Improving Spatial Soil Moisture Representation through the Integration of SMAP and PROBA-V Products

Fan, Shudi; Hu, Yueming; Wang, Lu; Liu, Zhenhua; Shi, Zhou; Wu, Wenbin; Pan, Yuchun; Wang, Guangxing; Zhu, A‐Xing; Li, Bo

doi:10.3390/su10103459

Cited by 1 publication

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References 32 publications

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“…In order to address the need for spatially detailed SM information, a growing number of studies have attempted to provide high-spatial-resolution SM maps through downscaling by leveraging the complementary strength of passive microwave and radar and/or optical/thermal observations [22][23][24][25][26][27][28][29][30]. In this context, downscaling takes advantage of high-spatial-resolution land surface variables derived from radar and/or optical/thermal sensors to disaggregate the coarse resolution of passive microwave SM products.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Spatial Resolution of SMAP Soil Moisture Products through Spatial Downscaling over a Large Watershed: A Case Study for the Susquehanna River Basin in the Northeastern United States

Wakigari

Leconte

2022

Remote Sensing

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Soil moisture (SM) with a high spatial resolution plays a paramount role in many local and regional hydrological and agricultural applications. The advent of L-band passive microwave satellites allowed for it to be possible to measure near-surface SM at a global scale compared to in situ measurements. However, their use is often limited because of their coarse spatial resolution. Aiming to address this limitation, random forest (RF) models are adopted to downscale the SMAP level-3 (L3SMP, 36 km) and SMAP enhanced (L3SMP_E, 9 km) SM to 1 km. A suite of predictors derived from the Sentinel-1 C-band SAR and MODIS is used in the downscaling process. The RF models are separately trained and verified at both spatial scales (i.e., 36 and 9 km) considering two experiments: 1) using predictors derived from the MODIS and Sentinel-1 along with other predictors such as elevation and brightness temperature and 2) using all predictors of the first experiment except for the Sentinel-1 predictors. Only dates when the Sentinel-1 images were available are considered for the comparison of the two experiments. The comparison of the results of the two experiments indicates that the removal of Sentinel-1 predictors from the second experiment only reduces the R value from 0.84 to 0.83 and from 0.91 to 0.86 for 36 and 9 km spatial scales, respectively. Among the predictors used in the downscaling, the brightness temperature in VV polarization is identified as the most important predictor, followed by NDVI, surface albedo and API. On the contrary, the Sentinel-1 predictors play a less important role with no marked contribution in enhancing the predictive accuracy of RF models. In general, the two experiments have limitation, such as a small sample size for the training of the RF model because of the scarcity of Sentinel-1 images (i.e., revisit time of 12 days). Therefore, based on this limitation, a third experiment is proposed, in which the Sentinel-1 predictors are not considered at all in the training of the RF models. The results of the third experiment show a good agreement between the downscaled L3SMP and L3SMP_E SM, and in situ SM measurements at both spatial scales. In addition, the temporal availability of the downscaled SM increased. Moreover, the downscaled SM from both SMAP products presented greater spatial detail while preserving the spatial patterns found in their original products. The use of the two SMAP SM products as background fields for the downscaling process does not show marked differences. Overall, this study demonstrates encouraging results in the downscaling of SMAP SM products over humid climate with warm summers dominated by vegetation.

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