River channels are fundamental geomorphological and hydrological features that play a critical role in regulating the Earth’s water cycle and ecosystems and influencing human activities. This study utilized Digital Elevation Model (DEM) data and multi-source remote sensing imagery (including GF-1 WFV, Sentinel-1, and Sentinel-2) to determine river channel dimensions. River water masks were obtained from multiple remote sensing imagery sources and processed through triangulation and segmentation to generate river reach results. Based on these segmented river reaches, buffer analysis was conducted. The buffer analysis results were then used to refine and clip the 5 m DEM and 12.5 m DEM datasets. Finally, river channels were extracted from the clipped DEM data using the natural breaks classification method. The classification accuracy was assessed using a confusion matrix. Experimental results demonstrate a high overall classification accuracy, reaching or exceeding 0.985, with classification consistency (Kappa coefficient) ranging from 0.78 to 0.81. The 5 m resolution DEM exhibited superior performance compared to the 12.5 m resolution DEM in river channel extraction, especially regarding the classification consistency (Kappa coefficient), with the 5 m resolution model outperforming the latter. This approach effectively delineates the river channel boundaries, transcends the constraints of a singular data source, enhances the precision and resilience of river extraction, and possesses several practical applications. The extracted data can support analyses of river evolution, facilitate hydrological modeling at the basin scale, improve flood disaster monitoring, and contribute to various other research domains.

Accurate monitoring of crop drought thresholds at different growth periods is crucial for drought monitoring. In this study, the canopy temperature (T_c) of winter wheat (‘Weilong 169’ variety) during the three main growth periods was extracted from high-resolution thermal and multispectral images taken by a complete unmanned aerial vehicle (UAV) system. Canopy-air temperature difference (ΔT) and statistic Crop Water Stress Index (CWSI_si) indicators were constructed based on T_c. Combined experiment data from the field and drought thresholds for the ΔT and CWSI_si indicators for different drought levels at three main growth periods were monitored. The results showed a strong correlation between the T_c extracted using the NDVI-OTSU method and ground-truth temperature, with an R² value of 0.94. The CWSI_si was more stable than the ΔT index in monitoring the drought level affecting winter wheat. The threshold ranges of the CWSI_si for different drought levels of winter wheat at three main growth periods were as follows: the jointing–heading period, where the threshold ranges for normal, mild drought, moderate drought, and severe drought are 0.48, respectively; the heading–filling period, where the threshold ranges for normal, and mild, moderate, and severe drought are 0.53, respectively; and the filling–maturation period, where the threshold ranges for normal, mild drought, moderate drought, and severe drought are 0.59, respectively. The UAV thermal threshold method system can improve the accuracy of crop drought monitoring and has considerable potential in crop drought disaster identification.

Field water use efficiency is an important parameter for evaluating the quality of field irrigation in irrigated areas, which directly affects the country’s food security and water resource allocation. However, most current studies use point-scale soil moisture (SM) or remote sensing water balance models to calculate the field water use coefficient, which cannot avoid errors caused by the spatial heterogeneity of SM and insufficient spatial resolution of remote sensing data. Therefore, in this study, the cosmic-ray neutron sensor (CRNS), Time-Domain Reflectometers (TDR) and Automatic Weather Stations (AWS) were used to monitor the meteorological and hydrological data such as SM, atmospheric pressure, and precipitation in the experimental area of Jinghuiqu Irrigation District for three consecutive years. The scale of the CRNS SM lies between the point and the remote sensing. Based on the CRNS SM, the calculation method for canal head and tail water was used to calculate the field water use efficiency to evaluate the level of agricultural irrigation water use in the experimental irrigation area. The results showed that CRNS could accurately detect the change in SM, and four irrigation events were monitored during the winter wheat growth period from October 2018 to June 2019; the calculation result of field water use efficiency in the experimental area was 0.77. According to the field water use efficiency of the same irrigation area from October 2013 to October 2015 in other studies, the field water use efficiency during the growing period of winter wheat in this area increased from 0.503 to 0.770 in 2013–2019, indicating a significant improvement in the field water use level. In general, this study not only solves the problem of low calculation accuracy of field water use efficiency caused by the mismatch of SM monitoring scales but also explores the application potential of CRNS in agricultural irrigation management and water resource allocation.

The river border positioning is an important part of river surveys, which is crucial for water conservation project development, water resource use, water disasters, river regime monitoring, and many other applications related to water resources. Currently, satellite images or field measurements are used to extract river channels. However, satellite images are insufficiently precise, and field measurement requires significant manpower and cost. In this paper, a new method for river channel extraction is proposed, which is based on the combination of Jenks natural breaks classification method and digital elevation model (DEM), and then the river channel range is complemented by using the water range monitored by GF-1(Gaofen-1 satellite) in flood season. The overall precision is greater than 85%, and the Kappa values achieve moderate stability (0.41–0.60). Using this method, the extraction of river range is practicable and achievable, and the higher the DEM resolution, the better the extraction result.

Accurate extraction of farmland boundaries is crucial for improving the efficiency of farmland surveys, achieving precise agricultural management, enhancing farmers’ production conditions, protecting the ecological environment, and promoting local economic development. Remote sensing and deep learning are feasible methods for creating large-scale farmland boundary maps. However, existing neural network models have limitations that restrict the accuracy and reliability of agricultural parcel extraction using remote sensing technology. In this study, we used high-resolution satellite images (2 m, 1 m, and 0.8 m) and the U²-Net++ model based on the RSU module, deep separable convolution, and the channel-spatial attention mechanism module to extract different types of fields. Our model exhibited significant improvements in farmland parcel extraction compared with the other models. It achieved an F1-score of 97.13%, which is a 7.36% to 17.63% improvement over older models such as U-Net and FCN and a more than 2% improvement over advanced models such as DeepLabv3+ and U²-Net. These results indicate that U²-Net++ holds the potential for widespread application in the production of large-scale farmland boundary maps.

Ground validation of remote sensing soil moisture requires ground measurements corresponding to the pixel scale. To date, there is still a lack of simple, fast and reasonable methods for soil moisture measurement at pixel scale between point measurements and remote sensing observations. In this study, a measurement method of soil moisture using ground-penetrating radar (GPR) was proposed for pixel scale. We used a PulseEKKOTM PRO GPR system with 250 MHz antennas to measure soil moisture by Fixed Offset (FO) method in four 30 × 30 m² plots chosen from the desert steppe. This study used a random combination method to analyze soil moisture measurements acquired by different numbers of GPR survey lines. The results showed that two survey lines of GPR would be sufficient under confidence level of 90% with the relative error of 7%, and four survey lines of GPR would be eligible under confidence level of 95% with the relative error of 5% for each plot. GPR measurement can reproduce the spatial distribution of soil moisture with higher resolution and smaller error, especially when two and four survey lines are designed in cross shape and grid shape, respectively. The method was applied to ground validation for the soil moisture from Landsat 8, showing the advantages of stable relative errors, less contingency and reliable evaluation when compared to point measurements. This method is fast and convenient and not limited to a certain pixel, and thus largely benefits the scale matching of remote sensing results and field measurements in ground validation.