Leaf area index (LAI) is a key biophysical parameter for monitoring crop growth status, predicting crop yield, and quantifying crop variability in agronomic applications. Mapping the LAI at the field scale using multispectral cameras onboard unmanned aerial vehicles (UAVs) is a promising precision-agriculture application with specific requirements: The LAI retrieval method should be (1) robust so that crop LAI can be estimated with similar accuracy and (2) easy to use so that it can be applied to the adjustment of field management practices. In this study, three UAV remote-sensing missions (UAVs with Micasense RedEdge-M and Cubert S185 cameras) were carried out over six experimental plots from 2018 to 2019 to investigate the performance of reflectance-based lookup tables (LUTs) and vegetation index (VI)-based LUTs generated from the PROSAIL model for wheat LAI retrieval. The effects of the central wavelengths and bandwidths for the VI calculations on the LAI retrieval were further examined. We found that the VI-LUT strategy was more robust and accurate than the reflectance-LUT strategy. The differences in the LAI retrieval accuracy among the four VI-LUTs were small, although the improved modified chlorophyll absorption ratio index-lookup table (MCARI2-LUT) and normalized difference vegetation index-lookup table (NDVI-LUT) performed slightly better. We also found that both of the central wavelengths and bandwidths of the VIs had effects on the LAI retrieval. The VI-LUTs with optimized central wavelengths (red = 612 nm, near-infrared (NIR) = 756 nm) and narrow bandwidths (~4 nm) improved the wheat LAI retrieval accuracy (R² ≥ 0.75). The results of this study provide an alternative method for retrieving crop LAI, which is robust and easy use for precision-agriculture applications and may be helpful for designing UAV multispectral cameras for agricultural monitoring.

Satellite and unmanned aerial vehicle (UAV) remote sensing can be used to estimate soil properties; however, little is known regarding the effects of UAV and satellite remote sensing data integration on the estimation of soil comprehensive attributes, or how to estimate quickly and robustly. In this study, we tackled those gaps by employing UAV multispectral and Sentinel-2B data to estimate soil salinity and chemical properties over a large agricultural farm (400 ha) covered by different crops and harvest areas at the coastal saline-alkali land of the Yellow River Delta of China in 2019. Spatial information of soil salinity, organic matter, available/total nitrogen content, and pH at 0–10 cm and 10–20 cm layers were obtained via ground sampling (n = 195) and two-dimensional spatial interpolation, aiming to overlap the soil information with remote sensing information. The exploratory factor analysis was conducted to generate latent variables, which represented the salinity and chemical characteristics of the soil. A machine learning algorithm (random forest) was applied to estimate soil attributes. Our results indicated that the integration of UAV texture and Sentinel-2B spectral data as random forest model inputs improved the accuracy of latent soil variable estimation. The remote sensing-based information from cropland (crop-based) had a higher accuracy compared to estimations performed on bare soil (soil-based). Therefore, the crop-based approach, along with the integration of UAV texture and Sentinel-2B data, is recommended for the quick assessment of soil comprehensive attributes.

Crop residues management has great effects on weeds in croplands. To understand the weed responses to crop residues management and weeds impact on crop yield, a field trial with three crop residues management strategies has been conducted in the North China Plain since 2008. Weed community composition and structure across the species, morphological types, life forms, and community levels were investigated during 2019–2020. The results show that the field with crop residues retention significantly decreased weed density than that in the field with no crop residues retention. Furthermore, total crop residues retention significantly decreased weed density than half crop residues retention. Compared with no crop residues retention, the weed aboveground dry matter in the field with total and half crop residues retention significantly decreased. Meanwhile, the maize grain yield significantly increased, resulting from weeds decreased with crop residues retention on the field. Negative correlations were found between maize grain yield and the density and aboveground dry matter of monocotyledonous weeds. These findings indicate that long term crop residues retention under conventional tillage might be an effective agronomic practice to retard weed growth. However, the mechanism of crop residues retention on weed control is still needed to research.

Salt-affected arable land is distributed widely in China, especially in the North China Plain. Crop residue management under appropriate tillage is critical to improving salt-affected soil organic carbon and reducing the carbon footprint. This study conducted four-year field experiments including two treatments (residue incorporated into soil with plough tillage (CT+); residue mulching with no-till (NT+)) in two sites (non-saline soil and salt-affected soil); the carbon footprint of wheat production was analyzed by life cycle assessment. The results showed that the carbon footprint of wheat production in the salt-affected soil was significantly larger than that in the non-saline soil, because the salt-affected soil exhibited higher N₂O emission than the non-saline soil. CT+ has lower carbon footprint than the NT+, mainly due to the lower N₂O emission and higher carbon sequestration in the CT+ compared to NT+. As for the salt-affected soil, the largest contributor of the carbon footprint per unit area was soil N₂O emission, with a relative contribution of 40%; the largest contributor of the carbon footprint per unit yield was carbon sequestration, with a relative importance of 47–50%. Our results indicated that wheat production in salt-affected land has a high carbon footprint, while it can be decreased by incorporating crop residue into the soil under the plough tillage.