Abstract
Improving the spatial resolution of microwave satellite soil moisture (SM) products is important for various applications. Most of the downscaling methods that fuse optical/thermal and microwave data rely on remotely sensed land surface temperature (LST) or LST-derived SM indexes (SMIs). However, these methods suffer from the problems of “cloud contamination”, “decomposing uncertainty”, and “decoupling effect”. This study presents a new downscaling method, referred to as DSCALE_mod16, without using LST and LST-derived SMIs. This model combines MODIS ET products and a gridded meteorological data set to obtain Land surface Evaporative Efficiency (LEE) as the main downscaling factor. A cosine-square form of downscaling function was adopted to represent the quantitative relationship between LEE and SM. Taking the central part of the United States as the case study area, we downscaled SMAP (Soil Moisture Active and Passive) SM products with an original resolution of 36km to a resolution of 500m. The study period spans more than three years from 2015 to 2018. In situ SM measurements from three sparse networks and three core validation sites (CVS) were used to evaluate the downscaling model. The evaluation results indicate that the downscaled SM values maintain the spatial dynamic range of original SM data while providing more spatial details. Moreover, the moisture mass is conserved during the downscaling process. The downscaled SM values have a good agreement with in situ SM measurements. The unbiased root-mean-square errors (ubRMSEs) of downscaled SM values is 0.035 m3/m3 at Fort Cobb, 0.026 m3/m3 at Little Washita, and 0.055 m3/m3 at South Fork, which are comparable to ubRMSEs of original SM estimates at these three CVS.
Highlights
Soil moisture (SM) is usually defined as the water contained in the unsaturated soil zone
The third column illustrates the downscaled SM. It can be observed from the downscaled maps that SM is greater near water or river areas and it decreases with the distance from water areas
The cosine-square downscaling function was the best among three candidate functions to express the quantitative relationship between Land surface Evaporative Efficiency (LEE) and SM
Summary
Soil moisture (SM) is usually defined as the water contained in the unsaturated soil zone. The retrieval methods based on visible, near infrared, and thermal infrared bands have a longer history and possess the advantages of high spatiotemporal resolution and abundant data sources They suffer from the common drawbacks of optical remote sensing, such as, unavailability under cloudy weather conditions and shallow penetration in soil [10,12,13,14,15]. The SAR sensor experienced a fatal anomaly and no longer transmitted data since 7 July 2015 [19] In this context, spatial downscaling of microwave soil moisture to higher resolutions, such as one kilometer or even hundreds of meters, is becoming more challenging. There are greater uncertainties in decomposing remotely sensed LST into soil and vegetation temperature components to calculate the LST-derived SMIs. It is very difficult to accurately determine the dry and wet boundaries of the LST/FVC space [32]. ELEE: LEa: nLdansudrfsaucrefaecveapevoaraptoivraeteivffeiceieffinccyie,nScMy,: SsoMil:msooiilstmuoreis. tTuhree. sTuhbescsruibpstcsrHipRtsaHndR CanRdrCepRrerseepnret sheingthh-riegsho-lruetsioolnutainond aconadrsceo-arresseo-lruestioolnu,tiroensp, reecstpiveecltyiv. ely
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