Cereal is an essential source of calories and protein for the global population. Accurately predicting cereal quality before harvest is highly desirable in order to optimise management for farmers, grading harvest and categorised storage for enterprises, future trading prices, and policy planning. The use of remote sensing data with extensive spatial coverage demonstrates some potential in predicting crop quality traits. Many studies have also proposed models and methods for predicting such traits based on multi-platform remote sensing data. In this paper, the key quality traits that are of interest to producers and consumers are introduced. The literature related to grain quality prediction was analyzed in detail, and a review was conducted on remote sensing platforms, commonly used methods, potential gaps, and future trends in crop quality prediction. This review recommends new research directions that go beyond the traditional methods and discusses grain quality retrieval and the associated challenges from the perspective of remote sensing data.

Interferometry Synthetic Aperture Radar (InSAR) provides unique capabilities to map regional/global topography and deformation of the Earth’s surface and has led to a broad spectrum of deformation monitoring applications. In order to adapt to various challenging monitoring environments, researchers have made tremendous innovations to deal with issues such as atmospheric and ionospheric effects, loss of coherence due to large displacements, geometric distortions and unwrapping errors. Owing to recent technical and methodological advances, the Earth’s surface deformation, ranging from earthquake ruptures, volcanic eruptions, landslides, glaciers, to groundwater storage variations, mining subsidence and infrastructure instability can now be mapped anywhere in the world at high spatial and temporal resolutions. This special issue received a set of contributions highlighting recent advances in methodologies and applications of InSAR to ground deformation monitoring. We aim to present overviews of both the state of the art of SAR/InSAR techniques and the next generation of applications across the broad range of deformation monitoring applications.

Due to high interferometric coherence in the Nevada region, Interferometric Synthetic Aperture Radar (InSAR) phase stacking is capable of mapping coseismic signals from the 27 January 1999, M_w 4.8 Frenchman Flat earthquake. This is one of the smallest earthquakes yet studied using InSAR with line-of-sight displacements as small as ~1.5 cm. Modelling the event as dislocation in an elastic half space suggests that the fault centroid was located at (115.96°W, 36.81°N) with a precision of 0.2~0.3 km (1σ) at a depth of 3.4 ± 0.2 km. Despite the dense local seismic network in southern Nevada, differences as large as 2~5 km were observed between our InSAR earthquake location and those estimated from seismic data. The InSAR-derived magnitude appeared to be greater than that from seismic data, which is consistent with other studies, and believed to be due to the relatively long time interval of InSAR data.

In 2017, China’s central government approved the national strategy to build Xiong’an New Area (XNA, 100 km southwest to Beijing), which was announced as a “millennium strategy” and a “demo area” for a sustainable, modern, and innovative urban model. Xiong’an will draw in as much as 380 billion investment and is expected to help accelerate the development of the wider Beijing-Tianjin-Hebei (Jingjinji) Area. In this paper, present subsidence in the XNA area is investigated using InSAR observations for the first time. The 24 SAR images acquired by European Space Agency’s Sentinel-1 satellites during the period from June 2017 to July 2018 suggest that in the north of Xiong County, the subsidence rate reaches up to 90 mm/y, which is highly correlated with the exploitation of geothermal drilling. As the construction in the XNA area will significantly accelerate and its high-quality development, the InSAR findings could provide valuable information for future sustainable urban planning and underground infrastructure construction.

The tremendous development of Synthetic Aperture Radar (SAR) missions in recent years facilitates the study of smaller amplitude ground deformation over greater spatial scales using longer time series. However, this poses greater challenges for correcting atmospheric effects due to the wider coverage of SAR imagery than ever. Previous attempts have used observations from Global Positioning System (GPS) and Numerical Weather Models (NWMs) to separate atmospheric delays, but they are limited by ①The availability (and distribution) of GPS stations; ②The low spatial resolution of NWM; And ③The difficulties in quantifying their performance. To overcome these limitations, we have developed the Generic Atmospheric Correction Online Service for InSAR (GACOS) which utilizes the high-resolution European Centre for Medium-Range Weather Forecasts (ECMWF) products using an Iterative Tropospheric Decomposition (ITD) model. This enables the reduction of the coupling effects of the troposphere turbulence and stratification and hence achieves equivalent performances over flat and mountainous terrains. GACOS comprises a range of notable features: ①Global coverage; ②All-weather, all-time usability; ③Available with a maximum of two-day latency; And ④Indicators available to assess the model’s performance and feasibility. In this paper, we demonstrate some successful applications of the GACOS online service to a variety of geophysical studies.