Remote Sensing Applications-Society and Environment最新文献_第9页

Comparing daily and 8-day MODIS land surface temperature data for urban heat island assessment using random forest modeling in data-limited regions 利用随机森林模型比较数据有限地区MODIS日地表温度和8天地表温度的城市热岛评估

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01 Epub Date: 2026-02-02 DOI: 10.1016/j.rsase.2026.101904

Vannesah K. Kporha , Dennis M. Fox , Mostafa Banitalebi , Yacine Bouroubi , Richard Fournier

Land Surface Temperature (LST) is a critical metric for understanding surface-atmosphere interactions and serves as a key variable in analyzing the Urban Heat Island (UHI) effect, a phenomenon where urban areas exhibit elevated temperatures compared to surrounding rural regions. UHI effects, driven by urbanization and surface thermal properties, significantly impact urban climates, particularly under a changing climate. This study evaluates the utility of MODIS daily and 8-day composite data in modeling summer LST and UHI effects across 137 cities in continental France over a 10-year period. Using Random Forest machine learning model and SHAP analysis, we assessed the role of structural, temporal, and meteorological variables in predicting LST and UHI dynamics for both daytime and nighttime. The results reveal that MODIS daily data achieves higher model accuracy (R² = 0.85 for daytime, R² = 0.85 for nighttime) compared to 8-day composites (R² = 0.75 for daytime, R² = 0.70 for nighttime) when meteorological inputs are included. However, in scenarios without weather data, the MODIS 8-day data outperformed daily data for daytime LST prediction (R² = 0.57 vs. R² = 0.48), emphasizing its potential for regions with limited meteorological coverage. Month emerged as a key predictor across all models, serving as a proxy for seasonal temperature variability. Diurnal analyses revealed stronger daytime UHI prediction accuracy whereas nighttime models showed lower accuracy, particularly for 8-day composites.

Overall, this study clarifies when coarser-temporal satellite products can reliably substitute daily observations and identifies the dominant drivers of urban heat, providing practical guidance for large-scale UHI monitoring and heat-resilient urban planning in regions with limited ground-based meteorological data.

地表温度（LST）是了解地表与大气相互作用的关键指标，也是分析城市热岛效应的关键变量，城市热岛效应是指城市地区的温度高于周边农村地区的现象。由城市化和地表热特性驱动的热岛效应显著影响城市气候，特别是在气候变化的情况下。本研究评估了MODIS每日和8天复合数据在模拟法国大陆137个城市10年期间夏季地表温度和热岛效应中的效用。利用随机森林机器学习模型和SHAP分析，我们评估了结构、时间和气象变量在预测白天和夜间LST和UHI动态中的作用。结果表明，考虑气象输入时，MODIS日数据的模型精度（白天R2 = 0.85，夜间R2 = 0.85）高于8天合成数据（白天R2 = 0.75，夜间R2 = 0.70）。然而，在没有天气数据的情景中，MODIS 8天数据在日间地表温度预测方面优于日数据（R2 = 0.57 vs. R2 = 0.48），这强调了其在气象覆盖有限地区的潜力。月份在所有模型中都是一个关键的预测指标，作为季节性温度变化的代表。日分析显示白天热岛指数的预测精度较高，而夜间模型的预测精度较低，尤其是8天合成模型。总体而言，本研究阐明了何时较粗时相卫星产品可以可靠地替代日常观测并识别城市热量的主要驱动因素，为地面气象数据有限地区的大规模热岛热岛监测和热弹性城市规划提供了实用指导。

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引用次数: 0

Multi-scenario modeling of soil organic carbon in semi-arid croplands with uncertainty quantification and model interpretation 半干旱农田土壤有机碳多情景不确定性量化与模型解释

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01 Epub Date: 2026-01-30 DOI: 10.1016/j.rsase.2026.101903

Azamat Suleymanov , Salavat Telyagissov , Ilgiz Asylbaev , Ramil Mirsayapov , Ruslan Suleymanov , Ali Keshavarzi , Iren Tuktarova , Larisa Belan

Climate change and the need for agricultural production are driving the increasing demand for accurate spatial information about soil organic carbon (SOC). While remote sensing (RS) data is effective for SOC modeling in croplands, the integration of additional covariates is often neglected. In the study, we evaluated the performance of a Sentinel-2 mosaic-based approach for digital mapping of SOC content in a semi-arid region and evaluated the inclusion of additional soil-forming variables. Several scenarios were examined where crop intensity, climate, and terrain covariates were incorporated into the bare soil mosaic using a recursive feature elimination (RFE) analysis and machine learning approach. The models were evaluated with repeated cross-validation and the prediction interval coverage probability (PICP) to assess the SOC predictions and associated uncertainties. The contribution of environmental covariates was assessed by permutation feature importance and Shapley values methods. The results showed that the model with only temporal mosaic of bare soil as explanatory variables resulted in RMSE = 0.75 %, R² = 0.15 and RPIQ = 1.11, whereas the scenario with all covariates significantly improved the model performance (RMSE = 0.56 %, R² = 0.52 and RPIQ = 1.55) and reduced the associated uncertainties. Among the spatial covariates, climate (precipitation, land surface temperature) and elevation emerged as key factors influencing the prediction of SOC content. Interpretation of such complex models through Shapley values revealed that the decrease in temperature, solar radiation and precipitation seasonality and the increase in elevation had a strong positive contribution to the SOC predictions. In steppe drier zones, these variables had a negative contribution to the predictions. While temporal mosaics of bare soil are useful predictors, we highlighted the importance of incorporating a more diverse range of environmental covariates for SOC modeling across cultivated lands. Our results suggest that integrating climate and elevation data with RS information is essential for achieving robust and accurate SOC mapping, especially across heterogeneous semi-arid landscapes.

气候变化和农业生产的需要推动了对土壤有机碳（SOC）精确空间信息的需求。虽然遥感数据对农田有机碳建模是有效的，但附加协变量的整合往往被忽略。在这项研究中，我们评估了基于Sentinel-2马赛克的半干旱区有机碳含量数字制图方法的性能，并评估了包含额外土壤形成变量的方法。使用递归特征消除（RFE）分析和机器学习方法，研究了几种将作物强度、气候和地形协变量纳入裸地马赛克的情况。通过重复交叉验证和预测区间覆盖概率（PICP）对模型进行评估，以评估土壤有机碳的预测和相关的不确定性。通过排列特征重要性和Shapley值法评估环境协变量的贡献。结果表明，仅以裸土时间嵌合为解释变量的模型RMSE = 0.75%, R2 = 0.15, RPIQ = 1.11，而所有协变量的模型RMSE = 0.56%, R2 = 0.52, RPIQ = 1.55，显著提高了模型性能，降低了相关不确定性。在空间协变量中，气候（降水、地表温度）和海拔是影响土壤有机碳含量预测的关键因子。利用Shapley值对这些复杂模式进行解释表明，温度、太阳辐射和降水季节性的降低以及海拔的升高对有机碳的预测有很强的正贡献。在草原干旱地区，这些变量对预测有负贡献。虽然裸露土壤的时间嵌合是有用的预测因子，但我们强调了将更多样化的环境协变量纳入耕地有机碳模型的重要性。我们的研究结果表明，将气候和海拔数据与RS信息相结合对于实现可靠和准确的有机碳制图至关重要，特别是在异质半干旱景观中。

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引用次数: 0

Remote sensing data facilitate large-scale monitoring of natural vegetation integrity in Brazilian biomes 遥感数据有助于对巴西生物群落的自然植被完整性进行大规模监测

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01 Epub Date: 2025-12-09 DOI: 10.1016/j.rsase.2025.101821

Juliana Silveira dos Santos , Larissa Rocha-Santos , Pavel Dodonov , Rosane Garcia Collevatti , Victor Hugo Soares Ney , Dhemerson Conciani , Beryl Eirene Lutz , Nathália Vieira Hissa Safar , Tainá Cirne , Rodrigo Cippolline Valim , Felipe Martello , Luiz Fernando Silva Magnago , Milton Cezar Ribeiro , Duccio Rocchini

引用次数: 0

Integrating post-rainfall multispectral satellite-derived features and multi-source datasets to enhance soil salinity mapping accuracy 整合降雨后多光谱卫星特征和多源数据集，提高土壤盐度制图精度

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01 Epub Date: 2026-01-26 DOI: 10.1016/j.rsase.2026.101896

Jamal-Eddine Ouzemou , Ahmed Laamrani , Ali EL Battay , Joann K. Whalen , Abdelghani Chehbouni

Soil salinity poses a critical threat to agricultural productivity in arid and semi-arid regions, particularly under changing climate. In Morocco's Sehb El Masjoune area, we hypothesize that post-rainfall terrain dynamics and soil spectral responses drive the spatial variability of surface salinity. This study integrates Sentinel-2, Landsat-9, and PlanetScope imagery with field-measured electrical conductivity and machine learning. It evaluates three hypotheses: (i) micro-topographic depressions retain moisture and promote localized salt accumulation; (ii) soil composition clusters influence differential salt retention; and (iii) combining multi-source data improves salinity mapping. Two novel post-rainfall proxies were developed from PlanetScope imagery. The first, the Depression Proxy (DP), identifies moisture-retaining concavities. The second, the Soil Clusters Proxy (SCP), groups soils based on post-rainfall spectral responses that are linked to texture and moisture properties. These were integrated with spectral indices and terrain variables into three modeling approaches using RF and GBR. Sentinel-2 combined with GBR and DP feature achieved the highest accuracy on an independent test set (R² = 0.85), identifying concave terrain as persistent salinity location and highlighting the role of surface topography (i.e., micro-depressions) in salinity distribution. Categorical accuracy confirmed that 56 % of samples were assigned to the exact soil salinity class and 80 % within ±1 class. Additionally, seasonal changes in predicted salinity were also examined using consistent spectral signatures between wet and dry imagery; however, due to the lack of wet-season ground truth, the resulting map represents qualitative spatial trends rather than validated salinity estimates. This process-informed Earth Observation-based framework improves the accuracy and interpretability of salinity mapping.

土壤盐分对干旱和半干旱地区的农业生产力构成严重威胁，特别是在气候变化的情况下。在摩洛哥的Sehb El Masjoune地区，我们假设降雨后地形动力学和土壤光谱响应驱动了地表盐度的空间变异。这项研究将Sentinel-2、Landsat-9和PlanetScope图像与现场测量的电导率和机器学习相结合。它评估了三个假设：(1)微地形洼地保持水分并促进局部盐积聚；㈡土壤组成簇影响不同的盐潴留；(3)多源数据的结合提高了盐度成图的质量。从PlanetScope图像中开发了两个新的降雨后代用物。第一种是凹陷代理（DP），它能识别出保湿凹陷。第二个是土壤集群代理（SCP），根据与质地和水分特性相关的降雨后光谱响应对土壤进行分组。将这些数据与光谱指数和地形变量集成到使用RF和GBR的三种建模方法中。Sentinel-2结合GBR和DP特征在独立测试集上获得了最高的精度（R2 = 0.85），将凹地形识别为持续的盐度位置，并突出地表地形（即微洼地）在盐度分布中的作用。分类精度证实56%的样品被分配到精确的土壤盐度类别，80%在±1类别内。此外，还使用干湿图像之间一致的光谱特征来检查预测盐度的季节变化；然而，由于缺乏雨季地面的真实情况，所得的地图代表定性的空间趋势，而不是经过验证的盐度估计。这种基于过程的地球观测框架提高了盐度制图的准确性和可解释性。

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引用次数: 0

Biochemical oxygen demand estimation using explainable ensemble learning methods 用可解释的集合学习方法估计生化需氧量

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01 Epub Date: 2025-12-12 DOI: 10.1016/j.rsase.2025.101835

Aamir Ali , Guanhua Zhou , Yumin Tan , Franz Pablo Antezana Lopez

Biochemical oxygen demand over five days (BOD₅) is a cornerstone indicator of organic pollution, yet its retrieval from remote sensing is hindered by its non-optically active nature. We present an explainable ensemble-learning framework that predicts BOD₅ in Hong Kong's marine waters by fusing multi-year (2019–2023) Sentinel-2 imagery with cyclic temporal features and four physicochemical and climatic proxies—chlorophyll-a (Chl-a), salinity, suspended solids (SS) and temperature. Initially, each proxy is estimated and subsequently utilized for BOD₅ prediction using CatBoost, LightGBM, XGBoost and Random Forest. XGBoost best captures Chl-a (r = 0.81) and temperature (r = 0.99), whereas CatBoost excels for salinity (r = 0.93), SS (r = 0.85) and ultimately BOD₅ (r = 0.88). SHapley Additive exPlanations reveal the dominant predictors and spatio-temporal mapping across four representative dates shows persistently elevated Chl-a, SS and BOD₅ and depressed salinity in eutrophic Deep Bay zone. This transparent, high-accuracy framework can guide Environmental Protection Department in prioritizing field sampling and streamlining pollution mitigation.

生化五天需氧量（BOD5）是有机污染的基础指标，但其非光学活性特性阻碍了其从遥感中获取。我们提出了一个可解释的集合学习框架，通过融合具有周期时间特征的多年（2019-2023）Sentinel-2图像以及四种物理化学和气候代理-叶绿素-a (Chl-a)，盐度，悬浮固体（SS）和温度，预测香港海水中的BOD5。最初，对每个代理进行估计，随后使用CatBoost、LightGBM、XGBoost和Random Forest进行BOD5预测。XGBoost对Chl-a （r = 0.81）和温度（r = 0.99）的捕获效果最好，而CatBoost对盐度（r = 0.93）、SS （r = 0.85）和BOD5 （r = 0.88）的捕获效果最好。SHapley加性解释揭示了四个代表性日期的主要预测因子和时空映射，显示富营养化后海湾地区Chl-a、SS和BOD5持续升高，盐度持续降低。这种透明、高精度的框架可以指导环境保护部门确定实地采样的优先顺序，并简化污染缓解工作。

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引用次数: 0

Neural network–enhanced calibration of ERA5 wind speeds in the northwestern Indian Ocean using 32 years of satellite observations 利用32年的卫星观测，神经网络增强了对西北印度洋ERA5风速的校准

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01 Epub Date: 2025-12-12 DOI: 10.1016/j.rsase.2025.101826

Mohammad Hossein Kazeminezhad

Accurate wind speed estimation is essential for applications in ocean engineering, renewable energy, and climate modeling. Although the ERA5 reanalysis dataset provides high-resolution global wind data, its accuracy varies across different regions and wind regimes. This study presents a comprehensive assessment and calibration of ERA5 wind speed data in the Northwestern Indian Ocean, using satellite observations from multiple scatterometers and altimeters over a 32-year period (1992–2023). The evaluation reveals systematic biases in ERA5, including a general underestimation of high wind speeds and localized discrepancies near coastlines, particularly in the Persian Gulf, Gulf of Oman, and equatorial regions. To enhance the accuracy of wind data, three calibration techniques including Linear Regression (LR), Quantile Mapping (QM), and Artificial Neural Networks (ANN), were applied and compared. The results demonstrate that while LR reduces the normalized mean bias, it offers only limited improvements in the Scatter Index (SI), decreasing it from 20.24 % to 19.07 %. QM improves the alignment of high wind percentiles and reduces the SI for the 99th percentile from 28.69 % to 26.47 %, but it does not significantly enhance the overall dataset. ANN, on the other hand, offers the most effective correction, reducing the overall SI to 17.93 % and the SI at the 99th percentile to 24.76 %. A dedicated assessment under tropical cyclone (TC) conditions further confirms the robustness of the ANN calibration, showing substantial improvements in the representation of extreme wind speed (with the SI reduced from 36.53 % to 28.77 % at the 99th percentile). Despite persistent residual biases at the highest wind speeds, the ANN approach significantly enhances agreement with satellite observations, outperforming raw ERA5 in all evaluated metrics. These findings highlight the potential of machine learning techniques to improve reanalysis datasets in regions where accurate wind representation is critical for climate resilience, offshore safety, and renewable energy planning.

准确的风速估算对于海洋工程、可再生能源和气候模型的应用至关重要。尽管ERA5再分析数据集提供了高分辨率的全球风数据，但其准确性因地区和风况而异。本研究利用32年（1992-2023）期间多个散射计和高度计的卫星观测资料，对西北印度洋的ERA5风速资料进行了综合评估和校准。评估揭示了ERA5的系统性偏差，包括对高风速的普遍低估和海岸线附近的局部差异，特别是在波斯湾、阿曼湾和赤道地区。采用线性回归（LR）、分位映射（QM）和人工神经网络（ANN）三种校正技术提高了风数据的精度。结果表明，虽然LR降低了归一化平均偏差，但它对散射指数（SI）的改善有限，从20.24%降至19.07%。QM改善了高风百分位的对齐，并将第99百分位的SI从28.69%降低到26.47%，但对整体数据集没有显著增强。另一方面，人工神经网络提供了最有效的修正，将总体SI降低到17.93%，将第99个百分位的SI降低到24.76%。在热带气旋（TC）条件下的专门评估进一步证实了人工神经网络校准的稳健性，显示出极端风速的表示有了实质性的改善（SI在第99百分位从36.53%降至28.77%）。尽管在最高风速下存在持续的残余偏差，但人工神经网络方法显著提高了与卫星观测的一致性，在所有评估指标上都优于原始ERA5。这些发现突出了机器学习技术在改善地区再分析数据集方面的潜力，在这些地区，准确的风力表示对气候适应能力、海上安全和可再生能源规划至关重要。

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引用次数: 0

Enhanced transfer learning for marine oil spill pollution monitoring 海洋溢油污染监测的强化迁移学习

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01 Epub Date: 2025-12-13 DOI: 10.1016/j.rsase.2025.101834

Anagha S. Dhavalikar

Oil spill detection is a crucial component of marine environmental protection and disaster management. Remote sensing technologies, using Synthetic Aperture Radar (SAR) imagery, offer a consistent and robust method for identifying and monitoring oil spills. In this study, transfer learning is employed to adapt three state-of-the-art deep convolutional neural networks (CNNs)—ResNet18, ResNet50, and EfficientNet-B0 which are pretrained on the ImageNet dataset, to the binary classification task of identifying oil spills and look-alikes in SAR images. With a balanced dataset having 278 images of oil spill and 262 of look-alike classes, across 10 epochs, ResNet18, ResNet50, and EfficientNet-B0 achieved high training accuracies in the range of 95–97 %. ResNet50 showed the best validation accuracy of 87.86 % and Test Accuracy 84.05 %. EfficientNet-B0, while lighter and faster, had slightly lower validation performance. ResNet18 offers a balance between speed and accuracy, whereas ResNet50 is optimal for accuracy if resources permit.

溢油探测是海洋环境保护和灾害管理的重要组成部分。使用合成孔径雷达（SAR）图像的遥感技术为识别和监测石油泄漏提供了一致且可靠的方法。在这项研究中，迁移学习被用于适应三个最先进的深度卷积神经网络(cnn) -ResNet18， ResNet50和EfficientNet-B0，这三个网络是在ImageNet数据集上预训练的，用于识别SAR图像中的漏油和相似物的二元分类任务。ResNet18、ResNet50和EfficientNet-B0在10个时代的平衡数据集中，拥有278张溢油图像和262张相似类图像，达到了95 - 97%的高训练准确率。ResNet50的验证准确度为87.86%，测试准确度为84.05%。效率网- b0虽然更轻更快，但验证性能略低。ResNet18提供了速度和准确性之间的平衡，而ResNet50在资源允许的情况下是准确性的最佳选择。

引用次数: 0

Reconstructing savannah wildfire events using a deep learning framework 使用深度学习框架重建大草原野火事件

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01 Epub Date: 2025-12-18 DOI: 10.1016/j.rsase.2025.101837

Simon Ramsey , Karin Reinke , Andrew Edwards , Simon Jones

This research presents a method to produce fire reconstructions with a high spatial and temporal resolution by downscaling geostationary satellite observations using a deep learning segmentation framework. This was achieved by training a U-Net on low earth orbit active fire detections paired with synchronous observations from the geostationary satellite Himawari-9/Advanced Himawari Imager (AHI) resampled to the resolution of the 500 m

0.64 μ m

RED channel. Geostationary satellites provide a means for continuous monitoring of fire behaviour but are constrained by the low spatial resolution of the infrared channels used to detect active fires. Localising fire activity using information from the higher spatial resolution solar reflective channels of geostationary satellites enables detailed fire progression mapping with a comparable spatial resolution to low earth orbit satellite systems. Six case study fires in the northern Australian savannahs are reconstructed with their lifecycles compared to the burn scar mapped by the Northern Australia and Rangelands Fire Information (NAFI), with F1-scores ranging from 0.80 to 0.96. Model predictions synchronous to VIIRS active fire detections during the selected case study fires were used to test performance during case study events. The results indicate a high positive detection rate (75%) for detections with a fire radiative power (FRP) above 12.4 MW during day-time and 3.1 MW at night-time which degraded with decreasing FRP as the fire signal becomes increasingly difficult to detect within the 2 km instantaneous field of view (IFOV) of the AHI infrared channels against the high day-time background temperatures within the northern Australian savannahs.

本研究提出了一种利用深度学习分割框架，通过对地静止卫星观测降尺度，产生高时空分辨率火灾重建的方法。这是通过训练U-Net在近地轨道上进行主动火灾探测，并与地球同步卫星Himawari-9/高级Himawari成像仪（AHI）的同步观测结果进行配对，重新采样到500 m 0.64μm RED通道的分辨率。地球同步卫星提供了一种持续监测火灾行为的手段，但受到用于探测活火的红外通道的低空间分辨率的限制。利用来自地球静止卫星的高空间分辨率太阳反射通道的信息来定位火灾活动，能够以与低地球轨道卫星系统相当的空间分辨率绘制详细的火灾进展图。本文重建了澳大利亚北部稀树大草原的六个火灾案例，并将其生命周期与北澳大利亚和牧场火灾信息（NAFI）绘制的烧伤疤痕进行了比较，f1得分从0.80到0.96不等。在选定的案例研究火灾期间，与VIIRS主动火灾探测同步的模型预测用于测试案例研究事件期间的性能。结果表明，在澳大利亚北部的热带草原上，在高白天背景温度下，AHI红外通道的2公里瞬时视场（IFOV）范围内，火灾信号越来越难以被探测到，因此，在白天高于12.4 MW和夜间高于3.1 MW的火灾辐射功率（FRP）探测的阳性检出率很高（75%），夜间随着FRP的降低而降低。

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引用次数: 0

Informing snow measurement site selection with remote sensing and local ecological knowledge: A case study in Oregon 利用遥感和当地生态知识为积雪测量地点选择提供信息：以俄勒冈州为例

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01 Epub Date: 2026-02-03 DOI: 10.1016/j.rsase.2026.101912

Hannah Steele, Kelsey Emard, Mark S. Raleigh

Seasonal snow provides critical water resources for communities throughout the western United States where melting snowpack drives much of the summer water supply. Water resource planning in western basins depends on reliable snowpack data; however, many basins lack a local snow measurement system, instead relying on data from measurements sites in neighboring basins with different landscape dynamics. Ensuring best site locations before installing snow measurement stations promotes optimal use of resources, improves data quality, and supports local water planning. Using the Chewaucan Basin in Oregon as a case study, this research examines how two novel and previously underutilized information sources - satellite remote sensing data and local knowledge - can help inform site selection for a snow measurement station. Using a 23-year record of daily snow cover mapping from the Moderate Resolution Imaging Spectroradiometer (MODIS), a 500m resolution spatial map of correlations between annual snow disappearance date and summer streamflow volume was derived. This correlation map served as an input to a GIS model alongside key landscape and infrastructure variables to determine potential site locations for an automated snow measurement system. The potential sites were shared with local water users to gather feedback on local conditions that may not be well represented in available geospatial data. The final site recommendation reflected both the modeled suitability analysis and the water users’ ecological knowledge. This study demonstrates a method for integrating remote sensing data and local ecological knowledge to inform the selection of snow measurement locations.

季节性的雪为整个美国西部的社区提供了重要的水资源，在那里，融化的积雪驱动了大部分夏季供水。西部流域水资源规划依赖于可靠的积雪数据；然而，许多流域缺乏当地的积雪测量系统，而是依赖于具有不同景观动态的邻近流域的测量站点的数据。在安装积雪测量站之前确保最佳的站点位置可以促进资源的最佳利用，提高数据质量，并支持当地的水资源规划。本研究以俄勒冈州的Chewaucan盆地为例，探讨了两个新的和以前未充分利用的信息源——卫星遥感数据和当地知识——如何帮助为积雪测量站的选址提供信息。利用中分辨率成像光谱仪（MODIS） 23年的日积雪测绘记录，导出了500米分辨率的年积雪消失日期与夏季河流流量之间的相关空间图。该相关图与关键景观和基础设施变量一起作为GIS模型的输入，以确定自动积雪测量系统的潜在站点位置。潜在的地点与当地的用水者共享，以收集对现有地理空间数据中可能没有很好反映的当地情况的反馈。最终的选址建议既反映了模型的适宜性分析，也反映了水用户的生态知识。本研究展示了一种整合遥感数据和当地生态知识的方法，为雪测量地点的选择提供信息。

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引用次数: 0

GNSS-CORS as water vapor sensors for local atmospheric monitoring: Comparing high-end geodetic-grade and low-cost stations in S Spain GNSS-CORS作为当地大气监测的水汽传感器：比较西班牙南部高端大地测量级站和低成本站

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01 Epub Date: 2026-01-08 DOI: 10.1016/j.rsase.2026.101880

M. Selmira Garrido-Carretero , M. Clara De Lacy-Pérez de los Cobos , Elena Giménez-De Ory , Leire Anne Retegui-Schiettekatte

In order to study a possible densification of the regional GNSS network in S Spain, a local cost-effective GNSS network has been installed in the province of Jaén: JAENet. This network is the first one installed in Spain to analyze the GNSS-derived Precipitable Water Vapor (GNSS-PWV) and its time variations. JAE1 is the first low-cost GNSS Continuously Operating Reference Station (GNSS-CORS) setup. It is strategically located very close to UJAE, a high-end geodetic-grade GNSS-CORS, in order to investigate the GNSS-PWV at the same geographic location and under the same environmental and atmospheric conditions. This study aims to evaluate the performance of low-cost GNSS devices for atmospheric water vapor monitoring through an experimental design and the first comparison of data coming from low-cost and high-precision devices over a period of more than one year. Eighteen months divided into six seasonal periods is considered. The common GNSS data period for both GNSS-CORS has been processed using the Precise Point Positioning (PPP) method to estimate their coordinates and evaluate the Zenith Tropospheric Delay (ZTD) using open-source GNSS software. The results show a good agreement between JAE1 and UJAE ZTD time series, with differences ranging from −11.59 mm to 10.12 mm, a mean difference value at the 2-mm level and a remarkably high correlation equal to 0.99. The difference between the mean of GNSS-PWV at GNSS-CORS throughout the six periods analyzed is always under the 1-mm level. The results show that low-cost GNSS-CORS are promising as water vapor sensors for local atmospheric monitoring.

为了研究西班牙南部区域GNSS网络可能的密集化，在JAENet省安装了一个具有成本效益的当地GNSS网络。该网络是西班牙安装的第一个用于分析gnss衍生的可降水量（GNSS-PWV）及其时间变化的网络。JAE1是第一个低成本的GNSS连续运行参考站（GNSS- cors）装置。为了在相同的地理位置和相同的环境和大气条件下调查GNSS-PWV，它的战略位置非常靠近高端测量级GNSS-CORS UJAE。本研究旨在通过实验设计和首次比较低成本和高精度设备一年多的数据，评估用于大气水蒸气监测的低成本GNSS设备的性能。18个月分为6个季节。采用精确点定位（PPP）方法对两个GNSS- cors的通用GNSS数据周期进行处理，估算其坐标，并使用开源GNSS软件评估天顶对流层延迟（ZTD）。结果表明，JAE1与UJAE ZTD时间序列具有较好的一致性，差异范围为- 11.59 mm ~ 10.12 mm，平均差值为2 mm，相关性为0.99。在分析的6个时段中，GNSS-CORS的GNSS-PWV平均值之间的差值始终在1毫米以下。结果表明，低成本的GNSS-CORS作为局部大气监测的水汽传感器具有广阔的应用前景。

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引用次数: 0