Ground subsidence caused by coal mining may lead to serious damage to ecological environment system and economic and social development. SAR image interferometry technology has been widely used for monitoring coal mining subsidence. However, at present monitoring fast and large subsidence using SAR images is still a huge challenge. In order to obtain fast and large gradient subsidence using SAR images, some scholars have resorted to other methods and technology, which do not just depend on the interferograms, and they have obtained better results. In summary, these methods and technology can be grouped into three main categories: (i) Combining InSAR with coal mining subsidence prediction models; (ii) Using SAR image amplitude information offset tracking technology; (iii) Combining InSAR with terrestrial 3D laser scanning technology. In this paper, the technology features, research progress, applications and limitations of these methods are analyzed; and some potential prospects of using SAR images to monitor fast and large subsidence in coal mining areas are discussed in the end.

Rice is a staple food for nearly half the world’s population. Therefore, rice yield estimation has far-reaching significance for food security. As the population continues to expand, rice is often in short supply. Timely and accurate data on rice production is crucial for governments and markets. Traditional rice yield estimation techniques cannot meet the current demand. With its ability to penetrate clouds and rain and the advantage of abundant remote sensing information, spaceborne synthetic aperture radar (S-SAR) can be used in the application of rice yield measurement. In this paper, the history and recent developments of rice yield measurement (1988-2018) based on spaceborne synthetic aperture radar are introduced, and the application prospect and technical development of rice yield monitoring are sought.

Timely and accurate monitoring of key agricultural information on crops is the premise and foundation for ensuring national food security and guiding agricultural production in a scientific and precise manner [1]. In recent years, domestic and foreign governments and research departments have included agricultural information monitoring within key research programs.

A microwave scattering model is a powerful tool for determining relationships between vegetation parameters and backscattering characteristics. The crown shape of the vegetation canopy is an important parameter in forestry and affects the microwave scattering modeling results. However, there are few numerical models or methods to describe the relationships between crown shapes and backscattering features. Using the Modified Tor Vergata Model (MTVM), a microwave scattering model based on the Matrix Doubling Algorithm (MDA), we quantitatively characterized the effects of crown shape on the microwave backscattering coefficients of the vegetation canopy. FEKO was also used as a computational electromagnetic method to make a complement and comparison with MTVM. In a preliminary experiment, the backscattering coefficients of two ideal vegetation canopies with four representative crown shapes (cylinder, cone, inverted cone and ellipsoid) were simulated: MTVM simulations were performed for the L (1.2 GHz), C (5.3 GHz) and X (9.6 GHz) bands in fully polarimetric mode, and FEKO simulations were carried out for the C (5.3 GHz) band at VV and VH polarization. The simulation results show that, for specific input parameters, the mean relative differences in backscattering coefficients due to variations in crown shape are as high as 127%, which demonstrates that the crown shape has a non-negligible influence on microwave backscattering coefficients of the vegetation canopy. In turn, this also suggests that investigation on effects of plant crown shape on microwave backscattering coefficients may have the potential to improve the accuracy of vegetation microwave scattering models, especially in canopies where volume scattering is the predominant mechanism.

This paper indicated a potential approach for direct rice production estimation with single high frequency SAR image. A modified cloud model had been built, in which the grain-layer was considered as the top layer and the stems and leaves as the second layer. Beneath these two layers, the water surface was considered as mirror reflection, of which value was extracted from nearby water surface. The simulated annealing algorithm and the leave-one-out method had also been applied. The result suggested that the scheme had good potential to estimate the rice grain number per unit, which was the key indice of yield, from a single TerraSAR image.