Reconstruction of GF-1 Soil Moisture Observation Based on Satellite and In Situ Sensor Collaboration Under Full Cloud Contamination

Clouds often limit the ability of optical satellite sensors (such as the newly launched Gaofen-1 (GF-1) satellite in China) to observe regional soil moisture at high spatial resolutions, especially under full-cloud-contamination condition. Thus, accurate reconstruction of regional soil moisture in this case became a great methodological challenge because of the complexity and ill-posed nature of the problem. In this paper, we present a Satellite and In situ sensor Collaborated Reconstruction (SICR) method. In this method, four reconstruction rules were proposed to rebuild four kinds of corresponding missing pixels, defined as follows: C1 pixel (including one in situ sensor in its area), C2 pixel (physically similar to C1), C3 pixel (with a regular soil moisture observation sequence), and C4 pixel (remaining). By analyzing soil moisture observation relationships between these four types of pixels with in situ measurements and within these pixels, four numerical reconstruction rules were established. Linear regression, similar pixel determination, least square method, and geostatistical interpolation algorithms were used in these four rules. At last, all blank soil moisture pixels in the target soil moisture image can be filled by the SICR method. The experiment conducted in the central south of U.S. integrated 11 in situ soil moisture sensors from the United States Department of Agriculture with 11 GF-1 satellite soil moisture images. It was demonstrated that GF-1 soil moisture observations on October 17, 2014, were successfully reconstructed by the SICR method, based on the evaluations of visual appearance comparison, error distribution analysis, subimage comparison, average relative error, and universal image quality index. SICR also performed better than the reconstruction results only based on in situ or satellite sensor data. Moreover, the comparison with the soil moisture observation from the microwave sensor demonstrated the value of SICR in regional high-resolution soil moisture reconstruction. It was suggested that the SICR method provided an effective reconstruction method under full cloud contamination and showed great potential for collaborating satellite and in situ sensors.