As the largest component of carbon export from terrestrial ecosystems, ecosystem respiration (RECO), together with vegetation productivity, determines the carbon stock changes in terrestrial ecosystems, so it is crucial to reveal the response of RECO to climate change. However, the simulation of RECO is usually inaccurate due to the neglect of the lagged response of respiration to changes in water conditions. In this study, we integrated meteorological data, remote sensing data, and soil data, and introduced the indicators of the previous water conditions into the deep learning model to simulate RECO. The conclusions are as follows: (a) There is a 1–2 years' lagged response of RECO to changes in water conditions. (b) It is necessary to consider the influence of previous water conditions when simulate RECO. It not only improved the simulation accuracy of RECO, but also reflected its inter‐annual fluctuations and change trends more accurately, avoiding the underestimation of RECO inter‐annual fluctuations. (c) There is an inconsistency in carbon input and carbon output change trends which impacts the carbon sink pattern and potential of terrestrial ecosystems. The growth trend of Gross Primary Production (GPP) in global terrestrial ecosystems is greater than that of RECO, with a trend of increasing carbon sinks, especially in the northern extra‐tropics; while the carbon sink capacity of tropical regions has gradually saturated, showing that the change trend of RECO is close to that of GPP, which poses a potential risk to the sustainable carbon sink capacity of global ecosystems in the future.

The application of SAR tomography (TomoSAR) on the urban infrastructure and other man-made buildings has gained increasing popularity with the development of modern high-resolution spaceborne satellites. Urban tomography focuses on the separation of the overlaid targets within one azimuth-range resolution cell, and on the reconstruction of their reflectivity profiles. In this work, we build on the existing methods of compressive sensing (CS) and generalized likelihood ratio test (GLRT), and develop a multiple scatterers detection method named CS-GLRT to automatically recognize the number of scatterers superimposed within a single pixel as well as to reconstruct the backscattered reflectivity profiles of the detected scatterers. The proposed CS-GLRT adopts a two-step strategy. In the first step, an L1-norm minimization is carried out to give a robust estimation of the candidate positions pixel by pixel with super-resolution. In the second step, a multiple hypothesis test is implemented in the GLRT to achieve model order selection, where the mapping matrix is constrained within the afore-selected columns, namely, within the candidate positions, and the parameters are estimated by least square (LS) method. Numerical experiments on simulated data were carried out, and the presented results show its capability of separating the closely located scatterers with a quasi-constant false alarm rate (QCFAR), as well as of obtaining an estimation accuracy approaching the Cramer–Rao Low Bound (CRLB). Experiments on real data of Spotlight TerraSAR-X show that CS-GLRT allows detecting single scatterers with high density, distinguishing a considerable number of double scatterers, and even detecting triple scatterers. The estimated results agree well with the ground truth and help interpret the true structure of the complex or buildings studied in the SAR images. It should be noted that this method is especially suitable for urban areas with very dense infrastructure and man-made buildings, and for datasets with tightly-controlled baseline distribution.

Conventional measurement methods with low spatial resolution, differential interferometric synthetic aperture radar (D-InSAR) is susceptible to signal decorrelation and atmospheric delay, so InSAR time series analysis is widely used for detection and monitoring of slow surface deformation. Persistent scatterer interferometric synthetic aperture radar (PS-InSAR) is based on a large number of SAR images, but small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) only needs a small number of images and performs better than PS-InSAR for obtaining nonlinear deformation information. In this paper, 21 COSMO-SkyMed radar images acquired between 2016 and 2017 are processed for land subsidence monitoring with the Small Baseline Subset (SBAS) approach in Yongshan County transmission corridors in China.

Gaofen-3 is a Chinese remote sensing satellite with multiple working modes, among which the scanning synthetic aperture radar (ScanSAR) mode is used for wide-swath imaging. synthetic aperture radar (SAR) interferometry in the ScanSAR mode provides the most rapid way to obtain a global digital elevation model (DEM), which can also be realized by Gaofen-3. Gaofen-3 ScanSAR interferometry works in the repeat-pass mode, and image pair non-synchronizations can influence its performance. Non-synchronizations can include differences of burst central times, satellite velocities, and burst durations. Therefore, it is necessary to analyze their influences and improve the interferometric coherence. Meanwhile, interferometric phase compensation and rapid DEM geolocation also need to be considered in interferometric processing. In this paper, interferometric coherence was analyzed in detail, followed by an iterative filtering method, which helped to improve the interferometric performance. Further, a phase compensation method for Gaofen-3 was proposed to compensate for the phase error caused by the unsynchronized azimuth time offset of image pair, and a closed-form solution of DEM geolocation with ground control point (GCP) information was derived. Application of our methods to a pair of Gaofen-3 interferometric images showed that these methods were able to process the images with good accuracy and efficiency. Notably, these analysis and processing methods can also be applied to other SAR satellites in the ScanSAR mode to obtain DEMs with high quality.