Rice growth monitoring is very important as rice is one of the staple crops of the world. Rice variables as quantitative indicators of rice growth are critical for farming management and yield estimation, and synthetic aperture radar (SAR) has great advantages for monitoring rice variables due to its all-weather observation capability. In this study, eight temporal RADARSAT-2 full-polarimetric SAR images were acquired during rice growth cycle and a modified water cloud model (MWCM) was proposed, in which the heterogeneity of the rice canopy in the horizontal direction and its phenological changes were considered when the double-bounce scattering between the rice canopy and the underlying surface was firstly considered as well. Then, three scattering components from an improved polarimetric decomposition were coupled with the MWCM, instead of the backscattering coefficients. Using a genetic algorithm, eight rice variables were estimated, such as the leaf area index (LAI), rice height (h), and the fresh and dry biomass of ears (F_e and D_e). The accuracy validation showed the MWCM was suitable for the estimation of rice variables during the whole growth season. The validation results showed that the MWCM could predict the temporal behaviors of the rice variables well during the growth cycle (R² > 0.8). Compared with the original water cloud model (WCM), the relative errors of rice variables with the MWCM were much smaller, especially in the vegetation phase (approximately 15% smaller). Finally, it was discussed that the MWCM could be used, theoretically, for extensive applications since the empirical coefficients in the MWCM were determined in general cases, but more applications of the MWCM are necessary in future work.

Timely and accurate estimation of rice parameters plays a significant role in rice monitoring and yield forecasting for ensuring food security. Compact-polarimetric (CP) synthetic aperture radar (SAR), a good compromise between the dual- and quad-polarized SARs, is an important part of the new generation of Earth observation systems. In this paper, the ability of CP SAR data to retrieve rice biophysical parameters was explored using a modified water cloud model. The results showed that S₁ was superior to other CP variables in rice height inversion with a coefficient of determination (R²) of 0.92 and a root-mean-square error (RMSE) of 5.81 cm. RL was the most suitable for inverting the volumetric water content of the rice canopy, with an R² of 0.95 and a RMSE of 0.31 kg/m³. The m-χ decomposition produced the highest accuracies for the ear biomass: R² was 0.89 and RMSE was 0.17 kg/m². The highest accuracy of leaf area index (LAI) retrieval was obtained for RH (right circular transmit and horizontal linear receive) with an R² of 0.79 and a RMSE of 0.33. This study illustrated the capability of CP SAR data with respect to retrieval of rice biophysical parameters, especially for height, volumetric water content of the rice canopy, and ear biomass, and this mode may offer the best option for rice-monitoring applications because of swath coverage.

Salt crust is a normal landform in drying-out salt lake basins or marine regression coastlines, but the surface evolution processes over a decadal or even centenary period are not well understood due to poor data records. Microrelief characteristics control erodibility and erosivity, which will significantly influence wind erosion and dust emission. It is essential to classify the microrelief pattern of salt crust for mapping its spatial distribution and evaluating the environmental process. A desiccated inland tail-end lake would be an example of the coastline surface evolution after regression and represent a good case study of salt crust because of the fewer exogenic process interruptions. For this paper, field work was performed in the Lop Nur playa in China, about 90° E, 40° N, which used to be a salt lake half a century ago. Ground-based photos of the salt crust were acquired and imported into structure-from-motion (SfM) software to produce a fine centimeter-scale digital elevation model (DEM). Two indexes were introduced and extracted from the digital elevation model to classify various types of salt crust: roughness was calculated to evaluate the magnitude and the gray-level co-occurrence matrix (GLCM) score was derived to describe the structure pattern of the salt crust. Moreover, in this paper, sedimentary features during different parts of a playa evaporation cycle are reviewed and peculiar kinds of salt crust found on Lop Nur are further discussed.