摘要：Core Ideas Upscaling methods compared in situ measures with soil moisture from the SMAP satellite. The accuracy of SMAP soil moisture products in annual cropland was assessed. The spatial representativeness of sparse in situ networks was determined. In 2015, NASA launched the Soil Moisture Active Passive (SMAP) satellite. Data from this satellite are being exploited to improve forecasting of extreme weather events and delivery of disaster response. International core validation sites (CVSs) have been contributing in situ soil moisture data to validate and calibrate SMAP soil moisture products. Overall the soil moisture retrieval errors have exceeded SMAP's mission requirement (errors below 0.04 m 3 m −3 ), with the exception of some sites of annual cropland as present at the Carman (Canada) CVS. In 2016, a SMAP validation experiment was conducted at the Canadian site in Manitoba (SMAPVEX16‐MB) in an attempt to understand the differences between the SMAP soil moisture retrievals and the permanent in situ network observations. The research presented here analyzed the performance of this network in representing soil moisture within a SMAP pixel and tested five upscaling approaches. Comparisons between the permanent network and SMAPVEX16‐MB measurements (from temporary stations and field measures) confirmed agreement among these three sources of soil moisture measures. The SMAP soil moisture values were compared with in situ soil moisture upscaled from the four tested approaches as well as soil moisture estimated by the NOAH Land Surface Model (LSM). There were similar discrepancies when analyzing all methods (RMSE 0.072–0.074 m 3 m −3 for the four upscaling methods; 0.076 m 3 m −3 for the LSM approach), yielding no reduction in the soil moisture RMSE for this site. The SMAP team will continue to investigate other factors that may be contributing to errors above 0.04 m 3 m −3 at these annually cropped CVSs.
关键词：AAFC; Agriculture and Agri-Food Canada; CVS; core validation site; L2 SM P E; Enhanced Level 2 Soil Moisture Passive; LSM; Land Surface Model; IOP; intensive observing period; NDVI; normalized difference vegetation index; PALS; Passive and Active L-band Sensor; RISMA; Real-time In Situ Soil Monitoring for Agriculture; SMAP; Soil Moisture Active Passive.
