Abstract
To meet the demand of regional hydrological and agricultural applications, a new method named near infrared-red (NIR-red) spectra-based disaggregation (NRSD) was proposed to perform a disaggregation of Soil Moisture Active Passive (SMAP) products from 36 km to 250 m resolution. The NRSD combined proposed normalized soil moisture index (NSMI) with SMAP data to obtain 250 m resolution soil moisture mapping. This NRSD method was validated with the data from in situ OzNet network in May and September 2015. Results showed that NRSD performed a decent downscaling (root-mean-square error (RMSE) = 0.04 m3/m3 and 0.12 m3/m3 in May and September, respectively). Based on the validation, it was found that the proposed NSMI was a new alternative indicator for denoting the heterogeneity of soil moisture at sub-kilometer scales. Attributed to the excellent performance of the NSMI, NRSD has a higher overall accuracy, finer spatial representation within SMAP pixels and wider applicable scope on usability tests for land cover, vegetation density and drought condition than the disaggregation based on physical and theoretical scale change (DISPATCH) has at 250 m resolution. This revealed that the NRSD method is expected to provide soil moisture mapping at 250-resolution for large-scale hydrological and agricultural studies.
Highlights
As a key variable in hydrology, climatology, meteorology and ecology, surface soil moisture plays an important role in both global [1,2] and regional [3,4,5] applications, including numerical weather forecasting [6], climate change prediction [7], agricultural drought warning [8] and flood hazard monitoring [9]
250 m resolution normalized soil moisture index (NSMI) data were aggregated to a 36 × 36 km2 the mean value within the study area, and were compared with 36 km resolution Soil Moisture Active Passive (SMAP) soil moisture
The results indicate that the error of downscaling methods mainly comes from SMAP soil moisture itself
Summary
As a key variable in hydrology, climatology, meteorology and ecology, surface soil moisture plays an important role in both global [1,2] and regional [3,4,5] applications, including numerical weather forecasting [6], climate change prediction [7], agricultural drought warning [8] and flood hazard monitoring [9]. To meet the demand of these applications, soil moisture must be measured with the required accuracy over the desired range of spatial and temporal scales [10]. Soil moisture can be measured mainly by in situ sites, airborne sensors and satellite observation. Airborne measurements mainly consist of radiometer and scatterometer [12], both of which can provide regional soil moisture observations with the desired retrieval accuracy, as well as high spatial and temporal resolution, but are difficult to promote globally [13]. Satellite-based retrieval (e.g., AMSR-E, Aquarius, Soil Moisture and Ocean Salinity (SMOS), SMAP, etc.) has the potential capability to measure soil moisture with global coverage, a moderate repeat cycle and middle to high accuracy on diverse surface conditions, but coarse spatial resolution
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