Wuhan city is the biggest city in central China and has suffered subsidence problems in recent years because of its rapid urban construction. However, longtime and wide range monitoring of land subsidence is lacking. The causes of subsidence also require further study, such as natural conditions and human activities. We use small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) method and high-resolution RADARSAT-2 images acquired between 2015 and 2018 to derive subsidence. The SBAS-InSAR results are validated by 56 leveling benchmarks where two readings of elevation were recorded. Two natural factors (carbonate rock and soft soils) and three human factors (groundwater exploitation, subway excavation and urban construction) are investigated for their relationships with land subsidence. Results show that four major areas of subsidence are detected and the subsidence rate varies from −51.56 to 27.80 millimeters per year (mm/yr) with an average of −0.03 mm/yr. More than 83.81% of persistent scattered (PS) points obtain a standard deviation of less than −6 mm/yr, and the difference between SBAS-InSAR method and leveling data is less than 5 mm/yr. Thus, we conclude that SBAS-InSAR method with Radarsat-2 data is reliable for longtime monitoring of land subsidence covering a large area in Wuhan city. In addition, land subsidence is caused by a combination of natural conditions and human activities. Natural conditions provide a basis for subsidence and make subsidence possible. Human activities are driving factors and make subsidence happen. Moreover, subsidence information could be used in disaster prevention, urban planning, and hydrological modeling.

In constructing models for predicting soil organic matter (SOM) by using visible and near-infrared (vis–NIR) spectroscopy, the selection of representative calibration samples is decisive. Few researchers have studied the inclusion of spectral pretreatments in the sample selection strategy. We collected 108 soil samples and applied six commonly used spectral pretreatments to preprocess soil spectra, namely, Savitzky–Golay (SG) smoothing, first derivative (FD), logarithmic function log(1/R), mean centering (MC), standard normal variate (SNV), and multiplicative scatter correction (MSC). Then, the Kennard–Stone (KS) strategy was used to select calibration samples based on the pretreated spectra, and the size of the calibration set varied from 10 samples to 86 samples (80% of the total samples). These calibration sets were employed to construct partial least squares regression models (PLSR) to predict SOM, and the built models were validated by a set of 21 samples (20% of the total samples). The results showed that 64−78% of the calibration sets selected by the inclusion of pretreatment demonstrated significantly better performance of SOM estimation. The average improved residual predictive deviations (ΔRPD) were 0.06, 0.13, 0.19, and 0.13 for FD, log(1/R), MSC, and SNV, respectively. Thus, we concluded that spectral pretreatment improves the sample selection strategy, and the degree of its influence varies with the size of the calibration set and the type of pretreatment.

With the rapid development of China’s economy and the acceleration of urbanization, the rapid expansion of urban space has led to a growing demand for land that has resulted in the destruction and expropriation of a large amount of ecological land in China. In the face of the current situation of China’s resource constraints, environmental degradation, and ecosystem destruction, it is necessary to thoroughly study the territorial ecological space of China and its evolution rules. Based on previous studies on the classification of ecological land use, this study proposes the concept of territorial ecological space and distinguishes it from urban space and agricultural space. A matching ecological space classification system has been established, which may help in understanding the ecosystem model and related ecological processes. According to the constructed ecological space classification system, ecological spatial data processing was conducted. The standard deviation ellipse model was used to analyze the pattern of various ecological spaces in China and the migration of their barycenter. The results of the study show the following: 1) China’s territorial ecological space area showed a downward trend from 2000 to 2015. From the aspect of flow, the area of ecological space mainly flows into urban space, followed by agricultural space. According to the division of the first-level ecological space, the main ecological space structure of China is grassland ecological space and woodland ecological space. 2) The spatial distribution characteristics of China’s territorial ecological space are more prominent, and the distribution is very uneven. During the study period, the variation of the standard deviation ellipse angle θ of the ecological space is small, and the migration of the barycenter is not obvious, indicating that China’s territorial ecological space is in a relatively stable state. 3) It is necessary to implement a differentiated policy on the optimization and control of territorial ecological space according to the non-equilibrium of territorial space, and build a unified and coordinated territorial space management and control system. Such a differentiation policy would provide a basis for decision making and a reference for formulating strategies for the sustainable development of the regional ecological environment and optimizing the spatial layout of the territory.