":322,"freshness":13,"relevant":48,"comment":323},5,"苏垦农发百万亩自有农田上实现天空地一体化AI闭环，含临海农场10万亩无人巡田等量化案例，产业参考价值较高，但来源为财富号自媒体、宣传色彩浓，权威性偏弱。",[325],{"name":318,"url":315},[69,327,328,329,22,330],"低空经济","农业人工智能","智能农机","数字农田",[332,333],"苏垦农发 神农慧种 AI大模型","临海农场 无人值守巡田","苏垦农发神农慧种AI大模型-2898","2026-09-19T00:06:07.612319Z"]