Soil Moisture Mapping from Satellites: An Intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over Two Dense Network Regions at Different Spatial Scales

Abstract

Abstract:A good knowledge of the quality of the satellite soil moisture products is of great importance for their application and improvement. This paper examines the performance of eight satellite-based soil moisture products, including the Soil Moisture Active Passive (SMAP) passive Level 3 (L3), the Soil Moisture and Ocean Salinity (SMOS) Centre Aval de Traitement des Données SMOS (CATDS) L3, the Japan Aerospace Exploration Agency (JAXA) Advanced Microwave Scanning Radiometer 2 (AMSR2) L3, the Land Parameter Retrieval Model (LPRM) AMSR2 L3, the European Space Agency (ESA) Climate Change Initiative (CCI) L3, the Chinese Fengyun-3B (FY3B) L2 soil moisture products at a coarse resolution of~0.25 • , and the newly released SMAP enhanced passive L3 and JAXA AMSR2 L3 soil moisture products at a medium resolution of~0.1 • . The ground soil moisture used for validation were collected from two well-calibrated and dense networks, including the Little Washita Watershed (LWW) network in the United States and the REMEDHUS network in Spain, each with different land cover. The results show that the SMAP passive soil moisture product outperformed the other products in the LWW network region, with an unbiased root mean square (ubRMSE) of 0.027 m 3 m −3 , whereas the FY3B soil moisture performed the best in the REMEDHUS network region, with an ubRMSE of 0.025 m 3 m −3 . The JAXA product performed much better at 0.25 • than at 0.1 • , but at both resolutions it underestimated soil moisture most of the time (bias < −0.05 m 3 m −3 ). The SMAP-enhanced passive soil moisture product captured the temporal variation of ground measurements well, with a correlation coefficient larger than 0.8, and was generally superior to the JAXA product. The LPRM showed much larger amplitude and temporal variation than the ground soil moisture, with a wet bias larger than 0.09 m 3 m −3 . The underestimation of surface temperature may have contributed to the general dry bias found in the SMAP (−0.018 m 3 m −3 for LWW and 0.016 m 3 m −3 for REMEDHUS) and SMOS (−0.004 m 3 m −3 for LWW and −0.012 m 3 m −3 for REMEDHUS) soil moisture products. The ESA CCI product showed satisfactory performance with acceptable error metrics (ubRMSE < 0.045 m 3 m −3 ), revealing the effectiveness of merging active and passive soil moisture products. The good performance of SMAP and FY3B demonstrates the

Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields

“…Launched in January 2015, SMAP provides soil moisture observations at an unprecedented 9-km and approximately 3-d resolution. Daily average soil moisture is observed at a spatial resolution analogous to county-level maize yields 38 and these outperform other remotely sensed soil moisture data products when compared with in situ soil moisture measurements 39,40 . SMAP observes soil moisture to a depth of approximately 5 cm, which we use as the surface boundary forcing of a simple hydrologic model 41 to estimate root-zone soil moisture.…”

A Review of Current and Potential Applications of Remote Sensing to Study the Water Status of Horticultural Crops

“…L-band microwave radiometry, a component of synthetic aperture radar systems, has been shown to be a reliable approach to estimate soil moisture via satellite-based remote sensing [223], such as using the ESA's Soil Moisture and Ocean Salinity (SMOS) [224] and NASA's Soil Moisture Active Passive (SMAP) satellites [225,226]. The limitation of the SMOS and SMAP missions, with regard to horticultural application, is their depth of retrieval (up to 5 cm) and spatial resolution (in the order of tens of kilometre) [227][228][229]. As an airborne application, the volumetric soil moisture has been estimated by analysing the SNR of the GNSS interference signal [230,231].…”

Impact of Soil Moisture Data Characteristics on the Sensitivity to Crop Yields Under Drought and Excess Moisture Conditions

“…The differences in the strength and timing of the statistical relationship for all three data sets suggest that the SMOS data set, which presumably has the highest accuracy since the sensor characteristics were designed specifically to measure soil moisture, is capturing this trend better than the other two data sets. SMOS soil moisture has been shown to have a higher accuracy than ESA-CCI in previous studies [5]. The weaker relationship with the ESA-CCI data set may be due to the statistical rescaling of that data set to the GLDAS model, which may be reducing the dynamic range of the data set, with a bias toward drier soil moisture values (Figure 4, Table 1).…”

“…These soil moisture data sets have been validated around the world using in situ station networks. Validation results have shown that the volumetric soil moisture derived from L-band frequencies is more accurate than those using higher radiometric frequencies such as X-and C-band, but the temporal trend in soil moisture (i.e., wetting and drying cycles) can be captured reasonably well using all three types of data [4,5]. Satellites are sensitive to the moisture in a thin layer of soil at the surface, limiting their use for monitoring root zone soil moisture most closely associated with agricultural droughts.…”