Soil moisture (SM) plays an important role in the water–heat–energy exchange and water cycle of the land ecosystem. Long-term SM products are vital in the time series study of ecology and hydrology. Therefore, it is vital to extend the time span with limited SM monitoring sensors, since there is no single long-term SM product currently. In this study, an SM product prolonging method based on an artificial neural network (ANN) and moderate-resolution imaging spectroradiometer (MODIS) optical products was proposed. The prolonging results of Soil Moisture Active Passive (SMAP) and Fenyun-3B (FY3B) products were validated in Tibetan Plateau to present the feasibility of this method. The result shows this method is feasible in areas under medium vegetation cover (0.2$R $= 0.84, RMSE3${cm}^{-3}$) within situmeasurements for both SMAP and FY-3B products. The generated long-term SM will benefit the global water cycle study.

Error estimation of precipitation products is an important procedure in the data quality evaluation. It is a challenging task due to the lack of the in-situ ground observations and the variations of the geophysical characteristics in regions with complex terrain. Compared with the traditional methods, the double instrumental variable (DIV) method has the merits of being able to estimate the errors between two products. In this study, the double instrumental variable method for data error estimation is applied and validated on precipitation products in regions with complex terrain. The DIV-based Errors for two state-of-the-art precipitation products IMERG and SM2RAIN are being further verified by using another high-accuracy ground-based precipitation products CMPA. The results indicate that the DIV-based Errors of IMERG and SM2RAIN range from 0 to 25 mm per day and 0 to 15 mm per day, respectively. The RMSEs of IMERG and SM2RAIN compared with CMPA, which are defined as CMPA-based Errors, are ranging from 0 to 23 mm and 0 to 22 mm, respectively. It is concluded that the spatial distribution of the DIV-based Errors shows the consistency with the CMPA-based Errors, which further demonstrates the potential of using double instrumental variable method for precipitation products fusion.

Highly quality soil moisture is significant for hydrological, meteorological and agricultural applications. At present, active and passive remote sensing are the only ways to monitor soil moisture directly at regional scale. However, the quality of single satellite-based soil moisture product is insufficient to meet the requirements of these applications. Hence, fusing these two soil moisture products to improve their quality of change capture ability and accuracy is a necessary and challenging work. This study proposes an improved double instrumental variable method to fuse active and passive soil moisture products. First, the method is improved in finding the best instrumental variables in time series based on correlation coefficient. Second, fused weights of input soil moisture products are estimated using the improved method. Finally, fused soil moisture products are obtained with higher change capture ability and higher accuracy. The Tibetan Plateau was selected as the study area to test the algorithm using both of the Climate Change Initiative (CCI) active and passive soil moisture products from the European Space Agency (ESA). The ground validation results show that, compared with the original soil moisture products, the change capture ability, expressed by the correlation coefficient (R), and the accuracy, expressed by the unbiased root mean square deviation (ubRMSD), have been both improved by about 10% on average. This study indicates that the proposed fusion method can effectively improve the quality of soil moisture products to further understand the global changing water cycle.

Irrigation is critical to agricultural production in arid and semiarid regions, and it is imperative to map high-resolution irrigated area to improve water productivity. This study proposes a field-scale (30-m resolution) irrigated area mapping method based on soil moisture change detection using remote sensing data only. First, normalized soil moisture is obtained using the optical trapezoid model (OPTRAM) and then converted to soil water content. Next, individual irrigation events are identified in the time series of soil water content using threshold detection. Finally, irrigation events are accumulated over the time series, and then, the irrigated area map can be obtained. This method was tested using Google Earth Engine (GEE) to analyze remote sensing images and map irrigated areas in a typical arid and semiarid region called Hexi Corridor in northwestern China in the past 30 years.In situvalidation shows that this method has an accuracy close to 100%. The shortcoming of low recall is also overcome by long-term observations. An application of the proposed method shows that the irrigated cropland of Hexi Corridor has increased by 4840 km2(42.2%) over a 31-year time period (1990–2020). This field-scale irrigated area mapping method can improve the management of water resources.

Channel network topology plays an important role in hydrological analysis. This letter proposes an innovative method to construct it based only on remote sensing images. The method uses spectral water indexes and mask of large lakes and ocean areas derived from remote sensing data to generate the map of channels. Then, a morphological thinning algorithm is introduced to extract the initial skeleton of channels. Moreover, an iterative pruning process based on a graph algorithm is proposed to simplify the initial skeleton. Finally, according to the simplified skeleton and its adjacency matrix, a new connectivity graph can be constructed to describe the channel network topology. The proposed method can construct complete skeleton structure and the topology of channels with good connectivity in an automatic way. The output will facilitate detailed hydrological modeling and further applications.

Soil moisture is a key variable in the exchange of water and energy between the land surface and the atmosphere. Long time series of and spatio-temporal continuous soil moisture is of great importance to meteorological and hydrological applications, such as weather forecasting, global change and drought monitoring. In this study, the Essential Climate Variable (ECV) soil moisture product of the Tibetan Plateau (TP) from 2002 to 2015 was reconstructed using the General Regression Neural Network (GRNN) based on reconstructed MODIS products, i.e., LST, NDVI, and Albedo. Results show that the ECV soil moisture could be well reconstructed with R2higher than 0.71, RMSE less than 0.05 cm3cm-3and absolute Bias less than 0.03 cm3cm-3for both grids of 0.25°×0.25° and 1°×1°, compared with the in-situ measurements in 2012 over the TP. The reconstructed long time series of and spatio-temporal continuous soil moisture could be valuable in hydrometeorological studies of the TP.

This paper proposes a novel method to fuse multi-source acoustical remote sensing images for marine sediment mapping. Acoustic images from sidescan sonar, multibeam bathymetry, and sub-bottom profiler describe the 2-D, 2.5-D, and 3-D properties of the seabed sediment respectively. In attempt to make use of the multi-dimensional information from the multi-source data, the evidential fusion method, combined with object-based classification and spatial overlay analysis is proposed. Firstly, marine sediments are independently classified in the multi-source acoustic images with object based methods. Then, the spatial overlay analysis is conducted to group the classification results as evidence of different sediments. Finally, the evidential fusion method is employed to determine the exact distribution of sediments on the map. The proposed method introduces the classification results of sub-bottom profiler data that provide useful information, even though the data is limited in spatial coverage and is rarely used in automatic sediment mapping. The experiments show that the fused data from the three different sources of acoustic images significantly improve the mapping accuracy.

This article proposed a modified vegetation backscattering model to retrieve soil moisture using C-band RADARSAT-2 images and field measurements over agricultural study sites in Shaanxi province of China. The effects of vegetation and soil on radar signals were separated at pixel level by introducing vegetation coverage in the modified model. The direct scattering contribution from the underlying ground surface was considered as an important component in the total backscattering at agricultural sites. The results indicated that the modified model could be effectively applied to a variety of surface cover types ranging from sparse to full vegetation cover. The accuracy of soil moisture retrieval was significantly high with R2 and RMSE of 84.3% and 0.028 m3/m3, respectively. Therefore, the modified model was suitable for soil moisture retrieval at large scales by combining the advantages of SAR and optical remote sensing data.

This paper proposes a framework of change detection with multi-source remote sensing images through collaboration of multiple operators. Firstly, pre-processed images are distributed to different operators. Then the images are classified by the operators independently. Finally, with uploaded classification results, change detection result can be derived through evidential fusion based on PCR5 rule in the server. By making use of complementary and redundant information in the images, the framework can solve the problems of information loss, imprecision, inconformity or conflict in multi-source data. The framework is applied to detect a landslide barrier lake with multi-source images from Landsat7 and GF-1, results show that as the amount of operator and input image increases, the proposed framework performs better than commonly used major voting strategy for disaster mapping.

The inversion of vegetation parameters using microwave remote sensing is usually affected by the heterogeneous distribution of vegetation, sparse vegetation cover, and bare soil, which leads to unsatisfactory results in parameter estimation of agricultural applications. In this letter, in order to solve the problem of surface vegetation parameter retrieval by using microwave remote sensing, a modified water-cloud model (WCM) was developed to retrieve leaf area index (LAI) by adding vegetation coverage and direct effect of bare soil on the total backscatter coefficients, which fully took into account the distribution of vegetation cover. The modified model was validated between the simulated backscatter coefficients and measurements based on ground observations and RADARSAT-2 data in China. Then, a look-up table algorithm was applied to calculate the value of vegetation water content and retrieve LAI according to a linear relationship between vegetation water content and LAI. Results indicated that the modified model was more sensitive to vegetation condition and the estimation accuracy was higher than that of the original WCM.$R^{2}$and rmse were 85.0% and 0.918 dB in HH polarization, and 73.9% and 1.475 dB in VV polarization, respectively. Meanwhile, the modified model could separate the scattering influences produced by the vegetation cover and bare soil components on the backscatter coefficients effectively. The accuracy of LAI retrieval was significantly high with$R^{2}$and rmse of 84.1% and 0.233 m2/m2, respectively. This method will provide support for estimating LAI of winter wheat by using radar data in a wide range.

This research aims at providing a decision-support method for the government and the public of their water resource projects allocation. The Water Poverty Index (WPI) is introduced to evaluate the extent of water supply shortage, and the WPI driving factors of each evaluated unit are analyzed using the Least Square Error (LSE) method. Then 32 types of water-supply related projects are organized by their contribution to WPI components. This paper provides a method to calculate a decision matrix bases on the results of WPI driving factor analysis and 32 types of water resource projects. The result of decision matrix calculation can be visualized to illustrate suggestions for the government and the public that which projects are most effective for a certain administrative unit. Qianjiang district, a mountainous poverty district in Chongqing city, southwest of China, is chosen as the study case in this research.

Hetao Irrigation District, located in Bayan nur city of Inner Mongolia, is one of the largest irrigation areas in Asia. Because of little rainfall and large evaporation, lack of water resources has become one of the main factors which restrict the development of the local economy. Real-time monitoring of the actual irrigation area can contribute to efficient allocation of water and soil resources. This article selected four rounds of images to calculate MPDI and build a spectral feature space. By comparing the estimated and statistical water volume, the best estimation accuracy reached to 92.8%. And a good agreement was conducted between MPDI and soil moisture content on July 20. The result showed that MPDI was conducive to the real-time monitoring of actual irrigation area.