Compared to conventional laboratory testing methods, crop nitrogen estimation methods based on canopy spectral characteristics have advantages in terms of timeliness, cost, and practicality. A variety of rapid and non-destructive estimation methods based on the canopy spectrum have been developed on the scale of space, sky, and ground. In order to understand the differences in estimation accuracy and applicability of these methods, as well as for the convenience of users to select the suitable technology, models for estimation of nitrogen status of winter wheat were developed and compared for three methods: drone equipped with a multispectral camera, soil plant analysis development (SPAD) chlorophyll meter, and smartphone photography. Based on the correlations between observed nitrogen status in winter wheat and related vegetation indices, green normalized difference vegetation index (GNDVI) and visible atmospherically resistant index (VARI) were selected as the sensitive vegetation indices for the drone equipped with a multispectral camera and smartphone photography methods, respectively. The correlation coefficients between GNDVI, SPAD, and VARI were 0.92 ** and 0.89 **, and that between SPAD and VARI was 0.90 **, which indicated that three vegetation indices for these three estimation methods were significantly related to each other. The determination coefficients of the 0–90 cm soil nitrate nitrogen content estimation models for the drone equipped with a multispectral camera, SPAD, and smartphone photography methods were 0.63, 0.54, and 0.81, respectively. In the estimation accuracy evaluation, the method of smartphone photography had the smallest root mean square error (RMSE = 9.80 mg/kg). The accuracy of the smartphone photography method was slightly higher than the other two methods. Due to the limitations of these models, it was found that the crop nitrogen estimation methods based on canopy spectrum were not suitable for the crops under severe phosphate deficiency. In addition, in estimation of soil nitrate nitrogen content, there were saturation responses in the estimation indicators of the three methods. In order to introduce these three methods in the precise management of nitrogen fertilizer, it is necessary to further improve their estimation models.

In North China Plain, accurate spatial and temporal ET and precipitation pattern is very important in the groundwater resource assessment. This study demonstrated the potential for modelling ET and groundwater processes using remote sensing data for distributed hydrological modelling with MIKE SHE codes in the Haihe Plain, China. The model was successfully validated against independent groundwater level measurements following the calibration period and the model also provided a reasonable match of the lysimeter measurements of ET. The remote sensing data included ET derived from global radiation products of Fengyun-2C geostationary meteorological satellite (FY-2C) and FY-2C precipitation products. The comparisons show that precipitation is a critical factor for the hydrological response and for the spatial distribution of ET and groundwater flow. FY-2C precipitation products has a spatial resolution of about 11 km, which thus adds more spatial variability to the most important driving variable. The ET map based on FY-2C data has a higher spatial variability than that map based on conventional data, which are caused by higher resolution of ground information. The groundwater level changes in the aquifer system are shown in the quite different spatial patterns under two models, which is affected by the significant difference between two types of precipitation. In the Haihe Plain, accurate spatial and temporal ET pattern is very important in the groundwater resource assessment that determines the recharge to the saturated zone.

Albedo is one of the import parameters for the routine applications of various remote sensing models, and most methods for estimating albedo are only applicable in plain areas with relatively flat topography. Due to the existence of the undulant terrain in mountain areas, the incoming radiance can be decomposed into beam, diffuse, and terrain radiance, which are affected by atmospheric transmittance, solar zenith and azimuth angles, terrain slope and aspect, etc. The existence of partial shadow in low-resolution pixel makes the calculation of albedo more complicated in the mountain areas. This paper presents a rapid and operational method for estimating albedo on complex terrain using high-resolution Digital Elevation Model (DEM) and MODerate-resolution Imaging Spectroradiometer (MODIS) data with sensor view angles less than 20°. Shadow coverage ratio of the low-resolution map was retrieved from high-resolution shadow map, and was used for the correction of beam radiance. The parameters used for the atmospheric correction of narrowband beam radiance and weighting coefficients for the calculation of broadband albedo using MODIS with and without band 5 were developed based on the SMARTS2 simulation under different atmospheric conditions. Taken the Taihang Mountain in the North China as the study area, the calculated reflectance and albedo were compared with the MODIS reflectance and albedo products. The maximum deviations between estimated and blue-sky albedos were −0.0155 and +0.0151 in plain areas, and −0.00667 and 0.01747 in the mountain areas, respectively. Good agreements indicate that the procedure presented in this paper is applicable for the albedo calculation in the mountain areas using MODIS images and high-resolution DEM.

Intercropping with green manures is an effective practice for increasing agricultural production and reducing environmental issues. However, the effects of green manure type and intercropping patten on soil nutrient availability and microbial communities remains underexplored. In the present study, the impacts of three green manure–maize intercropping patterns on maize yield, rhizosphere nutrient availability, and soil fungal community were evaluated. Four treatments (three replicate plots for each) were involved, including a monoculture treatment (MC) as a control and three intercropping patterns as follows: maize–ryegrass (Lolium perenne L.) (IntL), maize–forage soybean (Fen Dou mulv 2, a hybrid soybean cultivar) (IntF), and maize–ryegrass–forage soybean (IntLF) intercropping. The results showed that all three intercropping patterns significantly increased maize yield and rhizosphere available phosphorus (AP) compared with MC. Intercropping shifted the dominant assembly process of the maize rhizosphere fungal community from stochastic to deterministic processes, shaping a community rich in arbuscular mycorrhizal fungi (AMF) and limited in plant pathogens, primarily Exserohilum turcicum. AP showed significant correlations with fungal community and AMF, while maize yield was negatively correlated with plant pathogens. In addition, the dual-species green manure intercropping pattern (IntLF) had the strongest positive effects on maize yield, AP content, and fungal community compared with single-species patterns (IntL and IntF). These results illustrate the advantages of planting diversification in boosting crop production by improving nutrient availability and soil health in the rhizosphere and suggest that the maize–ryegrass–forage soybean intercropping system is a potential strategy for improving soil fertility and health.