Search

Ponds constitute a pivotal component of aquatic ecosystems. The aquatic ecosystem of the Huai River Basin (HRB) in China was once damaged by severe pollution, and numerous ponds in the basin have not been secured. In this paper, Shenqiu County, a typical county in HRB with many ponds, is selected. Based on high-resolution images with ALOS in 2010, GF-2 in 2016, and GF-1 in 2022, we employed discriminant analysis (DA), classification and regression tree, support vector machine, and random forest to extract the ponds based on object-oriented and further analyzed the spatial-temporal variations of the ponds in this county. The results of the DA in these three years exhibited a higher kappa coefficient (>0.7), and overall accuracy (>75%), signifying superior performance when compared to the other three methods. There were 4625, 5315, and 4748 ponds in 2010, 2016, and 2022, with a total area of 12.87, 11.99, and 9.37 km2, respectively. The number of ponds had a trend of rising in the initial period (2010–2016) and falling later (2016–2022), while the total area revealed a continuous decline. Meanwhile, these ponds showed a clustering phenomenon with three main clustering areas, and the scope of the clustering areas also changed to a certain extent from 2010 to 2022. Our study offers valuable methodological support for the ecological monitoring and management of water environments in regions characterized by a dense concentration of ponds. The crucial data related to ponds in this study will help inform both environmental and social development initiatives within the area. [ABSTRACT FROM AUTHOR]

Through the reasonable calculation of water resources, evaluating the irrigation carrying capacity of farmland under the constraints of water resources is crucial for optimizing the spatial distribution of agricultural production and ecology and rationally adjusting the scale of agricultural production. This paper proposes an optimization framework based on Type 2 fuzzy chance-constrained programming (T2FCCP) to solve the problem of regional water resources optimal allocation and evaluation of farmland irrigation carrying capacity under uncertain conditions. To illustrate the applicability of the proposed framework, this paper conducts a case study on Lancang County, Puer City, Yunnan Province. Methods, such as watershed harmony evaluation method, remote sensing data, and shared socioeconomic pathways (SSPs), are applied and integrated into the proposed optimization framework to systematically deal with uncertainties in water resource systems and agricultural systems. The results include the costs and benefits of regional water and soil resources systems, water resources optimal allocation, and crop planting structure results under different SSPs in Lancang County, Puer City. The results also show that the total cost under T2FCCP is about 5% lower than that under fuzzy chance-constrained programming (FCCP) and about 17% lower than that under chance-constrained programming (CCP). By 2025, the water resources carrying capacity of different tributaries in Lancang County, Puer City will increase, and based on the evaluation results of agricultural production irrigation carrying capacity, suggestions are given to ensure agricultural production carrying capacity. [ABSTRACT FROM AUTHOR]

Social media texts spontaneously produced and uploaded by the public contain a wealth of disaster information. As a supplementary data source for remote sensing, they have played an important role in disaster reduction and emergency response in recent years. However, social media also has certain flaws, such as insufficient location information, etc. This affects the efficiency of combining these data with remote sensing data. For flood disasters in particular, extensively flooded areas limit the distribution of social media data, which makes it difficult for these data to function as they should. In this paper, we propose a disaster reduction framework to solve these problems. We first used an approach that was based on search engine and lexical rules to automatically extract disaster-related location information from social media texts. Then, we combined the extracted information with the upload location of social media itself to construct location-pointing relationships. These relationships were used to build a new social network, which can be used in combination with remote sensing images for disaster analysis. The analysis integrated the advantages of social media and remote sensing. It can not only provide macro disaster information in the study area but can also assist in evaluating the disaster situation in different flooded areas from the perspective of public observation. In addition, the timeliness of social media data also improved the continuity and situational awareness of flood monitoring. A case study of the flood disaster in the Yangtze River Basin in China in 2020 was used to verify the effectiveness of the method described in this paper. [ABSTRACT FROM AUTHOR]

To protect and restore this downstream ecosystem, the Tarim River Basin Administration Bureau (TBAB) initiated the Ecological Water Compensation (EWC) project from 2000 to 2018. Revealing the mechanism of vegetation-hydroecological response processes in the lower reaches of the Tarim River before and after EWC work is conducive to water resource planning, utilization and protection. In this paper, the spatiotemporal responses of vegetation and groundwater to EWC were examined at the points, lines, and area (PLA) scale by coupling remote sensing techniques and field station observation data collected between 2000 and 2017. The findings indicated that (1) In general, the regional fractional vegetation coverage (FVC) increased significantly, and the average FVC growth rate was 3.5%/year from 2000 to 2017 (R2 > 0.84, p < 0.01, 2-tailed). (2) The regional vegetation restoration process showed obvious fluctuations and stage characteristics, but the spatial scope of the significantly increased vegetation area was limited. Plants grew rapidly within 10 km of the river, while 10 km away from the water channel, no obvious change was observed. (3) Strong coupling relationships were identified among the FVC growth, EWC volume and groundwater depth variations (p < 0.05, 2-tailed). The response times of the regional vegetation and groundwater depth to EWC indicated one-year lags. The above results imply that the regional ecological environment was significantly improved over the study period, thus confirming that the EWC project made remarkable accomplishments. However, the effect of ecological restoration is not sufficiently stable at present. Vegetation restoration has mainly been centralized around the river channel and is greatly dependent on the annual EWC volume. In addition, the local conditions begin to degrade soon after an EWC project is terminated, and vice versa; when EWC commences, the FVC immediately begins to improve. Therefore, the current EWC achievements need to be further consolidated and strengthened in the future. [ABSTRACT FROM AUTHOR]

Due to its wide scope, rapid development and high hazard, remote sensing images with a single time phase and local area coverage cannot meet the needs of large-scale flood detection. The problem of large-scale flooding emergency detection can be well solved by using Google Earth Engine (GEE) platform, which has the characteristics of fast data computing speed, easy image retrieval and rich remote sensing data. In this paper, we propose a method for flood detection using Sentienl-1 imagery based on the GEE platform, which can quickly accomplish a large scale flood detection. The method was validated in a flooding event that occurred around the Pearl River basin, China in June 2022. It shown that using the proposed method can not only meet the needs of basin-wide flood detection, but also extract information on the temporal dimension of the flood development status. [ABSTRACT FROM AUTHOR]

The parameters of hydrological models should be determined before applying those models to estimate or predict hydrological processes. The Xin'anjiang (XAJ) hydrological model is widely used throughout China. Since the prediction in ungauged basins (PUB) era, the regionalization of the XAJ model parameters has been a subject of intense focus; nevertheless, while many efforts have targeted parameters related to runoff yield using in-site data sets, classic regression has predominantly been applied. In this paper, we employed remotely sensed underlying surface data and a machine learning approach to establish models for estimating the runoff routing parameter, namely, CS, of the XAJ model. The study was conducted on 114 catchments from the Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) data set, and the relationships between CS and various underlying surface characteristics were explored by a gradient-boosted regression tree (GBRT). The results showed that the drainage density, stream source density and area of the catchment were the three major factors with the most significant impact on CS. The best correlation coefficient (r), root mean square error (RMSE) and mean absolute error (MAE) between the GBRT-estimated and calibrated CS were 0.96, 0.06 and 0.04, respectively, verifying the good performance of GBRT in estimating CS. Although bias was noted between the GBRT-estimated and calibrated CS, runoff simulations using the GBRT-estimated CS could still achieve results comparable to those using the calibrated CS. Further validations based on two catchments in China confirmed the overall robustness and accuracy of simulating runoff processes using the GBRT-estimated CS. Our results confirm the following hypotheses: (1) with the help of large sample of catchments and associated remote sensing data, the ML-based approach can capture the nonstationary and nonlinear relationships between CS and the underlying surface characteristics and (2) CS estimated by ML from large samples has a robustness that can guarantee the overall performance of the XAJ mode. This study advances the methodology for quantitatively estimating the XAJ model parameters and can be extended to other parameters or other models. [ABSTRACT FROM AUTHOR]

For China, which has many big rivers, there is an urgent need for efficient dynamic monitoring technology of water and soil loss. However, there are some problems in the current 3S (RS, GIS and GPS) technology for dynamic monitoring water and soil loss. This paper takes the Yangtze River Basin as an example to innovate and optimize the key technologies of the remote sensing interpretation of the water and soil loss dynamic monitoring of the Yangtze River Basin, and overcome the major technical difficulties in the remote sensing interpretation of the dynamic monitoring of water and soil loss. The key technologies include: 1) The establishment of a field investigation platform based on Internet and UAV (Unmanned Aerial Vehicle) for remote sensing interpretation; 2) Near real-time evaluating key factors of soil and water loss based on UAV photogrammetry and digital terrain analysis; 3) Geometric and Radiometric Simultaneous Correction Model (GRSCM) framework for remote sensing images pre-processing; 4) An object-oriented land use change update quality control method supported by multi-PC and GIS; and an object-oriented remote sensing image classification system based on random forest, deep learning and transfer learning; 5) Improvement of quantitative change detection method for image vegetation and three-dimensional topography. The results have been successfully applied in the remote sensing interpretation of the dynamic monitoring of water and soil loss in the national key prevention and control area of the Yangtze River Basin. They have been provided a scientific reference for the development planning of The Yangtze River Economic Zone. [ABSTRACT FROM AUTHOR]

It is well known that flooding brings great losses to people's production and life, just because it is unsuspected, is extremely extensive, and has high frequency. More than half of the people in China live in flood-prone areas, and their lives and properties are threatened. Flood risk assessment is one of the measures of flood management, and it is economically and socially important to assess flood risk. The scope of the traditional monitoring and forecasting early warning system is mainly limited to the area affected by flash floods, while the objective reality of real-time monitoring, forecasting, and early warning of flood disasters in the area affected by river floods and by the scheduling of riverine terrace power stations is not possible. The multiphysics remote sensing big data fusion analysis can divide the data into grids according to the grid method in the sliding time window, filter the normal data by information entropy in each grid, judge the remaining data that may be abnormal by using local abnormality factor, and eliminate the abnormal data according to the judgment result. This paper introduces the application of remote sensing in flood control field, proposes the framework of basin flood prediction based on multiphysics field remote sensing big data fusion analysis, and designs each functional module of the system according to the object-oriented idea to realize the functions of data management, image processing, spatial analysis, and simulation output. The method can change the problems of cumbersome data processing and basic parameter rate determination in traditional hydrological methods and can find certain regularity through the connection between all related factors. Meanwhile, the use of artificial intelligence and other technical means makes the calculation speed faster and the obtained results are closer to the actual measured values, which is beneficial to guide the practical work. [ABSTRACT FROM AUTHOR]

Glacier mass balance can visually indicate the degree of glacier response to climate change. The mountain glaciers are an essential source of recharge for rivers in arid regions and play a vital role in maintaining regional ecological stability and production life. This paper drives a spatially distributed degree-day model using multi-source remote sensing data such as MOD11C3 and TRMM3B43 to simulate the mass balance in the Tianshan Mountains' south and north slope basins. The spatiotemporal heterogeneity of the mass balance was compared and attributed using a Geographical detector. The results show that: (1) The glaciers in the north and south basins are mainly distributed at an altitude of 3900–4300 m, and the total glacier area accounts for 85.71%. The number of less than 1 km2 glaciers is the most in the whole region. (2) During the study period, the glaciers in the north and south basins were negative (−465.95 mm w.e.) an entire interannual change rate was −28.36 mm w.e./a. The overall trend of ablation can be divided into two stages: from 2000 to 2010a, the persistence increased, and from 2010 to 2016a, the volatility decreased. (3) In the attribution of mass balance, the factors affecting glacier mass balance can be divided into two parts: climate and topography. The cumulative contribution rate of climate factors in Kaidu is nearly 20% higher than that of topographic factors, but the contribution rate of climate factors in Manas is only 7.3% higher. Therefore, the change of glacier mass balance in the Kaidu river basin is more driven by climate factors, while the glacier mass balance in the Manas river basin is more affected by the combination of climate and topographic factors. (4) The climate accumulation is the dominant factor in the Manas river basin (69.55%); for the ablation area, the Kaidu river basin is dominated by climate (70.85%), and the Manas river basin is dominated by topographic factors (54.11%). Due to the driving force of climate and topographic factors and the different coupling modes, glacier mass balance's spatiotemporal heterogeneity in the north and south slope basins is caused. This study contributes to analyzing the mechanism of regional changes in the glacier mass balance. It provides a scientific basis for investigating the characteristics of water resource changes and water resource regulation in the north and south slope basins of the Tianshan Mountains. [ABSTRACT FROM AUTHOR]

The Wenchuan earthquake occurred in Sichuan Province, China, on May 12, 2008, and caused a large number of secondary geological disasters such as collapses, debris flows, and landslides, as well as extensive damage to local vegetation. The earthquake had an impact on the regional climate, seriously affecting the sustainable development of the regional environment and economy. In this study, the normalized difference vegetation index (NDVI) was used to characterize the vegetation status, which was calculated from images acquired by the MODIS sensor. Based on the vegetation cover and precipitation of the area affected by the Wenchuan earthquake over about 70 years, in this paper we analyze the characteristics of vegetation changes and their impact on the climate after the earthquake. The results revealed that the above secondary geological disasters caused by the earthquake destroyed vegetation. After the earthquake, NDVI decreased from 0.394 in 2007 to 0.383 on July 1, 2008, and then increased year by year to 0.413 in 2015, except in 2010. During the two years after the earthquake, several large-scale debris flows occurred in this area, damaging the surface vegetation, so the vegetation coverage was low in 2010. The change of the land cover has an indirect impact on the local precipitation. The annual and seasonal (except winter) precipitation trended downward from 1948 to 2018, with an annual average decrease of 1.6069 mm, but trended upward from 2008 to 2018, with an annual average increase of 14.012 mm, after the Wenchuan earthquake, indicating the recovery of vegetation. The increased vegetation promoted regional precipitation, but the interaction mechanism between the vegetation cover and the precipitation is complex. The effect of the vegetation differs in different regions and cannot be generalized. This study provides theoretical support for vegetation restoration and environmental management in areas with strong earthquakes. [ABSTRACT FROM AUTHOR]

This paper aims to compare the performance of typical open global DEM datasets by using the indexes of elevation error, relative error and hydrologic network. Taking Fenhe River Basin of China as the study area, this research made quantitative performance comparison among four typical open global DEM datasets including SRTM data with 1" (SRTM1) and 3" (SRTM3) resolutions, ASTER Global DEM data at the 2nd version (GDEM-v2) and ALOS World 3D-30 m (AW3D) data. Through process and selection, more than 80,000 ICESat/GLA14 points were used as the reference data, and the elevation error was computed and compared accordingly. Furthermore, relative error was analyzed using slope values, and false slope ratio index was computed and categorically compared. Finally, the hydrologic networks extracted from the four DEM datasets were compared to the reference hydrologic network acquired by visual interpretation from remote sensing images. The research results show that the AW3D has the best performance, which is approximate to but a little better than SRTM1. The performance of SRTM3 and GDEM-v2 is similar, which are much worse than that of AW3D and SRTM1, and the performance of GDEM-v2 is the worst of all. [ABSTRACT FROM AUTHOR]

Hydrological simulation in areas with complex topography and pronounced climatic heterogeneity has a certain degree of uncertainty, and how precipitation data from multiple types of sources influence hydrological simulation remains unclear. Performances of precipitation datasets based on multiple interpolation methods (i.e., inverse distance weighted [IDW], cokriging, and ANUSPLIN) and satellite remote sensing datasets (i.e., tropical rainfall measuring mission multi‐satellite precipitation analysis 3B42 version7 [TRMM 3B42V7], precipitation estimation from remote sensing information using artificial neural networks–climate data record [PERSIANN‐CDR], climate predication centre morphing technique, and sian precipitation–highly resolved observational data integration towards evaluation of water resources [APHRODITE]) were obtained for comparison. Hydrological responses from these precipitation datasets were evaluated through distributed hydrological model variable infiltration capacity. The Yalong river basin (YRB), located in the Hengduan Mountains region and characterized by the significant heterogeneity of precipitation in China, was focused. Four sub‐regions (i.e., sub‐region I to IV) were divided. The results indicated that: (a) TRMM 3B42V7 and APHRODITE data were superior in detection capability from daily to annual measurement than other satellite precipitation data sets generally; (b) under the scale of gauges, TRMM 3B42V7, APHRODITE, and cokriging performed well in sub‐regions I, II, and IV, respectively. In sub‐region III, performance of the interpolation methods was better than that of TRMM 3B42V7; (c) the interpolation datasets exhibited a poor capability to simulate peak runoff. The combination dataset COM could improve the overall runoff simulation in flood periods; and (d) the interpolation method has the problem of excessive smoothness. Whatever the interpolation dataset is, hydrological simulation tends to result in smaller runoff, which was related to that the measurements from meteorological stations in mountainous valleys are not necessarily representative of the entire region. In this paper, the applicability of multi‐source precipitation data in the hydrological simulation process of YRB was comprehensively analysed, which could provide effective references for improving the accuracy of precipitation data and the applicability of hydrological simulation. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) accounts for 60%-70% of terrestrial precipitation. Accurately calculating ET has always been one of the hot issues in ecohydrological research. Watershed ET includes natural ET and artificial ET. Natural ET includes vegetation transpiration, bare land evaporation (the two called land ET) and water surface evaporation. Artificial ET mainly refers to irrigation ET, that is, ET generated by human activities. Estimating total ET is the premise of watershed ET management. In this paper, we used the Penman‒Monteith model to estimate land ET, the Dalton model to estimate water surface evaporation, and the AquaCrop model to estimate ET generated by agricultural irrigation water in the Ziya River Basin from 2005 to 2015. Based on the integrated watershed evapotranspiration model, the effects of driving factors on ET were quantitatively analysed. The results showed that the Penman‒Monteith model, Dalton model and AquaCrop model had high simulation accuracy. The total ET fluctuated between 418.4 and 452.3 mm/yr, and the annual average ET was 437.5 mm/yr. The ET of the main land use types was ranked as woodland (496.7 mm/yr) > arable land (468.9 mm/yr) > grassland (320.0 mm/yr). Spatially, the ET in mountainous areas was lower than that in plain areas. The distribution of ET in the total amount, mountainous and plain areas, woodland, grassland and arable land showed a significant Gaussian distribution. Temporally, the interannual variation was small, and the intra-annual difference was mainly caused by climate factors. The conversion of grassland and woodland into arable land had the greatest impact on the total ET. Temperature, precipitation and leaf area index had a positive impact on ET. The impact of land cover change on ET was more significant than that of climate factors. This study can provide an integrated approach for ET estimation in water resource management and land planning. [ABSTRACT FROM AUTHOR]

The landscape pattern is affected by human activities and the natural environment. China's urbanization has been a great achievement in the past 30 years, but also it has caused a variety of environmental problems. The landscape of the mountain valley area is particularly affected by human activities and the natural environment. The rise of remote sensing technologies has promoted a better understanding of ecological process, and the quantitative study on landscape dynamics has received more and more considerable attention. In this paper, four years from 2000, 2005, 2010, and 2015 were selected as the research time. Using Landsat TM/ETM remote sensing image, DEM, and socioeconomic data, we calculated the landscape pattern indices in four districts (Lishi, Fangshan, Liulin, and Zhongyang) of Luliang City on patch scale, land cover scale and landscape scale respectively. Thus, the change of landscape pattern in four districts from 2000 to 2015 were obtained. The results showed that: 􀃦 the landscape in the mountain valley area has become more and more fragmented in the past 15 years (2000-2015); 􀃧 the landscape dynamics of the main and tributary catchment areas of the Sanchuan River were different from that of urban development, which is closely related to the development stages of urban and rural areas; 􀃨 there were differences in landscape changes between different topographic and landform units, and the landscape at higher altitudes was more vulnerable to disturbance. This study can provide some reference for the ecological restoration and management on landscape scale in the mountain and valley areas of the north and northwest China. [ABSTRACT FROM AUTHOR]

Accurately assessing and predicting the impacts of land use changes on ecosystem carbon stocks in the Yellow River Basin (YRB) and exploring the optimization of land use structure to increase ecosystem carbon stocks are of great practical significance for China to achieve the goal of "double carbon". In this study, we used multi-year remote sensing data, meteorological data and statistical data to measure the ecosystem carbon stock in the YRB from 2000 to 2020 based on the InVEST model, and then simulated and measured the ecosystem carbon stock under four different land use scenarios coupled with the FLUS model in 2030. The results show that, from 2000 to 2020, urban expansion in the YRB continued, but woodland and grassland grew more slowly. Carbon stock showed an increasing trend during the first 20 years, with an overall increase of 7.2 megatons, or 0.23%. Simulating the four land use scenarios in 2030, carbon stock will decrease the most under the cropland protection scenario, with a decrease of 17.7 megatons compared with 2020. However, carbon stock increases the most under the ecological protection scenario, with a maximum increase of 9.1 megatons. Furthermore, distinct trends in carbon storage were observed across different regions, with significant increases in the upstream under the natural development scenario, in the midstream under the ecological protection scenario and in the downstream under the cropland protection scenario. We suggest that the upstream should maintain the existing development mode, with ecological protection prioritized in the middle reaches and farmland protection prioritized in the lower reaches. This study provides a scientific basis for the carbon balance, land use structure adjustment and land management decision-making in the YRB. [ABSTRACT FROM AUTHOR]

Studying the spatial representativeness of carbon flux measurement data for typical land cover types can provide important information for benchmarking Earth system models and validating multiple-scale remote sensing products. In our study, daily gross primary productivity (GPP) was firstly derived from eddy covariance observation systems and seasonal variations in field GPP were analyzed at nine flux tower sites for typical land cover types in the Heihe River Basin, China. Then, the real-time footprint distance and climate footprint distance of the field GPP were obtained by using a footprint source area model. Lastly, multiple-scale GPP products were validated at footprint scale, and the impacts (measurement height, surface roughness and turbulent state of the atmosphere) on the footprint distance of field GPP were analyzed. The results of this paper demonstrated that climate footprint distances ranged from about 500 m to 1500 m for different land cover types in the Heihe River Basin. The accuracy was higher when validating MODIS GPP products at footprint scale (R2 = 0.56, RMSE = 3.07 g C m−2 d−1) than at field scale (R2 = 0.51, RMSE = 3.34 g C m−2 d−1), and the same situation occurred in the validation of high-resolution downscaled GPP (R2 = 0.85, RMSE = 1.34 g C m−2 d−1 when validated at footprint scale; R2 = 0.82, RMSE = 1.47 g C m−2 d−1 when validated at field scale). The results of this study provide information about the footprints of field GPP for typical land cover types in arid and semi-arid areas in Northwestern China, and reveal that precision may be higher when validating multiple-scale remote sensing GPP products at the footprint scale than at the field scale. [ABSTRACT FROM AUTHOR]

The lower reaches of the Heihe River basin (HHLR), which is located in arid and semiarid areas of Northwest China, has experienced severe environmental deterioration in the past several decades. The ecological water conveyance project was initiated by the Chinese Government in 2000 to address this degraded ecosystem. In this paper, runoff, vegetation changes, and the relationship between runoff and vegetation in different regions were explored after the project, normalized difference vegetation index (NDVI), as a proxy of vegetation cover. For the entire lower reaches, NDVI and runoff exhibit an increasing trend after the project. In spatial, 44% of the vegetation-covered area is significantly increased; whereas, 37% also exhibits an insignificant increasing trend. Moreover, 15% of the vegetation-covered area shows an insignificant decreasing trend, and 5% exhibits a significant decrease. Overall, vegetation in HHLR improved after the ecological water conveyance project. The maximum time lag for NDVI along the east river and runoff from the east river, NDVI in the Ejina Oasis and runoff from the east river and the east canal, and NDVI along the west river and runoff from the west river was 4, 4, 3, and 6 years, respectively. However, runoff from last year had a significant influence on the current year's NDVI for the east river and the east canal. Meanwhile, autumn runoff shows a significant influence on the next year's NDVI, and spring runoff is important to the current year's NDVI. So we concluded that it is reasonable to convey more water into HHLR in autumn and spring. [ABSTRACT FROM AUTHOR]

The 2008 8.0 Ms Wenchuan earthquake caused serious ecological degradation in the northwest of China's Sichuan Province. Elucidating landscape fragmentation and spatial pattern of post-earthquake landscape is of great interest in order to improve ecological restoration and predict the spatial distribution of damaged ecosystems in earthquake-affected areas. We used four real-time remote sensing images to analyze the landscape pattern characteristics and spatial autocorrelation of the Longxi River Basin before and after the Wenchuan earthquake during the period from 2005 to 2015. In the study period, the degree of landscape fragmentation increased 1 year after the earthquake in 2009 but decreased in 2011 and 2015. The spatial distribution of forest, farmland, and shrub-grassland had significantly positive spatial correlation and the characteristics of spatial aggregation from 2005 to 2015. Construction land had no significant spatial correlation before the earthquake in 2005, but significantly positive spatial correlation after the earthquake, while traffic land had no significant spatial correlation across the study period. Unlike the other landscape types, geological disasters changed from a significantly negative spatial correlation before the earthquake to a significantly positive spatial correlation after the earthquake. However, the positive autocorrelation of all landscape types decreased with the increase of space distance, but in different distance-decay rates. The High-high spatial aggregation areas of geological disasters and construction land clustered gradually upstream of the basin and distributed in Longchi town, respectively; those of farmland distributed in the southeast of the basin increased in 2009 but then decreased, while those of forests had the opposite pattern as the dominant landscape type after the earthquake. Our results elucidated the spatial structure and distribution features of the Longxi River Basin to give a theoretical foundation for assessing the effects of ecological restoration and reconstruction management in earthquake-affected areas. [ABSTRACT FROM AUTHOR]

Estimating gross primary production (GPP) and net primary production (NPP) are significant important in studying carbon cycles. Using models driven by multi-source and multi-scale data is a promising approach to estimate GPP and NPP at regional and global scales. With a focus on data that are openly accessible, this paper presents a GPP and NPP model driven by remotely sensed data and meteorological data with spatial resolutions varying from 30 m to 0.25 degree and temporal resolutions ranging from 3 hours to 1 month, by integrating remote sensing techniques and eco-physiological process theories. Our model is also designed as part of the Multi-source data Synergized Quantitative (MuSyQ) Remote Sensing Production System. In the presented MuSyQ-NPP algorithm, daily GPP for a 10-day period was calculated as a product of incident photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (FPAR) using a light use efficiency (LUE) model. The autotrophic respiration (Ra) was determined using eco-physiological process theories and the daily NPP was obtained as the balance between GPP and Ra. To test its feasibility at regional scales, our model was performed in an arid and semi-arid region of Heihe River Basin, China to generate daily GPP and NPP during the growing season of 2012. The results indicated that both GPP and NPP exhibit clear spatial and temporal patterns in their distribution over Heihe River Basin during the growing season due to the temperature, water and solar influx conditions. After validated against ground-based measurements, MODIS GPP product (MOD17A2H) and results reported in recent literature, we found the MuSyQ-NPP algorithm could yield an RMSE of 2.973 gC m-2 d-1 and an R of 0.842 when compared with ground-based GPP while an RMSE of 8.010 gC m-2 d-1 and an R of 0.682 can be achieved for MODIS GPP, the estimated NPP values were also well within the range of previous literature, which proved the reliability of our modelling results. This research suggested that the utilization of multi-source data with various scales would help to the establishment of an appropriate model for calculating GPP and NPP at regional scales with relatively high spatial and temporal resolution. [ABSTRACT FROM AUTHOR]

Threshold level method is well-known for drought identification with the advantage of its simplicity. However, there is no criterion for classification of drought classes in this method. Therefore, a K index based on threshold level method was proposed and verified to perform well in drought assessment in the Luanhe River basin, China. Meteorological data and remote sensing data were used to calculate the net primary productivity (NPP) of the basin by the CASA model. Results showed that the model had a good performance in comparison with the downloaded yearly remote sensing NPP and monthly PsnNet. The NPP in the basin tended to increase slowly during the year of 2000–2010, and the NPP in the downstream was generally larger than upstream. In three selected representative drought years, drought reduced NPP by about 15–25%, and during the growth season, drought may reduce NPP by about 4.6%, 2.7%, and 1.9% in July, August, and September, respectively, due to reduced precipitation. NPP on grasslands and agricultural land were more susceptible to drought than forests. The result of gray incidence analysis showed that the effects of drought on NPP had a certain delay with about 5 months. [ABSTRACT FROM AUTHOR]

The Keriya River Basin was chosen as the study site in this paper in order to investigate the suitable scales of natural and artificial oases with a certain water resource quantity. A calculation model was constructed and the suitable scales of natural and artificial oases were analyzed using remote sensing images, water resources, weather and socioeconomic data. The results showed that (1) high- and low-flow variations were apparent for the Keriya River. The mean values for the high-flow period, normal flow period and low-flow period were 9.23 × 10 m, 7.177 × 10 m and 5.741 × 10 m, respectively. (2) The ecological water demand of natural oasis in the lower reaches of the Keriya River Basin was 2.561 × 10 m. (3) In the Tarim Basin, the proportion of natural and artificial oases was 6:4. (4) In the middle reaches, after guaranteeing the unchangeable scale of artificial oases, the most suitable size of a natural oasis was 798-1,157 km. Under the reasonable proportion (i.e. proportion of 6:4 for the natural and artificial oases), the respective suitable scales of natural and artificial oases in the middle reaches were 831-1,003 km and 554-669 km. This study constructed a model for calculating the suitable scale of an oasis, verified the suitable proportions of natural and artificial oases and discussed the reasonable scales of oases in order to provide a new theoretical basis for determining reasonable development planning in similar river basins located in extremely arid regions. [ABSTRACT FROM AUTHOR]

Landslides triggered by extreme precipitation events pose a significant threat to human life and property in mountainous regions. Therefore, accurate identification of landslide locations is crucial for effective prevention and mitigation strategies. During the prolonged heavy rainfall events in Guangdong Province between 21 May and 21 June 2022, shallow and clustered landslides occurred in the mountainous regions of the Beijiang River Basin. This research used high-resolution satellite imagery and integrated the Mask R-CNN algorithm model with spectral, textural, morphological and physical characteristics of landslides in remote sensing imagery, in addition to landslide-influencing factors and other constraints, to interpret the landslides induced by the event through remote sensing techniques. The detection results show that the proposed methodology achieved a high level of accuracy in landslide identification, with a precision rate of 81.91%, a recall rate of 84.07% and an overall accuracy of 87.28%. A total of 3782 shallow landslides were detected, showing a distinct clustered distribution pattern. The performance of Mask R-CNN, Faster-CNN, U-Net and YOLOv3 models in landslide identification was further compared, and the effects of setting the rotation angle and constraints on the identification results of the Mask R-CNN algorithm model were investigated. The results show that each model improves the evaluation indices, but the Mask R-CNN model has the best detection performance; the rotation angle can effectively improve the generalization ability and robustness of the model, and the landslide-inducing factor data and texture feature sample data are the best for landslide identification. The research results provide valuable references and technical support for deepening our understanding of the distribution patterns of rainfall-triggered shallow and cluster landslides in the Beijiang River Basin. [ABSTRACT FROM AUTHOR]

In the era of big data, making full use of remote sensing images to automatically extract surface water bodies (WBs) in complex environments is extremely challenging. Due to the weak capability of existing algorithms in extracting small WBs and WB edge information from remote sensing images, we proposed a new method—Multiscale Fusion SegFormer (MF-SegFormer)—for WB extraction in the Weihe River Basin of China using Landsat 8 OLI images. The MF-SegFormer method adopts a cascading approach to fuse features output by the SegFormer encoder at multiple scales. A feature fusion (FF) module is proposed to enhance the extraction of WB edge information, while an Atrous Spatial Pyramid Pooling (ASPP) module is employed to enhance the extraction of small WBs. Furthermore, we analyzed the impact of four kinds of band combinations on WB extraction by the MF-SegFormer model, including true color composite images, false color images, true color images, and false color images enhanced by Gaussian stretch. We also compared our proposed method with several different approaches. The results suggested that false color composite images enhanced by Gaussian stretching are beneficial for extracting WBs, and the MF-SegFormer model achieves the highest accuracy across the study area with a precision of 77.6%, recall of 84.4%, F1-score of 80.9%, and mean intersection over union (mIoU) of 83.9%. In addition, we used the determination coefficient (R2) and root-mean-square error (RMSE) to evaluate the performance of river width extraction. Our extraction results in an overall R2 of 0.946 and an RMSE of 28.21 m for the mainstream width in the "Xi'an-Xianyang" section of the Weihe River. The proposed MF-SegFormer method used in this study outperformed other methods and was found to be more robust for WB extraction. [ABSTRACT FROM AUTHOR]

Due to the lower canopy height at the maximum crown width at the bottom of young Picea crassifolia trees, they are mixed with undergrowth. This makes it challenging to accurately determine crown size using CHM data or point cloud data. UAV imagery, on the other hand, incorporates rich color information and, after processing, can effectively distinguish between spruce trees and ground vegetation. In this study, the experimental site was an artificial young forest of Picea crassifolia in Shangshan Village, Qinghai Province, China. UAV images were used to obtain normalized saturation data for the sample plots. A marker-controlled watershed segmentation algorithm was employed to extract tree parameters, and the results were compared with those obtained via point cloud clustering segmentation and the marker-controlled watershed segmentation algorithm based on Canopy Height Model (CHM) images. The research results showed that the single tree recognition capabilities of the three types of data were similar, with F-measures of 0.96, 0.95, and 0.987 for the CHM image, UAV imagery, and point cloud data, respectively. The mean square errors of crown width information extracted from the UAV imagery using the marker-controlled watershed segmentation algorithm were 0.043, 0.125, and 0.046 for the three sample plots, which were better than the values of 0.103, 0.182, and 0.074 obtained from CHM data, as well as the values of 0.36, 0.461, and 0.4 obtained from the point cloud data. The point cloud data exhibited better fitting results for tree height extraction compared to the CHM images. This result indicates that UAV-acquired optical imagery has applicability in extracting individual tree feature parameters and can compensate for the deficiencies of CHM and point cloud data. [ABSTRACT FROM AUTHOR]

The glaciers in northwest China are mainly distributed in the northern part of the Himalayas, the Kunlun Mountains, and the Tianshan Mountains. Glaciers are an important freshwater resource in the northern part of the Kunlun Mountains, and the melting of glaciers and snow provides an assured source of water for rivers on the southern edge of the Tarim Basin. Based on the first glacier inventory dataset on China (1968), the second glacier inventory dataset on China (2009), and the glacier inventory dataset on Western China in 2018, this study used DEM data, Landsat remote sensing images, and ERA5 atmospheric reanalysis data to investigate glacier change and its influencing factors with respect to the northern part of the Kunlun Mountains. The results showed that there were 9273 glaciers in the northern part of the Kunlun Mountains in 2018, with an area of about 11,762.72 km2, an ice inventory of about 1168.53 km3, and an average length per glacier of about 1.68 km. The glaciers were mainly distributed at altitudes of 5300–6100 m (7574.66 km2). From 1968 to 2018, the number of glaciers in the northern stretch of the Kunlun Mountains increased by 343, while the glacier area decreased by 2452.80 km2 (−0.14%/a). From 2009 to 2018, the glacier area at the altitude of 4900 m to 6100 m decreased in the northern section of the Kunlun Mountains, and the glacier area at the remaining altitude increased slightly (10.67 km2). From 1968 to 2018, the glacier area and glacier length in all river basins decreased. The relative rate of glacier area change in the Qarqan River basin from 2009 to 2018 was five times that of 1968–2009, and this needs significant attention. From 1968 to 2018, both temperature and precipitation increased to varying degrees, and the increase in precipitation was beneficial to the accumulation of glaciers. Therefore, the increase in temperature was the main cause of glacier change in the northern section of the Kunlun Mountains. [ABSTRACT FROM AUTHOR]

A high-quality remote sensing interpretation dataset has become crucial for driving an intelligent model, i.e., deep learning (DL), to produce land-use/land-cover (LULC) products. The existing remote sensing datasets face the following issues: the current studies (1) lack object-oriented fine-grained information; (2) they cannot meet national standards; (3) they lack field surveys for labeling samples; and (4) they cannot serve for geographic engineering application directly. To address these gaps, the national-standards- and DL-oriented raster and vector benchmark dataset (RVBD) is the first to be established to map LULC for conducting soil water erosion assessment (SWEA). RVBD has the following significant innovation and contributions: (1) it is the first second-level object- and DL-oriented dataset with raster and vector data for LULC mapping; (2) its classification system conforms to the national industry standards of the Ministry of Water Resources of the People's Republic of China; (3) it has high-quality LULC interpretation accuracy assisted by field surveys rather than indoor visual interpretation; and (4) it could be applied to serve for SWEA. Our dataset is constructed as follows: (1) spatio-temporal-spectrum information is utilized to perform automatic vectorization and label LULC attributes conforming to the national standards; and (2) several remarkable DL networks (DenseNet161, HorNet, EfficientNetB7, Vision Transformer, and Swin Transformer) are chosen as the baselines to train our dataset, and five evaluation metrics are chosen to perform quantitative evaluation. Experimental results verify the reliability and effectiveness of RVBD. Each chosen network achieves a minimum overall accuracy of 0.81 and a minimum Kappa of 0.80, and Vision Transformer achieves the best classification performance with overall accuracy of 0.87 and Kappa of 0.86. It indicates that RVBD is a significant benchmark, which could lay a foundation for intelligent interpretation of relevant geographic research about SWEA in the Yangtze River Basin and promote artificial intelligence technology to enrich geographical theories and methods. [ABSTRACT FROM AUTHOR]

Irrigated agriculture is indispensable to the Lower Mekong River Basin (LMB), which ensures food security and provides livelihoods for tens of millions of people. Irrigation, agricultural production, hydropower and aquatic ecosystem health are intertwined in LMB, so it is necessary to adopt a holistic approach to analyze irrigation problems. Here, we discuss the challenges and opportunities of LMB irrigation. Bibliometric analysis is carried out to determine the characteristics and patterns of watershed irrigation literature, such as the importance of authors, affiliated institutions, and their distribution in China. Based on bibliometric analysis, research topics are determined for thematic review. Firstly, we investigated the factors that directly affect the demand and supply of irrigation water and associated crop yield impacts. Secondly, we analyzed the influence of water availability, land use and climate change on agricultural irrigation. Thirdly, we analyzed the adverse effects of improper irrigation management on the environment, such as flow pattern change, ecosystem deterioration and land subsidence caused by groundwater overexploitation. Fourthly, the time–space mismatch between water supply and demand has brought serious challenges to the comprehensive water resources management in cross-border river basins. In each specific application area, we sorted out the technologies in which remote sensing technology is used. We hope that this review will contribute to in-depth research and decision analysis of remote sensing technology in agricultural irrigation. [ABSTRACT FROM AUTHOR]

Since the initiation of economic reforms and opening up, China has witnessed an unprecedented rate of development across all sectors. However, the country has also experienced severe ecological damage, surpassing that of many other nations. The rapid economic growth has come at the expense of the environment, revealing a significant lack of coordination between urbanization and eco-environmental protection in China. Consequently, there is an urgent need for a comprehensive and continuous historical dataset of China's eco-environmental quality (EEQ) based on remote sensing, allowing for the analysis of spatial and temporal changes. Such data would provide objective, scientific, and reliable support for China's eco-environmental protection and pollution prevention policies, while addressing potential ecological risks resulting from urbanization. To achieve this, the entropy value method is employed to integrate multi-source remote sensing data and construct an evaluation system for China's EEQ. Historical data from 2000 to 2017 is plotted to illustrate China's EEQ over time. The findings of this study are as follows: (1) The entropy method effectively facilitates the construction of China's eco-environmental quality assessment system. (2) From 2000 to 2017, approximately 39.7% of China's regions witnessed a decrease in EEQ, while 60.3% exhibited improvement, indicating an overall enhancement in EEQ over the past eighteen years. (3) The Yangtze and Yellow River basins experienced improved EEQ due to China's ecological restoration projects. (4) The future EEQ in China demonstrates a subtle positive trend across diverse contexts. This study departs from conventional approaches to EEQ evaluation by leveraging the advantages of multivariate remote sensing big data, including objectivity, timeliness, and accessibility. It provides a novel perspective for future eco-environmental quality evaluation. [ABSTRACT FROM AUTHOR]

Ecological challenges resulting from soil salinization in the Tarim River Basin (TRB), exacerbated by climate change and human activities, have emphasized the need for a quick and accurate assessment of regional ecological environmental quality (EEQ) and driving mechanisms. To address this issue, this study has developed a remote-sensing ecological index with salinity adaptability (RSEISI) for EEQ assessment in the Tarim River Basin by integrating the comprehensive salinity index (CSI) into the remote-sensing ecological index (RSEI). The RSEISI enhances the sensitivity of soil salinity and characterizes the surface features of arid regions, thus expanding the applicability. Then, we used time-series analysis methods and a geodetector to quantify the spatial temporal trends and driving factors of EEQ in the TRB from 2000 to 2022. The results show that the RSEISI with salinity adaptation effectively monitors the EEQ of the TRB. The EEQ of the TRB displayed the situation of oasis expansion, desert deterioration, and glacier melting, and the multiyear average EEQ grades were dominated by medium and poor grades in desert and saline areas, while medium, good, and excellent grades were concentrated in oasis and mountainous areas. Looking at the trend of change in conjunction with land-use types, the EEQ of the TRB showed a mild degradation trend mainly in unused land, followed by a mild improvement trend in cropland and grassland. The Hurst index indicated that the EEQ of most areas of the TRB will improve in the future. Soil type, land use, precipitation, and temperature were considered to be key factors affecting the EEQ across the TRB, and changes in the EEQ were found to be the interaction of multiple factors. This study may provide innovative concepts and methodologies, scientific and technological support for ecological management, and green development models in the northwest arid zone. [ABSTRACT FROM AUTHOR]

Climatic variability and the quantification of climate change impacts on hydrological parameters are persistently uncertain. Remote sensing aids valuable information to streamflow estimations and hydrological parameter projections. However, few studies have been implemented using remote sensing and CMIP6 data embedded with hydrological modeling. This research studied how changing climate influences the hydro-climatic parameters based on the earth system models that participated in the sixth phase of the Coupled Model Intercomparison Project (CMIP6). GRACE evapotranspiration data were forced into the Soil and Water Assessment Tool (SWAT) to project hydrologic responses to future climatic conditions in the Hongshui River basin (HRB) model. A novel approach based on climate elasticity was utilized to determine the extent to which climate variability affects stream flow. CMIP6 SSPs (shared socio-economic pathways) for the second half of the 20th century (1960–2020) and 21st century (2021–2100) projected precipitation (5–16%) for the whole Hongshui River basin (HRB). The ensemble of GCMs projected an increase of 2 °C in mean temperature. The stream flow is projected to increase by 4.2% under SSP-1.26, 6.2% under SSP-2.45, 8.45% under SSP-3.70, and 9.5% under SSP-5.85, based on the average changes throughout the various long-term future scenarios. We used the climate elasticity method and found that climate change contributes 11% to streamflow variability in the Hongshui River basin (HRB). Despite the uncertainty in projected hydrological variables, most members of the modeling ensemble present encouraging findings for future methods of water resource management. [ABSTRACT FROM AUTHOR]

The Zoige wetland located in the eastern margin of the Tibet Plateau, is very important for the runoff regulation and biodiversity in the Yellow River basin. Due to the large non-uniformity of the alpine wetland surface, the use of higher spatial resolution satellite remote sensing data is essential to match or validate the results of ground observation. The land evapotranspiration in this survey was estimated by using the GF-4 satellite data from 2016 to 2020 into the Two Source Energy Balance (TSEB) model, and the estimated evapotranspiration is compared to the ground observation. The results show that: (1) the regression analysis of the actual observed evapotranspiration and the evapotranspiration estimated by the TSEB model shows that the actual evapotranspiration and the evapotranspiration calculated by the TSEB model have a good positive linear correlation (R2 > 0.8) at the Maqu observation site and the Huahu wetland observation site. Therefore, the accuracy of the evapotranspiration calculated by the TSEB model is high. (2) The spatio-temporal distribution of evapotranspiration and relevant land surface variables is generally characterized by high in the southeast and low in the northwest over the Zoige wetland basin. (3) The mean daily evapotranspiration for different GF-4 observation over the Zoige area is as follows: 1.8 mm on 8 April 2018, 3.6 mm on 20 July 2016, 1.4 mm on 29 November 2020 and 0.9 mm on 15 December 2017, and the magnitudes of evapotranspiration of the different surface are as follows: waterbody > grassland > bare land. (4) The histogram of the frequency distribution of evapotranspiration show that 3.2–4.0 mm on 20 July 2016, 0–0.8 mm on 15 December 2017, 0.8–1.6 mm on 8 April 2018 and 1.6–2.4 mm on 29 November 2020 are the maximum evapotranspiration range over the Zoige wetlands area. [ABSTRACT FROM AUTHOR]

Particle size distribution is an important characteristic of reclaimed soil in arid and semi-arid mining areas in western China, which is important in the ecological environment protection and control of the Yellow River Basin. Large-scale coal resource mining disturbances have caused serious damage to the fragile ecological environment. The timely and accurate dynamic monitoring of mining area topsoil information has practical significance for ecological restoration and management evaluation. Investigating Wuhai City in the Inner Mongolia Autonomous Region of China, this study uses Landsat8 OLI multispectral images and measured soil sample particle size data to analyze soil spectral characteristics and establish a particle size content prediction model to retrieve the particle size distribution in the study area. The experimental results and analysis demonstrate that: (1) the 6SV (Second Simulation of the Satellite Signal in the Solar Spectrum Vector version) atmospheric correction model is more accurate than the FLAASH (Fast Line-of-sight Atmospheric Analysis of Hypercubes) model in arid and semi-arid areas with undulating terrain; (2) 0–40 cm is the optimum soil thickness for modeling and predicting particle size content in this study; and (3) the multi-band prediction model is more precise than the single-band prediction model. The multi-band model's sequence of advantages and disadvantages is SVM (Support Vector Machine) > MLR (Multiple Linear Regression) > PLSR (Partial Least Squares Regression). Among them, the 6SV-SVM model has the highest precision, and the prediction precision R2 of the 3 particle sizes' contents is above 0.95, which can effectively predict the soil particle-size distribution and provide effective data to support topsoil quality change monitoring in the mine land reclamation area. [ABSTRACT FROM AUTHOR]

To overcome spatial, spectral and temporal constraints of different remote sensing products, data fusion is a good technique to improve the prediction capability of soil prediction models. However, few studies have analyzed the effects of image fusion on digital soil mapping (DSM) models. This research fused multispectral (MS) and panchromatic Landsat 8 (L8) bands, and MS Sentinel 2 (S2) and panchromatic L8 bands using the Brovey, Intensity–Hue–Saturation and Gram–Schmidt methods in an agricultural area in Yellow River Basin, China. To analyze the effects of image fusion on DSM models, various SOC prediction models derived from remote sensing image datasets were established by the random forest method. Soil salinity indices and spectral reflectance from all the remote sensing data had relatively strong negative correlations with SOC, and vegetation indices and water indices from all the remote sensing data had relatively strong positive correlations with SOC. Soil moisture and vegetation were the main controlling factors of the SOC spatial pattern in the study area. More spectral indices derived from pansharpened L8 and fused S2–L8 images by all three image fusion methods had stronger relationships with SOC compared with those from MS L8 and MS S2, respectively. All the SOC models established by pansharpened L8 and fused S2–L8 images had higher prediction accuracy than those established by MS L8 and MS S2, respectively. The fusion between S2 and L8 bands had stronger effects on enhancing the prediction accuracy of SOC models compared with the fusion between panchromatic and MS L8 bands. It is concluded that digital soil mapping and image fusion can be utilized to increase the prediction performance of SOC spatial prediction models. [ABSTRACT FROM AUTHOR]

Potential vegetation distribution is an important study in environmental sciences. We utilized the Mixed Least Squares–Total Least Squares (MLS-TLS) method and the Signal Mode Decomposition method and the Ecological Niche model to identify the inter-correlations of internal climate change factors and constructed an environmental factor response regression model. We identified the resonance periods and trend relationships among climate factors (temperature, precipitation, and evapotranspiration) and found that the evapotranspiration of the watershed interferes with the correlation between temperature and precipitation on a five-year scale. The specific change degree of extreme climate indicators in the region was quantified by the Range of Variability Approach, among which the precipitation indicators were all below 33% (low change). There were significant differences between the key bioclimatic variables and Aspect of the development of suitable vegetation habitats. The difference between the Aspect and average daily air temperature is the main contributor to the spatial distribution of vegetation, and the mutual contribution is 76.19%. Our regression model can effectively simulate the potential distribution of vegetation (r = 0.854). Compared to the MaxEnt model, our regression model can quantitatively and intuitively provide suitable habitat values for Cryptomeria fortunei at any given location in the basin. Under future scenarios (2021–2040), suitable habitat for Cryptomeria fortunei in the eastern and western regions of the basin is projected to deteriorate further. The research results can provide some help for policymakers to eliminate the potential adverse effects of future climate change on regional ecology. [ABSTRACT FROM AUTHOR]

Quantitative assessment of the effectiveness of ecological restoration provides timely feedback on restoration efforts, and helps to accurately understand the extent of restoration, while providing scientific support for optimizing restoration programs. In recent decades, the Wuliangsuhai watershed in China's Inner Mongolia Autonomous Region has been affected by anthropogenic activities, resulting in an increasingly unbalanced ecological environment. In order to curb environmental degradation, the local government implemented the "mountain, water, forest, field, lake and grass ecological protection and restoration project of the Wuliangsuhai watershed" from 2018 to 2020. The project has been completed and there is an urgent need for remote sensing monitoring to aid in performance evaluation. We took the ecological protection and restoration area of the Wuliangsuhai watershed in China as the research object, applied multi-source remote sensing imagery and auxiliary data such as meteorology and geographic basic data, extracted information of each evaluation index before and after the implementation of this project, and used the entropy value method to determine the index weights to comprehensively evaluate the ecological restoration effect. The results showed that after the implementation of the ecological restoration project, the vegetation coverage was further improved, the effectiveness of desert management was obvious, soil and water conservation capacity was strengthened, the ecosystem became more stable, and the areas with good environment were mostly located in the central and eastern parts. A total of 37.86% of the areas had obvious ecological restoration effects, and all indicators were further improved. Among the main treatment areas, the restoration effect of the Wuliangsuhai water ecological restoration and biodiversity conservation area was the best. The restoration effect will be further accentuated over time. This study provides a scientific reference for the further management of the ecological environment in the watershed and can provide a reference for the evaluation of the ecological restoration effect in similar areas in the future. [ABSTRACT FROM AUTHOR]

Soil moisture is a key parameter in hydrological research and drought management. The inversion of soil moisture based on land surface temperature (LST) and NDVI triangular feature spaces has been widely used in various studies. Remote sensing provides regional LST data with coarse spatial resolutions which are insufficient for field scale (tens of meters). In this study, we bridged the data gap by adopting a Data Mining Sharpener algorithm to downscale MODIS thermal data with Vis-NIR imagery from Sentinel-2. To evaluate the downscaling algorithm, an unmanned aerial system (UAS) equipped with a thermal sensor was used to capture the ultra-fine resolution LST at three sites in the Tang River Basin in China. The obtained fine-resolution LST data were then used to calculate the Temperature Vegetation Dryness Index (TVDI) for soil moisture monitoring. Results indicated that downscaled LST data from satellites showed spatial patterns similar to UAS-measured LST, although discrepancies still existed. Based on the fine-resolution LST data, a 10-m resolution TVDI map was generated. Significant negative correlations were observed between the TVDI and in-situ soil moisture measurements (Pearson's r of − 0.67 and − 0.71 ). Overall, the fine-resolution TVDI derived from the downscaled LST has a high potential for capturing spatial soil moisture variation. [ABSTRACT FROM AUTHOR]

Lakes are an important part of the Earth's surface. Poyang Lake is China's largest freshwater lake with complex hydrological relationships and unique rhythmic changes. There is no systematic understanding of the relationship between lake watershed area and river–lake connectivity. In this study, using remote sensing imagery, hydrological data, meteorological data, and land use surveys, we investigated changes in the Poyang Lake water area from 1977–2021. In addition, we examined the relationship between the lake water area and potential impact factors and analyzed the influence of river–lake interactions on the lake water area. The results showed that the water area of Poyang Lake decreased during the study period. River runoff and sediment load changes in the Poyang Lake basin are the main factors affecting the lake water area. Additionally, the Three Gorges Dam water impoundment situated in the upper reaches of the Yangtze River weakened and eliminated the block and reverse flow of the Yangtze River into the Poyang Lake, causing the water area to decrease toward the end of the flood season in 2005 and in the dry season in 2003. Changes in the lakebed elevation caused by sedimentation and anthropogenic activities have also accelerated the shrinkage of the water area. Overall, the results of this study indicate that variations in the river–lake interactions are the primary cause of the significant changes observed in the Poyang Lake water area in the last five decades. [ABSTRACT FROM AUTHOR]

Due to the combined effects of the natural environment, climate change and human activities, profound changes have occurred in terms of the eco-environmental effects of land use/cover change (LUCC) in the Baiyangdian basin. Therefore, based on land remote sensing monitoring data from 2000 to 2020, the Eco-environmental Quality Index (EQI) was introduced in this study to measure the eco-environmental effects of land use change in the Baiyangdian basin. Subsequently, the GeoDetector model was applied to detect the formation mechanism of the eco-environmental effects in the Baiyangdian basin from 2000 to 2020. The results of the study showed that cropland, woodland and grassland were the most widely distributed land use types in the Baiyangdian basin. The area of cropland declined the most and was mostly converted to construction land. The EQI increased slightly during the study period. The eco-environment of the mountainous areas in the western part of the basin and in Baiyangdian Lake was better than that of other areas. Land use intensity had a significantly stronger influence on the quality of the eco-environment than other factors. The interaction between the influencing factors was mainly a non-linear enhancement and a two-factor enhancement, with non-linear enhancement dominating. [ABSTRACT FROM AUTHOR]

This paper explores how human disturbance and hydrologic elements affect the spatial distribution pattern of plant diversity in the watershed, taking Shamu watershed in the World Natural Heritage Site as a case study. Spatial analysis of multisource remote sensing and plant diversity plots data were conducted using linear mixed effects models and structural equation models. Results revealed that the distribution of plant diversity in the watershed is mainly affected by human disturbance. However, under similar human disturbance levels, hydrologic elements also affect the plant diversity within the watershed. The topographic undulation and surface runoff significantly promote plant diversity, while the river network density, the watershed shape factor, the river longitudinal gradient do not. The influence of topographic undulation is more obvious than that of runoff on plant diversity, but the effect of topographic undulation and runoff on plant diversity is getting weaker from upstream to downstream within the watershed. In addition, the impact of hydrologic elements on plant diversity is mainly regulated by environmental factors Pre and Tem. The findings clarify how human disturbance and hydrologic elements affect plant diversity distribution within the watershed, optimizing the conservation theory of plant diversity resources and scientifically guiding the region's sustainable development. • Human disturbance contributes most to plant diversity. • Surface runoff and topographic undulation promote plant diversity. • Precipitation and temperature regulate the hydrological impact on plant diversity. • Weaker restrictions of hydrologic elements on plant diversity in downstream. [ABSTRACT FROM AUTHOR]

The accurate quantification of evapotranspiration (ET) is critical to the sustainable management of irrigated agriculture. In this study, we proposed a remote sensing data fusion method for predicting ET, coupling a surface energy balance system model with an enhanced spatial and temporal adaptive reflectance fusion model utilizing remote sensing inversion with satellite data from Landsat and MODIS. The method was tested for a case study with cotton fields under film-mulched drip irrigation (FMDI) in the Manas River Basin. Areas under FMDI were identified, and ET patterns were evaluated for a 21-year period spanning from 2000 to 2020. A field experiment, a regional survey, and data retrieved from the literature provided results demonstrating that the method allowed reliable estimation of ET distribution with simultaneously, relatively high spatial and temporal resolutions at both field and regional scales. ET was found to decline from upstream to downstream in the basin, with the difference gradually diminishing over time. Supported by the promotion of FMDI technology, the area under cotton production in the basin increased by an average of 4.9% annually. Limited irrigation quotas to farmers and, therefore, water application per area led to a decreasing ratio of relative water supply for potential ET and, thus, to a reduction in average actual ET of 7.5 mm year−1. The average ET in the basin declined to about 415.7 mm in 2020, 287.2 mm lower than its water demand. The dynamics of fused ET provide a reliable scientific basis for agricultural water resources planning and management and for the sustainable utilization of water and soil resources in the basin. The method, with simultaneously high temporal and spatial resolutions, should have both local and global practical potential. [ABSTRACT FROM AUTHOR]

Runoff estimations play an important role in water resource planning and management. Many accomplishments have been made in runoff estimations based on data recorded at meteorological stations; however, the advantages of the use of remotely sensed data in estimating runoff in watersheds for which data are lacking remain to be investigated. In this study, the MOD13A2 normalized difference vegetation index (NDVI), TRMM3B43 precipitation (P), MOD11A2 land–surface temperature (LST), MOD16A2 evapotranspiration (ET) and hydrological station data were used as data sources with which to estimate the monthly runoff through the application of a fully connected long short–term memory (LSTM) model in the upstream reach of the Heihe River basin in China from 2001 to 2016. The results showed that inputting multiple remote sensing parameters improved the quality of runoff estimation compared to the use of rain gauge observations; an increase in R2 from 0.91 to 0.94 was observed from the implementation of this process, and Nash–Sutcliffe efficiency (NSE) showed an improvement from 0.89 to 0.93. The incorporation of rain gauge data as well as satellite data provided a slight improvement in estimating runoff with a respective R2 value of 0.95 and NSE value of 0.94. This indicates that the LSTM model based on remote sensing data has great potential for runoff estimation, and data obtained by remote sensing technology provide an alternative approach for estimating runoff in areas for which available data are lacking. [ABSTRACT FROM AUTHOR]

The recent development of the non-invasive cosmic-ray soil moisture sensing technique fills the gap between point-scale soil moisture measurements and regional-scale soil moisture measurements by remote sensing. A cosmic-ray probe measures soil moisture for a footprint with a diameter of ~ 600m (at sea level) and with an effective measurement depth between 12 and 76 cm, depending on the soil humidity. In this study, it was tested whether neutron counts also allow correcting for a systematic error in the model forcings. A lack of water management data often causes systematic input errors to land surface models. Here, the assimilation procedure was tested for an irrigated corn field (Heihe Watershed Allied Telemetry Experimental Research--HiWATER, 2012) where no irrigation data were available as model input although for the area a significant amount of water was irrigated. In the study, the measured cosmic-ray neutron counts and Moderate-Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products were jointly assimilated into the Community Land Model (CLM) with the local ensemble transform Kalman filter. Different data assimilation scenarios were evaluated, with assimilation of LST and/or cosmic-ray neutron counts, and possibly parameter estimation of leaf area index (LAI). The results show that the direct assimilation of cosmic-ray neutron counts can improve the soil moisture and evapotranspiration (ET) estimation significantly, correcting for lack of information on irrigation amounts. The joint assimilation of neutron counts and LST could improve further the ET estimation, but the information content of neutron counts exceeded the one of LST. Additional improvement was achieved by calibrating LAI, which after calibration was also closer to independent field measurements. It was concluded that assimilation of neutron counts was useful for ET and soil moisture estimation even if the model has a systematic bias like neglecting irrigation. However, also the assimilation of LST helped to correct the systematic model bias introduced by neglecting irrigation and LST could be used to update soil moisture with state augmentation. [ABSTRACT FROM AUTHOR]

The use of remote sensing to monitor surface water bodies has gradually matured. Long-term serial water change analysis and floods monitoring are currently research hotspots of remote sensing hydrology. However, these studies are also faced with some problems, such as coarse temporal or spatial resolution of some remote sensing data. In general, flood monitoring requires high temporal resolution, and small-scale surface water extraction requires high spatial resolution. The machine learning method has been proven to be effective against long-term serial surface water extraction, such as random forests (RFs). MODIS data are well suited for large-scale surface water dynamic analysis and flood monitoring because of its short return cycle and medium spatial resolution. In this paper, the Yangtze River Basin (YRB) in China was selected as the study area, and two MODIS products (MOD09A1 and MOD13Q1) and RF method were used to extract the surface water from 2000 to 2016. Considering the disadvantages of temporal or spatial resolution of these two MODIS products, this study also presents a data fusion method to combine them and get higher spatiotemporal resolution water results. Finally, 762 surface water maps from 2000 to 2016 are obtained, whose temporal and spatial resolution is every eight days and 250 m, respectively. In addition, water extent variation is analyzed and compared to observed precipitation data. The main conclusions are as follows: (1) this constructed approach for long-term serial surface water extraction based on the RF classifier is feasible, and a good fusion method is used to obtain the surface water body with higher spatiotemporal resolution; (2) the maximum area of the surface water extent is 48.53 × 103 km2, and seasonal and permanent water areas are 20.51 × 103 km2 and 28.01 × 103 km2, respectively; (3) surface water area is increasing in the YRB, such that seasonal water area decreased by 3450 km2, and the permanent water area increased by 3565 km2 in 2001–2015; (4) precipitation is the main factor causing variation in the surface water bodies, and they both show an increasing trend in 2000–2016. As such, the approach is worth referring to other remote sensing applications, and these products are very both valuable for water resource management and flood monitoring in the study area. [ABSTRACT FROM AUTHOR]

Observation schedules depend upon the accurate understanding of a single sensor’s observation capability and the interrelated observation capability information on multiple sensors. The general ontologies for sensors and observations are abundant. However, few observation capability ontologies for satellite sensors are available, and no study has described the dynamic associations among the observation capabilities of multiple sensors used for integrated observational planning. This limitation results in a failure to realize effective sensor selection. This paper develops a sensor observation capability association (SOCA) ontology model that is resolved around the task-sensor-observation capability (TSOC) ontology pattern. The pattern is developed considering the stimulus-sensor-observation (SSO) ontology design pattern, which focuses on facilitating sensor selection for one observation task. The core aim of the SOCA ontology model is to achieve an observation capability semantic association. A prototype system called SemOCAssociation was developed, and an experiment was conducted for flood observations in the Jinsha River basin in China. The results of this experiment verified that the SOCA ontology based association method can help sensor planners intuitively and accurately make evidence-based sensor selection decisions for a given flood observation task, which facilitates efficient and effective observational planning for flood satellite sensors. [ABSTRACT FROM AUTHOR]

It is known that evapotranspiration (ET) differs before and after vegetation change in watersheds. However, impacts of vegetation change on ET remain incompletely understood. In this paper, we investigated the process-specific, nonclimatic contribution (mainly vegetation coverage changes) to ET at grid, sub-basin, and basin scales using observation and remote sensing data. The Poyang Lake Basin was selected as the study area, which experienced a fast vegetation restoration from 1983 to 2014. Our results showed that vegetation cover change produced contrasting effects on annual ET in magnitude and direction during shifts from a less covered to a more covered stage. At the early stage (1983–1990), with vegetation cover of 30%, vegetation cover change produced negative effects on ET over the basin. At the middle stage (1990–2000), the vegetation coverage increased at a fast pace and the negative effects gradually shifted to positive. At the late stage (2000–2014), the vegetation coverage remained high (over 60%) and maintained a positive relationship with ET. In summary, the vegetation effects are collaboratively influenced by both vegetation coverage and its change rate. Our findings should be helpful for a comprehensive understanding of complicated hydrological responses to anthropogenic revegetation. [ABSTRACT FROM AUTHOR]

Revegetation programmes aim to avoid land degradation, control soil erosion, reduce floods, and improve ecological conditions. China has planted billions of trees over the past 20 years. However, little is known about the effectiveness of this artificial revegetation and its consequences on China's national conservation policies and changes in biophysical factors at the county level. Here we use satellite time series data and develop a new metric, the revegetation index (RVI), that quickly monitors revegetation and its long‐term changes. We found that the number of regions where the Grain to Green Program (GTGP) was successfully implemented was only about 55.8% of the areas of GTGP implementation. Surprisingly, we provide observational evidence that the implementation of the GTGP can decrease soil erosion and drought, and increase water availability, but revegetation in the Yangtze River basin (YRB) in China has a limited impact on soil erosion, water, and drought. In particular, for revegetation between 1999 and 2016, indicating that a previous assessment by the Government revealed as official statistics may have overestimated the effects of the ecological restoration practices. [ABSTRACT FROM AUTHOR]

It is of great significance for the coordinated development of the environment and the economy to study the impact of the human driving factors of land use change (LUC) on ecosystem service value (ESV). In this study, we combined the biomass and remote sensing data of the Lijiang River Basin (LRB), which is a typical karst basin with a fragile ecological environment, to establish an ESV model to calculate the ESV. We also introduced the Lorenz curve and Gini coefficient to further analyze the impacts of the human driving factors of LUC on ESV. The results show that (1) the ESV in the LRB from 1995 to 2015 decreased from 8640.03 million yuan to 8595.38 million yuan, with a total decrease of 44.65 million yuan, indicating that the overall ESV in the region has a decreasing trend; (2) the obvious changes in land use caused a significant loss in ESV and changes in the structure of ecosystem services; and (3) the human driving factors of the total population, GDP, and urbanization rate are inversely related to the ESV in the LRB. [ABSTRACT FROM AUTHOR]

Drought and flood are investigated in the Pearl River basin (PRB) using long-term terrestrial water storage anomaly (TWSA) data from the mascon (mass concentration) solutions based on Gravity Recovery and Climate Experiment (GRACE) satellite measurements (2002–19) and reanalysis data (1980–2019). The GRACE mascon solutions capture two major drought periods (2003–06 and 2009–12) with similar onsets and endings over the last two decades, but show considerable differences in quantifying total drought severity. The reanalysis data significantly overestimate drought duration and severity during 1980–2000 owing to overestimated negative TWSA forced by underestimated precipitation. The GRACE mascon solutions identify four major flood events in August 2002, June 2008, and July in 2006 and 2019. The flood potential is influenced by the precipitation in both the current and antecedent months. The flood potential index of the most recent flood in 2008 showed a similar spatial pattern compared to precipitation at monthly and subbasin scales. The precipitation and TWSA in the PRB are mainly influenced by El Niño–Southern Oscillation (ENSO). TWSA exhibits a lag of 1–3 months responding to ENSO during 1980–2019. This study emphasizes the significance of removing water storage changes in new large reservoirs before long-term drought and flood characterization. The inclusion of reservoir water storage would expand (shrink) the drought duration and overestimate (underestimate) drought severity for the period before (after) reservoir impoundment and overestimate flood potential for the period after reservoir impoundment. This study highlights the intensifying drought conditions in the PRB over the last four decades under the circumstances of more frequent human activities (reservoir construction and regulation) and the complex changing climate system. [ABSTRACT FROM AUTHOR]

Droughts have caused many damages in many countries and might be aggravated around the world. Therefore, it is urgent to predict and monitor drought accurately. Soil moisture and its corresponding drought index (e.g., soil water deficit index, SWDI) are the key variables to define drought. However, in situ soil moisture observations are inaccessible in many areas. This study applies support vector machine (SVM) by using a new set of inputs to investigate the performance of in situ and remote sensing products (CMORPH-CRT, IMERG V05 and TRMM 3B42V7) for soil moisture and SWDI forecast over the Xiang River Basin. This study also assesses whether the addition of remote sensing soil moisture as input can improve the performance of SWDI prediction. The results are as follows: (1) the new set of inputs is suitable for drought prediction based on SVM; (2) using in situ precipitation as input to SVM shows the best performance for soil moisture prediction, which followed by TRMM 3B42V7, IMERG V05 and CMORPH-CRT; (3) in situ precipitation and IMERG V05 as input are more suitable for indirect SWDI prediction, while CMORPH-CRT and TRMM 3B42V7 are more suitable for direct SWDI prediction; (4) the addition of soil moisture with in situ precipitation or CMORPH-CRT both can improve the performance of direct SWDI prediction; (5) the lead time for drought prediction with SVM over the Xiang River Basin is about 2 weeks. [ABSTRACT FROM AUTHOR]

This study investigated the attribution of terrestrial water storage (TWS) variations across China to changes in groundwater and human water use. As one vital storage component, the groundwater storage (GWS) derived from the Jet Propulsion Laboratory's GRACE (Gravity Recovery and Climate Experiment) mass concentration solution compared reasonably well with the in situ groundwater table depth, with the correlation coefficients ranging from −0.83 to −0.18, all of which were statistically significant at the 95% confidence level. About 71% of the trends in derived GWS had the same sign as those of observations, without systematic deviation, across China. The GWS variation contributed a large portion of the TWS trend in most regions of China, and the majority of contribution values reached 50%–150% in the Hai River basin, the Loess Plateau, and the middle portion of the Yangtze River basin. The dominant role of GWS is closely related to the detected long-term "memories" in both TWS and GWS. The increase of irrigation consumption accelerated the TWS depletion trend by 13.4% in the Huai River basin, while the decrease of consumptive agricultural water use alleviated the TWS decline rate by 4.1% in the Hai River basin. Importantly, the correlation coefficients reached 0.74–0.95 between the TWS change and the residual of precipitation, evapotranspiration, flow into the sea, and irrigation consumption in the four river basins of particular interest. The findings of this study are helpful for understanding regional water cycles in China. [ABSTRACT FROM AUTHOR]