黑河流域AMSR2和SMOS土壤水分产品评价

Hui Lu, T. Koike
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引用次数: 2

摘要

土壤水分的空间分布特征和时间变化趋势对陆地不同尺度的水、能量和碳循环有显著影响。人们高度期望卫星遥感能提供这种有价值的信息。在使用遥感土壤水分产品之前,必须进行彻底的验证,以确保产品质量。本文对欧洲空间局土壤水分和海洋盐度(SMOS)任务和全球变化观测任务-水(GCOM-W)上搭载的高级微波扫描辐射计2 (AMSR2)在中国黑河流域的土壤水分产品进行了评价。黑河流域土地覆被由荒漠到草地、农田、山林依次变化,土壤水分场空间变异性明显,对检验两种土壤水分产品的可靠性和稳定性具有重要意义。我们计算了各产品月平均土壤湿度的日相对差(DRD),并与相应的全球土地数据同化系统(GLDAS)模拟计算结果进行了比较。对比结果表明,SMOS土壤水分产物比AMSR2土壤水分产物更不稳定。SMOS的DRD是AMSR2的20倍,是GLDAS的100倍。我们推测射频干扰对SMOS观测的影响可能是造成这种不稳定性能的原因之一。此外,SMOS算法中多角度观测的检索也是造成系统偏差的潜在来源。
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Evaluation of AMSR2 and SMOS soil moisture products over Heihe river basin in China
The spatial distribution characteristics and temporal variation trends of soil moisture significantly affect terrestrial water, energy, and carbon cycles at various scales. Satellite remote sensing is highly expected to provide such valuable information. Before applying the remotely sensed soil moisture products, a thorough validation must be conducted to insure product quality. In this paper, we evaluate the soil moisture products retrieved from the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission and the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission - Water (GCOM-W) over Heihe river basin in China, respectively. The land cover in Heihe river basin changes from desert to grass, agriculture field, and then mountain forest, which makes the basin an obvious spatial variation in soil moisture field and valuable to check the reliability and stability of two soil moisture products. We calculate the diurnal relative difference (DRD) of monthly averaged soil moisture between day and night observation of each products, and comparing them with that calculated from corresponding Global Land Data Assimilation System (GLDAS) simulations. The comparison results indicate that the SMOS soil moisture products are much unstable than AMSR2 retrievals. The DRD of SMOS is 20 times larger than that of AMSR2 and 100 times larger than that of GLDAS. We speculate that the radio frequency interference effects on SMOS observation may contribute to this unstable performance. Moreover, the retrievals from multi-angle observations in SMOS algorithm is also a potential source causing this systemic bias.
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