Research and Application of a Precise Identification Method for Waterlogged Farmland through Multi-Source Data Integration

Zinc amendment increases the yield and industrial quality of Beta vulgaris L. cultivated in Northeast China

The accurate evaluation method of LID toward the attenuation of urban flood is still a hot issue. This paper focuses on a coupled 1D and 2D hydrodynamic model, investigating the model parameters set in a collapsible loess area, and the changes in the surface runoff, waterlogged area, and drainage network indicators under different rainfall patterns. The results show that the coupled model can effectively simulate the effect of LID facilities under unaltered and retrofitted conditions. It is found that the infiltration parameters in a collapsible loess area are higher than in other eastern cities by calibration and validation. After implementing the LID facilities, the total runoff, peak flood flow, waterlogged area, runoff coefficient, and drainage pressure under different rainfall patterns have all been reduced. With the increases in the rainfall return period, the waterlogging reduction effect of LID facilities would gradually weaken. The rainfall return period has a great impact on the indicators of surface runoff, waterlogged area, and drainage capacity. The coefficient of rainfall peak has a relatively big impact on indicators of pipelines, such as the proportion of overflow nodes, the proportion of fully loaded pipelines, and the average full-load duration. The rainfall duration has a major impact on the total runoff quantity, runoff coefficient, and average full-load duration.

An integrated approach using remote sensing (RS) and geographical information system (GIS) and groundwater modelling (GM) has been used for waterlogged areas identification in the Rohtak district of Haryana State, India. Surface waterlogged areas were delineated using optical remote sensing satellite data-based normalized difference water index (NDWI) technique. Sentinel 2 MSS (optical data) images pertaining to pre-monsoon and post-monsoon seasons were acquired and processed to extract water pixels. To overcome the limitation of false positives and cloud penetration associated with optical images, water pixels were also extracted using synthetic aperture radar (SAR) images of Sentinel 1A. Thresholding of NDWI for optical images and sigma naught for SAR images was done using the respective histograms to distinguish water and terrestrial features. Surface waterlogged areas were delineated from the generated combined water body raster based on the visual interpretation technique in a GIS environment. Sub-surface waterlogging conditions were simulated for the same year using process-based groundwater model MODFLOW. A conceptual model was created to simulate groundwater system based on flow processes, hydrogeological characteristics, aquifer hydraulic properties, source and sinks and boundary conditions. Sub-surface waterlogged areas were delineated based on depth to water table classification criteria. It was found that surface waterlogged area varies from 0.55 to 1.0% of the district area from pre-monsoon to post-monsoon season, while the percentage of sub-surface waterlogged areas was higher, varying from 9.1 to 21.6%, respectively. Statistics also shows that most of the surface waterlogged areas do not fall under the areas plagued by sub-surface waterlogging.

The investigation identifies the geographic focus on the smart city of Bhubaneswar, Odisha, India, within the timeframe (2000–2024). The study identifies seasonal trends, extreme events, and their impacts on climate and human life. The maps are prepared using Python, like scatter plots of mean monthly and yearly rainfall (line, box, bar, and pie charts, etc.). Further, GIS-based maps prepared are the annual Rainfall distribution map of Odisha, the choropleth map for temperature distribution, slope map, relief and contour map of Bhubaneswar smart city, highlighting spatial impacts in Bhubaneswar zones (e.g., Bomi Khal, Naya Palli), etc. The slope, the contour and the population. Slum pockets maps have been prepared from the data of the Government of Odisha, NIC data and Digital Elevation Model. The analysis, supported by Python libraries (pandas, matplotlib, seaborn, geopandas, folium). The study identifies seasonal trends, extreme events and their impacts on climate and human life, focusing on urban flooding, infrastructure, health, agriculture and environmental degradation, with a geoinformatics approach. Recommendations include GIS-driven drainage mapping, rainwater harvesting, resource management, and climate-smart planning, with future research proposed for long-term trends and geospatial adaptation strategies. The extreme events of rainfall cause waterlogged areas, urban flooding and fatalities due to heat strokes. The study discusses the impacts on climate and human life and supports the town planners and developers in climate-resilient planning, allied with SDGs 11 and 13 for sustainable urban dwellings, developing a zero slums concept in the sustainable smart city, Bhubaneswar.

Hydraulic correction method (HCM) to enhance the efficiency of SRTM DEM in flood modeling

Soil microbial communities are affected more by land use than seasonal variation in restored grassland and cultivated Mollisols in Northeast China

Abstract. Digital Elevation Models (DEMs) are fundamental for ensuring the geolocation accuracy of Synthetic Aperture Radar (SAR) imagery, as vertical errors in elevation data propagate into horizontal displacements. This study examines the impact of DEM accuracy on the geolocation performance of Sentinel-1 Ground Range Detected (GRD) products over an urban area in Ankara, Türkiye. A comparative analysis was conducted using four global DEMs (SRTM 1″, SRTM 3″, AW3D30, Copernicus 30 m), a high- resolution national DEM (YÜKPAF), and two interferometrically derived DEMs generated from Sentinel-1 Single Look Complex (SLC) pairs. Vertical accuracy was validated against ICESat-2 ATL08 reference elevations, with Copernicus (2.2 m RMSE) and YÜKPAF (3.2 m RMSE) providing the highest reliability, while the InSAR-derived DEMs showed larger errors (>12 m) due to coherence loss and phase unwrapping inconsistencies. Horizontal accuracy was evaluated using Ground Control Points (GCPs) obtained from HGM-Küre. The results demonstrated that high-quality DEMs, particularly YÜKPAF, achieved the lowest horizontal RMSE (8.2 m), whereas InSAR-based DEMs produced the largest errors, approaching 15 m. Despite these variations, all orthorectified outputs remained below 15 m geolocation error, owing to the precise orbital information of Sentinel-1 and the flat topography of the study area. Overall, the study confirms that DEM quality is a decisive factor for SAR geolocation accuracy and offers practical guidance for dataset selection in operational SAR orthorectification workflows.

Zou, W., Si, B., Han, X. and Jiang, H. 2012. The effect of long-term fertilization on soil water storage and water deficit in the Black Soil Zone in northeast China. Can. J. Soil Sci. 92: 439-448. The Black Soil Zone in northeast China is one of the most important areas of agricultural production in China and plays a crucial role in food supply. However, further improvement in crop yield hinges on effective management of soil water. There is a poor understanding of how different fertilization methods affect crop water use efficiency. The objective of this study was to examine the effect of different fertilization methods on soil water storage and deficit in Black soils. A long-term experiment was conducted at the National Field Research Station of Agro-ecosystems, at Hailun County, Heilongjiang province in northeastern China from 1999 to 2008. Three fertilizer treatments including no fertilizer (CK), inorganic fertilizer (NP) and inorganic fertilizer plus organic material (NPM) were tested. The results showed that soil water storage decreased in the order CK, NP, and NPM during the growing season and the differences in soil water storage in the active root zone (0-70 cm) and below the active root zone (70-130 cm) and soil water deficit were statistically significant among the three treatments. Due to the uneven temporal distribution of rainfall and crop water uptake, soil water content was very dynamic in all three treatments: The low soil water storage and resulting soil water deficit (defined as the monthly difference between potential evapotranspiration and soil available water storage) within the 0- to 70-cm soil profile were found in both June and July. Further, soil receiving NPM was more likely to have a soil water deficit, but less likely to have excessive water. A lower risk of excess water may result in deeper root penetration and increased water use at greater depth, and thus the water deficit under the NPM treatment may not be the limiting factor for crop production. Therefore, NPM seems a viable management practice for improving crop yields in the Black Soil Zone in northeast China, possibly due to higher soil organic carbon and nutrient supply and lower probability of excess water.

Based on the global Palmer drought severity index (PDSI) data and monthly precipitation data from China, using linear trend analysis, comparative analysis and EOF methods, the spatiotemporal evolution of the droughts and floods over China during 1961–2010 has been analyzed. Results show that: The decadal spatial distribution of PDSI in spring is similar to that in year-round. The regional differences of PDSI in summer and autumn are obvious, especially in autumn. According to the linear analysis, climate in North China, Northeast China, Southwest China, South China and East China is becoming dry obviously; while the west of Northwest China is becoming wet. The PDSI in summer from 1961 to 2010 is also analyzed by experience orthogonal function (EOF). Results show that droughts and floods occur frequently in China since the 1980s and the intensity of droughts and floods is also increased, especially droughts in the North China, Southwest China, Northeast China and floods in East China.

Quantifying the effect of soil physical properties on the compressive characteristics of two arable soils using uniaxial compression tests

Due to the basic topographical characteristics of the gentle and long slope lengths in the Mollisol region of Northeast China, severe soil erosion is easily aggravated by the concentration of surface flow. The spatial distribution of water depth and hydrological connectivity index were introduced to evaluate the effects of typical soil and water conservation practices on the overland flow path and hydrological connectivity based on the GIS and SIMWE (SIMulated Water Erosion) model. We analyzed the effects of different soil and water conservation practices on the hydrological connectivity, water flow path, and spatial distribution of soil erosion and sediment yield by quantifying the variations of soil infiltration rate and surface manning roughness, as well as by constructing an artificial terrain digital elevation model (DEM). The results showed that: 1) terraces could effectively affect the hydrological connectivity of the slope and regulate flow path, with significant differences between the responses of hydrological connectivity and flow path under different forms of terraced fields and ridges. The characteristics of spatial distribution of soil erosion and sediment yield varied with changes in water flow path, which would eventually lead to the intensification of local erosion; 2) practices of vegetated buffer strips and contour tillage presented limited effectiveness on runoff path controlling, though they played a significant role in sediment retention; and 3) conservation tillage could reduce the hydrological connectivity and improve the retention capacity of runoff by increasing surface roughness. This study quantified the effects of different soil and water conservation practices on the hydrological connectivity, flow path, and spatial distribution of soil erosion and sediment yield, and could provide a theoretical reference for scientific layout of soil and water conservation practices in black soil region.

Coupling the Xinanjiang model with geomorphologic instantaneous unit hydrograph for flood forecasting in northeast China

IntroductionThe Northeast black soil area is an important marketable grain base in China. However, due to soil erosion, the black soil layer has been gradually thinning and its quality deteriorating. Therefore, accurately assessing the extent of soil erosion in this region is essential for the protection and sustainable utilization of black soil resources.MethodsIn this study, linear and nonlinear models were compared combined with remote sensing images to invert soil organic carbon (SOC). In the scenario of SOC change, temporally variable soil erodibility factor were obtained. Then based on the RUSLE model and GIS technology, land use, rainfall, soil texture and digital elevation model (DEM) were used to evaluate the temporal and spatial variation characteristics of soil erosion in black soil region from 1995 to 2020 in Hulan River Basin. The main influencing factors were explored by random forest model and analyzes in combination with eco-geological.ResultsThe findings are as follows: The random forest (RF) model was optimal for SOC inversion (2020: R2 = 0.64, RMSE = 0.70, 2010: R2 = 0.66, RMSE = 0.35). The erosion intensity was mainly slight or mild while mean annual soil loss firstly decreased then increased from 1995 to 2020 reaching a rate of 1020.16 t km−2 y−1 by 2020. Rainfall and topography were the main driving factors of soil conservation changes, and soil erosion was more likely to occur in the eco-geological environment of the neutral rock hilly woodland area.DiscussionThe results provide insights into spatial distribution characteristics of black soils erosion which are crucial for preventing further degradation and ensuring national food security.

The past few years have seen the raise of unmanned aerial vehicles (UAV) in geosciences for generating highly accurate digital elevation models (DEM) at low costs, which promises to be an interesting alternative to satellite data for small river basins. The reliability of UAV-derived topography as input to hydraulic modelling is still under investigation: here, we analyse potentialities and highlight challenges of employing UAV-derived topography in hydraulic modelling in a tropical environment, where weather conditions and remoteness of the study area might affect the quality of the retrieved data. We focused on a stretch of the Limpopo River in Mozambique, where detailed ground survey and airborne data were available. First, we tested and compared topographic data derived by UAV (25 cm), RTK-GPS (50 cm DEM), LiDAR (1 m DEM) and SRTM (30 m DEM); then, we used each DEM as input data to a hydraulic model and compared the performance of each DEM-based model against the LiDAR based model, currently used as benchmark by practitioners in the area. Despite the challenges experienced during the field campaign—and described here—, the degree of accuracy in terrain modelling produced errors in water depth calculations within the tolerances adopted in this typology of studies and comparable in magnitude to the ones obtained from high-precision topography models. This suggests that UAV is a promising source of geometric data even in natural environments with extreme weather conditions.

Black soil has good properties and high fertility. Understanding the spatial distribution of black soil layer thickness is of great significance in promoting regional agricultural development, ecological environmental protection, and soil erosion control. However, traditional soil investigation methods often fail to provide detailed soil thickness information. This study focuses on a small watershed in Northeast China’s black soil region. By integrating topographical parameters and vegetation-climate indicators, random forest and kriging methods (classical bayesian, ordinary, and simple) were used to estimate the spatial distribution of thickness of black soil layer. An integrated evaluation framework was developed by combining RUSLE-derived erosion estimates with black soil layer thickness, systematically incorporating both external erosive forces and inherent soil erosion resistance attributes. The results show that the random forest model outperformed the kriging models, with smaller RMSE (34.05 cm) and larger R² (0.57), especially when handling nonlinear, high-dimensional data. The predicted thickness of the black soil layer ranged from 16.2 cm to 107 cm, with a mean of 48.31 cm, closely matching the measured value of 48 cm. Elevation (EL) was found to be the most significant factor affecting the thickness of black soil layer. Soil erosion risk assessment revealed that areas with no risk and low risk accounted for 21.91% and 62.21%, respectively, while medium and high-risk areas made up 15.87% and 0.01%. No-risk areas were soil accumulation zones, while low-risk areas were mainly sloped farmland, where measures like terracing, adjusting crop ridge directions, and planting pedunculated vegetation were recommended. Medium- and high-risk areas should be addressed by returning farmland to forests and implementing engineering practices. This study offers a reference for thickness of black soil layer estimation and provides valuable insights for soil erosion risk management.