An Intercomparison Study of Algorithms for SMAP Brightness Temperature Resolution Enhancement With or Without Information From AMSR2

Soil moisture is an essential variable for understanding water and heat exchanges between land and the atmosphere. Presently, <italic>L</italic>-band remote sensing technology has been widely employed for routine measurement of soil moisture from space. However, the spatial resolution of <italic>L</italic>-band soil moisture products obtained from microwave radiometers is too low (dozens of kilometres) to meet the needs of practical applications, such as hydrology modeling, weather forecasts, agricultural applications and water resource management. Therefore, this article proposes a new concept to downscale the soil moisture active passive (SMAP) <italic>L</italic>-band brightness temperature by using advanced microwave scanning radiometer-2 (AMSR2) <italic>X</italic>-band TB data, including the time-series regression (TSR) algorithm and two-dimensional discrete wavelet transform algorithm. An intercomparison study was conducted over a semiarid area located in the Shandian river basin with the other two algorithms of the Backus&#x2013;Gilbert (BG) optimal interpolation and natural neighbor interpolation without using the <italic>X</italic>-band TB data. The results revealed that the BG algorithm outperformed the NNI, 2D-DWT, and TSR algorithms compared with the original 36-km SMAP TB and airborne 1-km TB data. However, the soil moisture retrievals within one 9-km pixel with 8 soil moisture stations showed that the downscaled <italic>L</italic>-band TB with <italic>X</italic>-band data are reliable with lower unbiased root-mean-squared errors compared with resolution-enhanced TB without AMSR2 data.

Abstract licence: CC BY-NC-ND

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.