For mapping Earth surface movements at larger scale and smaller amplitudes, many new synthetic aperture radar instruments (Sentinel‐1A/B, Gaofen‐3, ALOS‐2) have been developed and launched from 2014–2017, and this trend is set to continue with Sentinel‐1C/D, Gaofen‐3B/C, RADARSAT Constellation planned for launch during 2018–2025. This poses more challenges for correcting interferograms for atmospheric effects since the spatial‐temporal variations of tropospheric delay may dominate over large scales and completely mask the actual displacements due to tectonic or volcanic deformation. To overcome this, we have developed a generic interferometric synthetic aperture radar atmospheric correction model whose notable features comprise (i) global coverage, (ii) all‐weather, all‐time useability, (iii) correction maps available in near real time, and (iv) indicators to assess the correction performance and feasibility. The model integrates operational high‐resolution European Centre for Medium‐Range Weather Forecasts (ECMWF) data (0.125° grid, 137 vertical levels, and 6‐hr interval) and continuous GPS tropospheric delay estimates (every 5 min) using an iterative tropospheric decomposition model. The model's performance was tested using eight globally distributed Sentinel‐1 interferograms, encompassing both flat and mountainous topographies, midlatitude and near polar regions, and monsoon and oceanic climate systems, achieving a phase standard deviation and displacement root‐mean‐square (RMS) of ~1 cm against GPS over wide regions (250 by 250 km). Indicators describing the model's performance including (i) GPS network and ECMWF cross RMS, (ii) phase versus estimated atmospheric delay correlations, (iii) ECMWF time differences, and (iv) topography variations were developed to provide quality control for subsequent automatic processing and provide insights of the confidence level with which the generated atmospheric correction maps may be applied.

Phenotyping plays an important role in crop science research; the accurate and rapid acquisition of phenotypic information of plants or cells in different environments is helpful for exploring the inheritance and expression patterns of the genome to determine the association of genomic and phenotypic information to increase the crop yield. Traditional methods for acquiring crop traits, such as plant height, leaf color, leaf area index (LAI), chlorophyll content, biomass and yield, rely on manual sampling, which is time-consuming and laborious. Unmanned aerial vehicle remote sensing platforms (UAV-RSPs) equipped with different sensors have recently become an important approach for fast and non-destructive high throughput phenotyping and have the advantage of flexible and convenient operation, on-demand access to data and high spatial resolution. UAV-RSPs are a powerful tool for studying phenomics and genomics. As the methods and applications for field phenotyping using UAVs to users who willing to derive phenotypic parameters from large fields and tests with the minimum effort on field work and getting highly reliable results are necessary, the current status and perspectives on the topic of UAV-RSPs for field-based phenotyping were reviewed based on the literature survey of crop phenotyping using UAV-RSPs in the Web of ScienceTM Core Collection database and cases study by NERCITA. The reference for the selection of UAV platforms and remote sensing sensors, the commonly adopted methods and typical applications for analyzing phenotypic traits by UAV-RSPs, and the challenge for crop phenotyping by UAV-RSPs were considered. The review can provide theoretical and technical support to promote the applications of UAV-RSPs for crop phenotyping.

Spatial and temporal variations of pressure, temperature, and water vapor content in the atmosphere introduce significant confounding delays in interferometric synthetic aperture radar (InSAR) observations of ground deformation and bias estimates of regional strain rates. Producing robust estimates of tropospheric delays remains one of the key challenges in increasing the accuracy of ground deformation measurements using InSAR. Recent studies revealed the efficiency of global atmospheric reanalysis to mitigate the impact of tropospheric delays, motivating further exploration of their potential. Here we explore the effectiveness of these models in several geographic and tectonic settings on both single interferograms and time series analysis products. Both hydrostatic and wet contributions to the phase delay are important to account for. We validate these path delay corrections by comparing with estimates of vertically integrated atmospheric water vapor content derived from the passive multispectral imager Medium‐Resolution Imaging Spectrometer, onboard the Envisat satellite. Generally, the performance of the prediction depends on the vigor of atmospheric turbulence. We discuss (1) how separating atmospheric and orbital contributions allows one to better measure long‐wavelength deformation and (2) how atmospheric delays affect measurements of surface deformation following earthquakes, and (3) how such a method allows us to reduce biases in multiyear strain rate estimates by reducing the influence of unevenly sampled seasonal oscillations of the tropospheric delay.

Pointwise GPS measurements of tropospheric zenith total delay can be interpolated to provide high‐resolution water vapor maps which may be used for correcting synthetic aperture radar images, for numeral weather prediction, and for correcting Network Real‐time Kinematic GPS observations. Several previous studies have addressed the importance of the elevation dependency of water vapor, but it is often a challenge to separate elevation‐dependent tropospheric delays from turbulent components. In this paper, we present an iterative tropospheric decomposition interpolation model that decouples the elevation and turbulent tropospheric delay components. For a 150 km × 150 km California study region, we estimate real‐time mode zenith total delays at 41 GPS stations over 1 year by using the precise point positioning technique and demonstrate that the decoupled interpolation model generates improved high‐resolution tropospheric delay maps compared with previous tropospheric turbulence‐ and elevation‐dependent models. Cross validation of the GPS zenith total delays yields an RMS error of 4.6 mm with the decoupled interpolation model, compared with 8.4 mm with the previous model. On converting the GPS zenith wet delays to precipitable water vapor and interpolating to 1 km grid cells across the region, validations with the Moderate Resolution Imaging Spectroradiometer near‐IR water vapor product show 1.7 mm RMS differences by using the decoupled model, compared with 2.0 mm for the previous interpolation model. Such results are obtained without differencing the tropospheric delays or water vapor estimates in time or space, while the errors are similar over flat and mountainous terrains, as well as for both inland and coastal areas.