Soil organic matter (SOM) is a critical indicator of soil nutrient levels, and the precise mapping of its spatial distribution through remote sensing is essential for soil regulation, precise fertilization, and scientific management and protection. This information can offer decision support to agricultural management departments and various agricultural producers. In this paper, two new soil indices, NLI_rededge2 and GDVI_rededge2, were proposed based on the sensitive spectral response characteristics of SOM in Northeast China. Nine parameters suitable for SOM mapping and modeling were determined using the competitive adaptive reweighted sampling (CARS) method, combined with spectrum reflectance, mathematical transformations of reflectance, vegetation indices, and so on. Then, utilizing unmanned aerial vehicle (UAV)-based multispectral images with centimeter-level resolution, a random forest machine learning algorithm was used to construct the inversion model of SOM and mapping SOM in the study area. The results showed that the random forest algorithm performed best for estimating SOM (R² = 0.91, RMSE = 0.95, MBE = 0.49, and RPIQ = 3.25) when compared with other machine learning algorithms such as support vector regression (SVR), elastic net, Bayesian ridge, and linear regression. The findings indicated a negative correlation between SOM content and altitude. The study concluded that the SOM modeling and mapping results could meet the needs of farmers to obtain basic information and provide a reference for UAVs to monitor SOM.

Lodging depresses the grain yield and quality of maize crop. Previous machine learning methods are used to classify crop lodging extents through visual interpretation and sensitive features extraction manually, which are cost-intensive, subjective and inefficient. The analysis on the accuracy of subdivision categories is insufficient for multi-grade crop lodging. In this study, a classification method of maize lodging extents was proposed based on deep learning algorithms and unmanned aerial vehicle (UAV) RGB and multispectral images. The characteristic variation of three lodging extents in RGB and multispectral images were analyzed. The VGG-16, Inception-V3 and ResNet-50 algorithms were trained and compared depending on classification accuracy and Kappa coefficient. The results showed that the more severe the lodging, the higher the intensity value and spectral reflectance of RGB and multispectral image. The reflectance variation in red edge band were more evident than that in visible band with different lodging extents. The classification performance using multispectral images was better than that of RGB images in various lodging extents. The test accuracies of three deep learning algorithms in non-lodging based on RGB images were high, i.e., over 90%, but the classification performance between moderate lodging and severe lodging needed to be improved. The test accuracy of ResNet-50 was 96.32% with Kappa coefficients of 0.9551 by using multispectral images, which was superior to VGG-16 and Inception-V3, and the accuracies of ResNet-50 on each lodging subdivision category all reached 96%. The ResNet-50 algorithm of deep learning combined with multispectral images can realize accurate lodging classification to promote post-stress field management and production assessment.

Accurately determining the distribution and quantity of maize straw types is of great significance for evaluating the effectiveness of conservation tillage, precisely estimating straw resources, and predicting the risk of straw burning. The widespread adoption of conservation tillage technology has greatly increased the diversity and complexity of maize straw coverage in fields after harvest. To improve the precision and effectiveness of remote sensing recognition for maize straw types, a novel method was proposed. This method utilized unmanned aerial vehicle (UAV) multispectral imagery, integrated the Stacking Enhanced Straw Index (SESI) introduced in this study, and combined height, shape, and spectral characteristics to improve recognition accuracy. Using the original five-band multispectral imagery, a new nine-band image of the study area was constructed by integrating the calculated SESI, Canopy Height Model (CHM), Product Near-Infrared Straw Index (PNISI), and Normalized Difference Vegetation Index (NDVI) through band combination. An object-oriented classification method, utilizing a “two-step segmentation with multiple algorithms” strategy, was employed to integrate height, shape, and spectral features, enabling rapid and accurate mapping of maize straw types. The results showed that height information obtained from the CHM and spectral information derived from SESI were essential for accurately classifying maize straw types. Compared to traditional methods that relied solely on spectral information for recognition of maize straw types, the proposed approach achieved a significant improvement in overall classification accuracy, increasing it by 8.95% to reach 95.46%, with a kappa coefficient of 0.94. The remote sensing recognition methods and findings for maize straw types presented in this study can offer valuable information and technical support to agricultural departments, environmental protection agencies, and related enterprises.