Soil moisture is a critical element in the hydrological cycle especially in a semiarid or arid region. Point measurement to comprehend the soil moisture distribution contiguously in a vast watershed is difficult because the soil moisture patterns might greatly vary temporally and spatially. Space‐borne radar imaging satellites have been popular because they have the capability to exhibit all weather observations. Yet the estimation methods of soil moisture based on the active or passive satellite imageries remain uncertain. This study aims at presenting a systematic soil moisture estimation method for the Choke Canyon Reservoir Watershed (CCRW), a semiarid watershed with an area of over 14,200 km2 in south Texas. With the aid of five corner reflectors, the RADARSAT‐1 Synthetic Aperture Radar (SAR) imageries of the study area acquired in April and September 2004 were processed by both radiometric and geometric calibrations at first. New soil moisture estimation models derived by genetic programming (GP) technique were then developed and applied to support the soil moisture distribution analysis. The GP‐based nonlinear function derived in the evolutionary process uniquely links a series of crucial topographic and geographic features. Included in this process are slope, aspect, vegetation cover, and soil permeability to compliment the well‐calibrated SAR data. Research indicates that the novel application of GP proved useful for generating a highly nonlinear structure in regression regime, which exhibits very strong correlations statistically between the model estimates and the ground truth measurements (volumetric water content) on the basis of the unseen data sets. In an effort to produce the soil moisture distributions over seasons, it eventually leads to characterizing local‐ to regional‐scale soil moisture variability and performing the possible estimation of water storages of the terrestrial hydrosphere.

During a ship voyage from Tasmania to Antarctica in summer 2000/01, radiative and meteorological measurements were continuously made, from which the surface energy budget was calculated. Sea conditions throughout the voyage ranged from open water to broken pack and finally to snow-covered unbroken sea ice in McMurdo Sound. The global radiation increased on average during the trip (to higher latitudes) as we travelled poleward. The net radiation, which was positive (toward the surface) on average, decreased however, mostly due to the increase in surface albedo. For open water, most of the net radiation is used for evapouration (61%), while for broken sea-ice conditions, nearly all energy is used for melting of the sea ice or heating of the ocean (96%). For unbroken snow-covered sea ice, the net radiation lies close to zero, due to the high surface albedo, which reached a mean value of 0.81. The sensible heat flux becomes the largest heat source and nearly all the energy is used for warming of the surface. Finally, a Radarsat image, on which the ship track was visible, was used to compare the ship observations with satellite derived ice types.

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Measuring soil moisture turns out to be crucial in watershed management. This study presents soil moisture prediction using RADRASAT-1 Synthetic Aperture Radar (SAR) satellite imagery collected in the Choke Canyon Reservoir Watershed (CCRW) in April 2004. Essential radiometric and geometric calibrations to correct the SAR imagery were performed with the aid of Corner Reflectors (CRs). The sensor data obtained after the installation of the corner reflectors in April 2004 showed better spatial accuracy, and consequently improves the correlation between the radar backscatter signals, σ 0 , and the Volumetric Moisture Content (VMC) of the soil in CCRW. Three prediction models were developed for soil moisture projection, which include simple linear regression, multiple linear regression, and genetic programming models. It found that the genetic programming model exhibits overall advantage of soil moisture estimation.

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