Ecosystem water use efficiency (WUE describes carbon-water flux coupling in terrestrial ecosystems. Understanding response and resilience of WUE to drought are essential for sustainable water resource and ecosystem management under increasing drought risks over China due to climate warming. Here we analyzed the response of ecosystem WUE to drought (spatiotemporal variability and resilience) over China during 1982–2015 based on an evapotranspiration (ET) dataset based on the model tree ensemble (MTE) algorithm using flux-tower ET measurements and satellite-retrieved GPP data. The results showed that the multiyear average WUE was 1.55 g C kg⁻¹ H₂O over China. WUE increased in 77.1% of Chinese territory during the past 34 years. During drought periods, the ecosystem WUE increased mainly in the northeast of Inner Mongolia, Northeast China and some regions in southern China with abundant forests but decreased in northwestern and central China. An apparent lagging effect of drought on ecosystem WUE was observed in the east of Inner Mongolia and Northeast China, the west and east regions of North China and the central part of Tibetan Plateau. Some ecosystems (e.g., deciduous needle-leaf forests, deciduous broadleaf forests, evergreen broadleaf forests and evergreen needle-leaf forests) in Central China, Northeast and Southwest China exhibited relatively greater resilience to drought than others by improving their WUE. Our findings would provide useful information for Chinese government to adopt a reasonable approach for maintaining the structure and functions of ecosystems under drought disturbance in future.

Monitoring urban expansion and greenspace change is an urgent need for planning and decision-making. This paper presents a methodology integrating Principal Component Analysis (PCA) and hybrid classifier to undertake this kind of work using a sequence of multi-sensor SPOT images (SPOT-2,3,5) and Sentinel-2A data from 1996 to 2016 in Hangzhou City, which is the central metropolis of the Yangtze River Delta in China. In this study, orthorectification was first applied on the SPOT and Sentinel-2A images to guarantee precise geometric correction which outperformed the conventional polynomial transformation method. After pre-processing, PCA and hybrid classifier were used together to enhance and extract change information. Accuracy assessment combining stratified random and user-defined plots sampling strategies was performed with 930 reference points. The results indicate reasonable high accuracies for four periods. It was further revealed that the proposed method yielded higher accuracy than that of the traditional post-classification comparison approach. On the whole, the developed methodology provides the effectiveness of monitoring urban expansion and green space change in this study, despite the existence of obvious confusions that resulted from compound factors.

This paper investigates a simplified polarimetric decomposition for soil moisture retrieval over agricultural fields. In order to overcome the coherent superposition of the backscattering contributions from vegetation and underlying soils, a simplification of an existing polarimetric decomposition is proposed in this study. It aims to retrieve the soil moisture by using only the surface scattering component, once the volume scattering contribution is removed. Evaluation of the proposed simplified algorithm is performed using extensive ground measurements of soil and vegetation characteristics and the time series of UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar) data collected in the framework of SMAP (Soil Moisture Active Passive) Validation Experiment 2012 (SMAPVEX12). The retrieval process is tested and analyzed in detail for a variety of crops during the phenological stages considered in this study. The results show that the performance of soil moisture retrieval depends on both the crop types and the crop phenological stage. Soybean and pasture fields present the higher inversion rate during the considered phenological stage, while over canola and wheat fields, the soil moisture can be retrieved only partially during the crop developing stage. RMSE of 0.06–0.12 m³/m³ and an inversion rate of 26%–38% are obtained for the soil moisture retrieval based on the simplified polarimetric decomposition.

Cyclone Global Navigation Satellite System (CYGNSS) soil moisture (SM) product is characterized by high temporal resolution, but the relative strengths and weaknesses of this new product are unknown. In this study, we analyze the performance of CYGNSS SM product across varied land covers and climates, using the triple collocation (TC) analysis and in-situ validation. The Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer 2 (AMSR2) Land Parameter Retrieval Model (LPRM), and European Space Agency Climate Change Initiative (ESA CCI) Active SM products were used as references as well as data alternatives to calculate TC-based standard deviation ( SD_TC ), correlation ( R_TC ), and in-situ validation Pearson's correlation coefficient (R), unbiased root-mean-square error (ubRMSE). The TC analysis indicated that CYGNSS had a relatively low median SD_TC of 0.024 m ³ /m ³ and R_TC of 0.419. Validation based on 251 in-situ SM stations showed that CYGNSS obtained a relatively low median ubRMSE of 0.057 m ³ /m ³

This paper aims to retrieve the temporal dynamics of soil moisture from 2015 to 2019 over an agricultural site in Southeast Australia using the Soil Moisture Active Passive (SMAP) brightness temperature. To meet this objective, two machine learning approaches, Random Forest (RF), Support Vector Machine (SVM), as well as a statistical Ordinary Least Squares (OLS) model were established, with the auxiliary data including the 16-day composite MODIS NDVI (MOD13Q1) and Surface Temperature (ST). The entire data were divided into two parts corresponding to ascending (6:00 p.m. local time) and descending (6:00 a.m. local time) orbits of SMAP overpasses. Thus, the three models were trained using the descending data acquired during the five years (2015 to 2019), and validated using the ascending product of the same period. Consequently, three different temporal variations of the soil moisture were obtained based on the three models. To evaluate their accuracies, the retrieved soil moisture was compared against the SMAP level-2 soil moisture product, as well as to in-situ ground station data. The comparative results show that the soil moisture obtained using the OLS, RF and SVM algorithms are highly correlated to the SMAP level-2 product, with high coefficients of determination (R²_OLS = 0.981, R²_SVM = 0.943, R²_RF = 0.983) and low RMSE (RMSE_OLS = 0.016 cm³/cm³, RMSE_SVM = 0.047 cm³/cm³, RMSE_RF = 0.016 cm³/cm³). Meanwhile, the estimated soil moistures agree with in-situ station data across different years (R²_OLS = 0.376~0.85, R²_SVM = 0.376~0.814, R²_RF = 0.39~0.854; RMSE_OLS = 0.049~0.105 cm³/cm³, RMSE_SVM = 0.073~0.1 cm³/cm³, RMSE_RF = 0.047~0.102 cm³/cm³), but an overestimation issue is observed for high vegetation conditions. The RF algorithm outperformed the SVM and OLS, in terms of the agreement with the ground measurements. This study suggests an alternative soil moisture retrieval scheme, in complementary to the SMAP baseline algorithm, for a fast soil moisture retrieval.

This paper presents an approach for retrieval of soil moisture in Nagqu region of Tibetan Plateau using VV-polarized Sentinel-1 SAR and MODIS optical data, by coupling the semi-empirical Oh-2004 model and the Water Cloud Model (WCM). The Oh model is first used to estimate the surface roughness parameter based on the hypothesis that the roughness is invariant among SAR acquisitions. Afterward, the vegetation water content (VWC) in the WCM is calculated from the daily MODIS NDVI data obtained by temporal interpolation. To improve the performance of the model, the parameters A, B, and α of the WCM are analyzed and optimized using randomly selected half of the sampled dataset. Then, the soil moisture is retrieved by minimizing a cost function between the simulated and measured backscattering coefficients. The comparison of the retrieved soil moisture with the ground measurements shows the determination coefficient R² and the Root Mean Square Error (RMSE) are 0.46 and 0.08 m³/m³, respectively. These results demonstrate the capability and reliability of Sentinel-1 SAR data for estimating the soil moisture over the Tibetan Plateau.