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首页> 外文OA文献 >Disaggregation of SMOS soil moisture to 100m resolution using MODIS optical/thermal and sentinel-1 radar data: evaluation over a bare soil site in morocco
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Disaggregation of SMOS soil moisture to 100m resolution using MODIS optical/thermal and sentinel-1 radar data: evaluation over a bare soil site in morocco

机译：使用mODIs光学/热学和哨兵-1雷达数据将smOs水分分解为100米分辨率：评估摩洛哥裸土场地

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The 40 km resolution SMOS (Soil Moisture and Ocean Salinity) soil moisture, previously disaggregated at a 1 km resolution using the DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) method based on MODIS optical/thermal data, is further disaggregated to 100 m resolution using Sentinel-1 backscattering coefficient (σ°). For this purpose, three distinct radar-based disaggregation methods are tested by linking the spatio-temporal variability of σ° and soil moisture data at the 1 km and 100 m resolution. The three methods are: (1) the weight method, which estimates soil moisture at 100 m resolution at a certain time as a function of σ° ratio (100 m to 1 km resolution) and the 1 km DISPATCH products of the same time; (2) the regression method which estimates soil moisture as a function of σ° where the regression parameters (e.g., intercept and slope) vary in space and time; and (3) the Cumulative Distribution Function (CDF) method, which estimates 100 m resolution soil moisture from the cumulative probability of 100 m resolution backscatter and the maximum to minimum 1 km resolution (DISPATCH) soil moisture difference. In each case, disaggregation results are evaluated against in situ measurements collected between 1 January 2016 and 11 October 2016 over a bare soil site in central Morocco. The determination coefficient (R2) between 1 km resolution DISPATCH and localized in situ soil moisture is 0.31. The regression and CDF methods have marginal effect on improving the DISPATCH accuracy at the station scale with a R2 between remotely sensed and in situ soil moisture of 0.29 and 0.34, respectively. By contrast, the weight method significantly improves the correlation between remotely sensed and in situ soil moisture with a R2 of 0.52. Likewise, the soil moisture estimates show low root mean square difference with in situ measurements (RMSD = 0.032 m3 m−3).
机译：以前使用基于MODIS光学/热数据的DISPATCH（基于物理和理论尺度变化的分解）方法以1 km的分辨率分解了分辨率为40 km的SMOS（土壤水分和海洋盐分）土壤水分，然后进一步分解为100 m使用Sentinel-1后向散射系数（σ°）的分辨率。为此，通过在1 km和100 m分辨率下将σ°的时空变化与土壤湿度数据联系起来，测试了三种基于雷达的分解方法。这三种方法是：（1）权重法，它根据σ°比（100 m到1 km分辨率）和同时1 km DISPATCH乘积的函数，估算某一时间在100 m分辨率下的土壤水分； （2）回归法，根据回归参数（例如截距和斜率）在空间和时间上变化，将土壤水分估算为σ°的函数； （3）累积分布函数（CDF）方法，该方法从100 m分辨率反向散射的累积概率和最大至最小1 km分辨率（DISPATCH）土壤湿度差异估算100 m分辨率土壤湿度。在每种情况下，均根据2016年1月1日至2016年10月11日在摩洛哥中部裸露土地上收集的现场测量结果评估分类结果。 1 km分辨率DISPATCH与本地土壤水分之间的确定系数（R2）为0.31。回归和CDF方法在提高站点规模的DISPATCH精度方面具有边际效应，R2在遥感和原地土壤湿度之间分别为0.29和0.34。相比之下，权重法显着改善了遥感和原地土壤水分之间的相关性，R2为0.52。同样，土壤湿度估计值在实地测量中显示出较低的均方根差（RMSD = 0.032 m3 m-3）。

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