Land use affects regional hydrological processes. The alteration of regional distributions of vegetation, crop types, and land-use patterns for construction has a significant impact on the runoff process and influences the water cycle in watersheds. Studies on runoff variations in the Hutuo River Basin have concentrated on climate change and the effect of human activities without adequate attention paid to land-use changes. In order to investigate the response of runoff to land-use changes in the upper Hutuo River Basin, a soil and water assessment model was used in this study to compare and analyze the changes in runoff under five land-use scenarios from 1980–2020. The results show that the area of farmland, forest land, and grassland in the watershed gradually decreased from 1980 to 2020, with a total decrease of 3.1%, while the area of urban construction land increased rapidly by 1.5 times. Corresponding with the trend of land-use change, the differences between the simulated and natural values for regional flood peak and annual runoff increased with time, which is in line with the changing land-use trends. From 1960–2020, the differences between the simulated and natural values for the flood peaks of the five land-use scenarios were −16.8, −6.7, −3.5, 4.6, and 9.3%, respectively, and the errors between the simulated and natural values for annual runoff were −6.7, −4.4, −2.0, −2.6, and 10.8%, respectively. Overall, the increase in urban construction land and decrease in farming, forest area, and grassland has caused the regional flood peak and annual runoff volume to increase in the upper Hutuo River Basin.

Soil moisture content is one of the most important soil indices for agriculture production. With the increasing food requirement and limited irrigation water sources, it is of great significance to accurately and quickly measure the soil moisture content for precision irrigation, especially in deficient agricultural areas, such as North China Plain. To achieve this goal, more attention was paid to the application of unmanned aerial vehicle multispectral reflectance technology. However, it was urgent to enhance the regression models between spectral data and soil realistic moisture content, and there were limited studies about the regression research on deep soil layers. Thus, the farmland of winter wheat–summer maize double cropping at Yongnian District, Hebei, North China, was selected as the study area. A six-band multispectral camera mounted on a low-altitude unmanned aerial vehicle (UAV) was used to obtain the field spectral reflectance with bands from 470~810 nm, and meanwhile, soil moisture content at different depths (10, 20, 30, 40, 50, and 60 cm) was measured after maize sowing. Unary linear regression (ULR), multivariate linear regression (MLR), ridge regression (RR), and an artificial neural network (ANN) were employed to establish regression models. The results demonstrated that the sensitive bands of spectral reflectance were 690 nm, 470 nm, and 810 nm. Those models all established significant regression at the depths of 0–20 cm and 40–60 cm, particularly at 10, 50, and 60 cm soil layers. However, for a depth of 20–40 cm, the prediction accuracy was generally lower. Among MLR, RR, and BP models, the MLR exhibited the highest identification accuracy, which was most recommended for the application. The findings of this study provide technical guidance and effective regression for the multispectral reflectance on the farmland of North China Plain, especially for deep soil layer moisture prediction.