埃塞俄比亚青尼罗河上游盆地卫星和再分析土壤水分产品综合评估

IF 5.7 Q1 ENVIRONMENTAL SCIENCES Science of Remote Sensing Pub Date : 2024-10-26 DOI:10.1016/j.srs.2024.100173
Addis A. Alaminie , Sofie Annys , Jan Nyssen , Mark R. Jury , Giriraj Amarnath , Muluneh A. Mekonnen , Seifu A. Tilahun
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引用次数: 0

摘要

土壤水分数据对于加强干旱监测、优化水资源管理、完善灌溉计划、预报洪水和了解气候变化影响至关重要。尽管存在长期的全球卫星和再分析产品,但对全球卫星产品在埃塞俄比亚的性能探索不足,这突出表明需要进行全面评估,以有效利用这些资源,应对关键的环境挑战。这项研究评估了 Gilgel Abay 流域的各种业务卫星和再分析土壤水分数据集。这些数据集包括欧洲航天局的气候变化倡议土壤湿度(ESA-CCI SM)、土壤湿度和海洋盐度(SMOS)、美国国家航空航天局的土壤湿度主动被动任务(SMAP Enhanced)、欧洲中期天气预报中心第五代再分析(ECMWF ERA5)、气候预报系统再分析(CFSRv2)、美国宇航局短期预报研究和转换中心--陆地信息系统(SPoRT-LIS)以及美国宇航局全球陆地数据同化系统(GLDAS)。经过偏差校正、Kolmogorov-Smirnov 双样本 t 检验、Bonferroni 校正和统计误差度量,评估结果显示,除 NASA-GLDAS 外,所有产品都能有效捕捉土壤水分动态。SMAP显示出更优越的时间动态性,其次是SMOS、ESA-CCI、CFSRv2、LIS和ERA5。利用斯皮尔曼等级相关系数(rs),SMAP(rs = 0.68)和 SMOS(rs = 0.67)被确定为最准确的土壤水分产品,其中 SMOS 在空间表示方面表现出色,并与地形湿度指数(TWI)密切相关。然而,由于缺乏足够的现场监测网络,限制了进行全面评估的能力。建立这些网络对于改进埃塞俄比亚青尼罗河上游流域的卫星检索和建模至关重要。
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A comprehensive evaluation of satellite-based and reanalysis soil moisture products over the upper Blue Nile Basin, Ethiopia
Soil moisture data is crucial for enhancing drought monitoring, optimizing water management, refining irrigation schedules, forecasting floods, and understanding climate change impacts. Despite the existence of long-term global satellite and reanalysis products, the performance of global satellite products in Ethiopia is underexplored, highlighting a need for comprehensive assessments to effectively utilize these resources and address critical environmental challenges. This research evaluates various operational satellites and reanalysis soil moisture datasets over the Gilgel Abay watershed. The datasets include the European Space Agency's Climate Change Initiative Soil Moisture (ESA-CCI SM), Soil Moisture and Ocean Salinity (SMOS), NASA's Soil Moisture Active Passive mission (SMAP Enhanced), the European Centre for Medium-Range Weather Forecasts Fifth Generation Reanalysis (ECMWF ERA5), Climate Forecast System reanalysis (CFSRv2), NASA's Short-term Prediction Research and Transition Center - Land Information System (SPoRT-LIS), and NASA's Global Land Data Assimilation System (GLDAS). After applying bias correction, the Kolmogorov-Smirnov two-sample t-tests, Bonferroni correction, and statistical error metrics, the evaluation reveals that all products, except NASA-GLDAS, effectively capture soil moisture dynamics. SMAP shows superior temporal dynamics, followed by SMOS, ESA-CCI, CFSRv2, LIS and ERA5. Using Spearman's rank correlation coefficient (rs), SMAP (rs = 0.68) and SMOS (rs = 0.67) identified as the most accurate soil moisture products, with SMOS excelling in spatial representation and closely aligning with the Topographic Wetness Index (TWI). However, the lack of sufficient in situ monitoring networks limits the ability to perform a thorough evaluation. Establishing these networks is essential for improving satellite retrievals and modelling in the upper Blue Nile Basin, Ethiopia.
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