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Estimating surface soil moisture with the scanning low frequency microwave radiometer (SLFMR) during the Southern Great Plains 1997 (SGP97) hydrology experiment

机译:在1997年南部大平原(SGP97)水文学实验中,使用扫描低频微波辐射计(SLFMR)估算地表土壤水分

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The scanning low frequency microwave radiometer (SLFMR) was used to map surface soil moisture (0-5 cm depth) during the Southern Great Plains 1997 (SGP97) hydrology experiment. On June 29, July 2, and July 3, surface soil moisture maps with a pixel resolution of 200 m were obtained using a soil moisture retrieval algorithm, developed for L-band (1.4 GHz frequency, 21 cm wavelength) passive microwave data. In comparison with the 800 m resolution data from the electronically scanned thinned array radiometer (ESTAR), the higher resolution SLFMR data required a more site specific calibration. After calibration root mean square difference (RMSD) between model and observed surface soil moisture observations were on the order of 5%. Although the higher pixel resolution generally provided brightness temperatures of individual fields, it is also meant the greater spatial variability in land cover properties (primarily vegetation cover) were affecting the microwave observations and had to be accounted for in the soil moisture algorithm. Parameters in the soil moisture algorithm required local recalibration, particularly for the heavily vegetated fields, in order to account for vegetation effects on the microwave brightness temperatures. Thus having microwave data at resolutions that differentiate field boundaries with sharp contrasts in vegetation cover amounts will likely require greater variation in parameter values (and more uncertainty) be assigned to the soil moisture algorithm than at coarser resolutions. This result indicates that parameter values in the soil moisture algorithm amy be resolution dependent under certain land cover conditions, particularly at resolutions that discriminate field boundaries.
机译:在南部大平原1997年(SGP97)水文学实验期间,使用扫描低频微波辐射计(SLFMR)绘制了表层土壤湿度(0-5厘米深度)的地图。在6月29日,7月2日和7月3日,使用针对L波段(1.4 GHz频率,21 cm波长)无源微波数据开发的土壤水分检索算法,获得了像素分辨率为200 m的表面土壤水分图。与来自电子扫描减薄阵列辐射计(ESTAR)的800 m分辨率数据相比,更高分辨率的SLFMR数据需要更多针对特定地点的校准。校准后,模型和观察到的表层土壤湿度之间的均方根差(RMSD)约为5%。尽管较高的像素分辨率通常会提供各个场的亮度温度,但这也意味着土地覆盖属性(主要是植被覆盖)的较大空间变化会影响微波观测,因此必须在土壤湿度算法中加以考虑。土壤水分算法中的参数需要进行局部重新校准,尤其是对于植被茂密的田地,以便考虑植被对微波亮度温度的影响。因此,与分辨率较差的分辨率相比,微波数据的分辨率可以区分出田间边界并与植被覆盖量形成鲜明对比,这可能需要将更大的不确定性参数值分配给土壤湿度算法。该结果表明,土壤水分算法中的参数值在某些土地覆盖条件下,特别是在区分田间边界的分辨率下,其分辨率取决于分辨率。

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