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首页> 外文期刊>Vadose zone journal VZJ >From Point to Pixel Scale: An Upscaling Approach for In Situ Soil Moisture Measurements
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From Point to Pixel Scale: An Upscaling Approach for In Situ Soil Moisture Measurements

机译:从点到像素尺度:原位土壤水分测量的一种放大方法

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摘要

Given the high spatial variability of soil moisture content (SMC), direct comparison and integration of observations from different sources and measurement scales is becoming a major challenge. We have developed a spatial upscaling method for SMC that enables the direct combination of in situ measurements and remotely sensed data. The approach is based on the fact that spatial soil moisture patterns are related to ancillary features like topography, land cover, and soil type. This study used in situ data from a well-equipped research site in the northern Italian Alps. One of the main goals was to enable the use of these data for the validation of the NASA Soil Moisture Active Passive (SMAP) products. Dealing with medium-to coarse-resolution satellite imagery, especially in mountain areas, requires compensating for different measurement scales. The study approach was assessed based on Envisat advanced synthetic aperture radar (ASAR) data, which were resampled to reproduce the spatial scale of the SMAP data. Results show that the representativeness of in situ data, with respect to the 3-by 3-km SMAP pixel scale, can be improved significantly-direct correlation between SMC and satellite backscatter was improved from R = 0.05 to 0.28; furthermore, the error of the estimated SMC was improved from RMSE = 0.12 to 0.03 m(3) m(-3). This leads to more accurate reference data, which can help to improve the retrieval of SMC from remotely sensed imagery.
机译:鉴于土壤水分含量(SMC)的高度空间变异性,来自不同来源和测量规模的观测结果的直接比较和集成正成为一项重大挑战。我们为SMC开发了一种空间放大方法,该方法可以将原位测量和遥感数据直接结合起来。该方法基于以下事实:空间土壤湿度模式与辅助特征(如地形,土地覆盖和土壤类型)相关。这项研究使用了来自意大利北部阿尔卑斯山一个设备完善的研究站点的原位数据。主要目标之一是使能够使用这些数据来验证NASA土壤水分主动被动(SMAP)产品。处理中分辨率到粗分辨率的卫星图像,尤其是在山区,需要补偿不同的测量比例。该研究方法是基于Envisat高级合成孔径雷达(ASAR)数据进行评估的,该数据已重新采样以再现SMAP数据的空间尺度。结果表明,相对于3 x 3 km SMAP像素尺度,原位数据的代表性可以显着提高,SMC与卫星反向散射之间的直接相关性从R = 0.05提高到0.28;此外,估计SMC的误差从RMSE = 0.12提高到0.03 m(3)m(-3)。这将导致更准确的参考数据,从而有助于改善从遥感影像中检索SMC的过程。

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