The salinization of freshwater lakes by agricultural activities poses a threat to many lake ecosystems around the world. Quantitative, medium- to long-term studies are needed to understand how some common agricultural practices, such as the discharge of crop irrigation in the vicinities of large lakes, may affect lake salinization. In this study, hydrological, hydrodynamics, water quality and meteorological datasets were used to analyze the long-term spatial-temporal variations of water salinities of a major lake, the Chagan Lake, in Northeast China. An integrated hydrodynamics-salinity model was used to simulate lake water salinity changes taking place at different times and locations, including (i) salt accumulations during a non-frozen period, and (ii) the time when water salinity may reach a significant threshold (1 psu) that jeopardizes a major environmental and economic value of this lake (i.e., the cultivation of local fish species). The results confirmed that Chagan Lake was indeed undergoing salinization in the ten year period between 2008 and 2018. The spatial-temporal patterns of the salinization processes were identified. For instance, (i) the mean salinity of the lake water was found to be 0.55 psu in the summer season of the region and 0.53 psu in the winter, and (ii) between May to October the salinity was up to 0.62 psu in the western region of the lake. The rate of salt accumulation was found to be 97 ton per annum during the non-frozen period. The simulation predicted that by 2024 the lake water will become sub-saline (salinity > 1.07 psu) which is toxic to fish species, if the current practice of irrigation discharge into the lake continues. In the scenario that the amount of irrigation discharges into the lake doubles, the western region of the lake will become sub-saline within one year, and then the whole lake within three years. Overall, this study has produced results that are useful to authorities around the world, for balancing the risks and benefits of developing crop irrigation fields in areas surrounding large freshwater lakes.

Spatiotemporal changes in the surface area of inland water bodies have important implications in regional water resources, flood control, and drought hazard prediction. Although inland water bodies have been investigated intensively, few studies have looked at the effect of human activities and climate variability on surface area of inland waters at a larger scale over time and space. In this study, we used MODIS (MOD13Q1) images to determine water surface area extent at 250 m spatial resolution. We then applied this algorithm with MOD13Q1 images taken at 16-day intervals from 2000 to 2018 to a large river basin in China’s northeast high latitude region with dense stream network and abundant wetlands to investigate spatiotemporal distribution and dynamics of inland water bodies. The study identified 209 ponds, lakes, and reservoirs with an average total surface area of 2080 km² in the past 19 years. The total water surface area fluctuated largely from 942 km² to 5169 km², corresponding to rainfall intensity and flood. We found that the total water surface area in this high latitude river basin showed an increasing trend during the study period, while the annual precipitation amount in the river basin also had an increasing trend concurrently. Precipitation and irrigation significantly contributed to the monthly change of water surface area, which reached the highest during June and August. The increase of water surface area was significant in the lower basin floodplain region, where agricultural irrigation using groundwater for rice production has progressed. Four nationally important wetland preserves (Zhalong, Xianghai, Momoge, and Chagan Lake) in the river basin made up nearly 50% of the basin’s total water surface area, of which Zhalong, Xianghai, and Momoge are designated by The Ramsar Convention as wetland sites of international importance. Seasonally, these water bodies reached their maximal surface area in August, when both the monsoon weather and agricultural discharge prevailed. This study demonstrates that water surface area in a high latitude river basin is affected by both human activities and climate variation, implying that high latitude regions will likely experience more changes in surface water distribution as global climate change continues and agriculture becomes intensified.

Soil nitrogen (N) content plays a vital role in agriculture and biogeochemical processes, ranging from the N fertilization management for intensive agricultural production to the patterns of N cycling in agroecological systems. While proximal sensing in laboratory settings can achieve ideal soil N estimation accuracy, the estimation and mapping by using remote sensing methods in a large spatial scale diplays low ability. A new hyperspectral imager with 166 spectral channels, the ZY1-02D, makes possible the detection of subtle but important spectral features of soil. This study aimed at exploring the capability of the ZY1-02D to estimate and map the topsoil N content of the black soil-covered farmlands in northeast China. To this aim, 646 soil samples from study sites were collected, processed, spectrally and geochemically measured for the soil N sensitive bands detection and partial least squares regression (PLSR) calibration and validation. The sensitive bands detection results showed an appealing regularity of the variability and stable tendency of the soil N sensitive spectral bands with the change of the sample size. Based on this, we compared the estimation capacity of the models developed with the full wavelength spectra and the models developed with the sensitive bands. The estimation based on ZY1-02D full wavelength spectral reflectance were robust, with R² of 0.64 in validation. Further, the results of model developed with the sensitive bands showed better validation accuracy with R² of 0.66 and were applied to create a map of topsoil N content of farmlands in the northeast China black soil area. The results demonstrated that sensitive bands modelling could enhance the accuracy of the estimation and simplify model, and what is more, showed the ideal capability of ZY1-02D for soil N content estimation at the regional scale.

Ecological water replenishment (EWR) has been increasingly applied to the restoration and maintenance of wetland hydrological conditions across China since the beginning of the 21st century. However, little is known about whether EWR projects help protect and/or restore wetland ecohydrology. As one of the earliest and longest-running EWR projects in China, water has been released from the Nenjiang River into the Zhalong wetland since 2001. It is important to examine the ecohydrological effects of this EWR project. In this study, long time series remote sensing data were used to extract the water area, inundation frequency, and normalized difference vegetation index (NDVI) to explore how eco-hydrological conditions changed during the pre- (1984–2000) and post-EWR (2001–2018) periods in the Zhalong wetland. Results show that the inundation area decreased due to the reduced surface water inflow during the pre-EWR period. Similarly, monthly vegetation NDVI in the growing season generally exhibited a decreasing and an increasing trend during the pre- and post-EWR periods, respectively. In the post-EWR period, NDVI increased by 19%, 73%, 45%, 28%, 13% for the months of May through September, respectively. Due to EWR, vegetation growth in areas with low inundation frequency was better than in areas with high inundation frequency. We found that the EWR project, runoff, and precipitation contributed 25%, 11%, and 64% to changes in the NDVI, respectively, and 46%, 37%, and 17% to changes in inundation area, respectively. These results indicate that the EWR project has improved hydrological conditions in the Zhalong wetland. For further maximum benefits of EWR in the Zhalong wetlands, we suggest that implementing similar eco-hydrological projects in the future should focus on flood pulse management to increase the inundation area, improve hydrological connectivity, and create new habitats.

A thorough understanding of the freshwater ice process received considerable critical attention due to increasing winter recreations and ice engineering. The development of the lake ice process of Chagan Lake was monitored using MODIS and Landsat images over eight consecutive snow seasons from October 2013 to April 2021. We derived the lake ice phenology from an eight-day time series of lake water skin temperature (LWST) provided by MODIS, including freeze-up date, break-up date, and ice cover duration. We discovered a large-scale fracture extending from northwest to southeast that repeatedly appeared on Landsat images since 1986. A novel fractal-based auto-extraction is proposed to extract the length and angle of these fractures. We also carried out a field campaign and an ice ridge was found at the southernmost part of what we observed from the images. Moreover, we explained the fracturing development by thermal changes, wind in lake, and underlying flow. Results show that the lake ice fracture is nearly perpendicular to the dominant wind direction in the cold season, which indicates the crucial role of wind on lake ice fracturing.

Excessive nitrogen (N) and phosphorus (P) input resulting from anthropogenic activities seriously threatens the supply security of drinking water sources. Assessing nutrient input and export as well as retention risks is critical to ensuring the quality and safety of drinking water sources. Conventional balance methods for nutrient estimation rely on statistical data and a huge number of estimation coefficients, which introduces uncertainty into the model results. This study aimed to propose a convenient, reliable, and accurate nutrient prediction model to evaluate the potential nutrient retention risks of drinking water sources and reduce the uncertainty inherent in the traditional balance model. The spatial distribution of pollutants was characterized using time-series satellite images. By embedding human activity indicators, machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), and Multiple Linear Regression (MLR), were constructed to estimate the input and export of nutrients. We demonstrated the proposed model’s potential using a case study in the Yanghe Reservoir Basin in the North China Plain. The results indicate that the area information concerning pollution source types was effectively established based on a multi-temporal fusion method and the RF classification algorithm, and the overall classification low-end accuracy was 92%. The SVM model was found to be the best in terms of predicting nutrient input and export. The determination coefficient (R²) and Root Mean Square Error (RMSE) of N input, P input, N export, and P export were 0.95, 0.94, 0.91, and 0.93, respectively, and 32.75, 5.18, 1.45, and 0.18, respectively. The low export ratios (2.8–3.0% and 1.1–2.2%) of N and P, the ratio of export to input, further confirmed that more than 97% and 98% of N and P, respectively, were retained in the watershed, which poses a pollution risk to the soil and the quality of drinking water sources. This nutrient prediction model is able to improve the accuracy of non-point source pollution risk assessment and provide useful information for water environment management in drinking water source regions.