The rapidly increasing world population and human activities accelerate the crisis of the limited freshwater resources. Water quality must be monitored for the sustainability of freshwater resources. Unmanned aerial vehicle (UAV)-borne hyperspectral data can capture fine features of water bodies, which have been widely used for monitoring water quality. In this study, nine machine learning algorithms are systematically evaluated for the inversion of water quality parameters including chlorophyll-a (Chl-a) and suspended solids (SS) with UAV-borne hyperspectral data. In comparing the experimental results of the machine learning model on the water quality parameters, we can observe that the prediction performance of the Catboost regression (CBR) model is the best. However, the prediction performances of the Multi-layer Perceptron regression (MLPR) and Elastic net (EN) models are very unsatisfactory, indicating that the MLPR and EN models are not suitable for the inversion of water quality parameters. In addition, the water quality distribution map is generated, which can be used to identify polluted areas of water bodies.

Soil organic matter (SOM) refers to all carbon-containing organic matter in soil and is one of the most important indicators of soil fertility. The hyperspectral inversion analysis of SOM traditionally relies on laboratory chemical testing methods, which have the disadvantages of being inefficient and time-consuming. In this study, 69 soil samples were collected from the Honghu farmland area and a mining area in northwest China. After pretreatment, 10 spectral indicators were obtained. Ridge regression, kernel ridge regression, Bayesian ridge regression, and AdaBoost algorithms were then used to construct the SOM hyperspectral inversion model based on the characteristic bands, and the accuracy of the models was compared. The results showed that the AdaBoost algorithm based on a grid search had the best accuracy in the different regions. For the mining area in northwest China, $R_p^2$ = 0.91, $RMS{E}_p$ = 0.22, and $MA{E}_p$ = 0.2. For the Honghu farmland area, $R_p^2$ = 0.86, $RMS{E}_p$ = 0.72, and $MA{E}_p$ = 0.56. The detection of SOM content using hyperspectral technology has the characteristics of a high detection precision and high speed, which will be of great significance for the rapid development of precision agriculture.

Hyperspectral imagery has been widely used in precision agriculture due to its rich spectral characteristics. With the rapid development of remote sensing technology, the airborne hyperspectral imagery shows detailed spatial information and temporal flexibility, which open a new way to accurate agricultural monitoring. To extract crop types from the airborne hyperspectral images, we propose a fine classification method based on multi-feature fusion and deep learning. In this research, the morphological profiles, GLCM texture and endmember abundance features are leveraged to exploit the spatial information of the hyperspectral imagery. Then, the multiple spatial information is fused with the original spectral information to generate classification result by using the deep neural network with conditional random field (DNN+CRF) model. Specifically, the deep neural network (DNN) is a deep recognition model which can extract depth features and mine the potential information of data. As a discriminant model, conditional random field (CRF) considers both spatial and contextual information to reduce the misclassification noises while keeping the object boundaries. Moreover, three multiple feature fusion approaches, namely feature stacking, decision fusion and probability fusion, are taken into account. In the experiments, two airborne hyperspectral remote sensing datasets (Honghu dataset and Xiong’an dataset) are used. The experimental results show that the classification performance of the proposed method is satisfactory, where the salt and pepper noise is decreased, and the boundary of the ground object is preserved.

The formation of black-odor water in urban rivers has a long history. It not only seriously affects the image of the city, but also easily breeds germs and damages the urban habitat. The prevention and treatment of urban black-odor water have long been important topics nationwide. “Action Plan for Prevention and Control of Water Pollution” issued by the State Council shows Chinese government’s high attention to this issue. However, treatment and monitoring are inextricably linked. There are few studies on the large-scale monitoring of black-odor water, especially the cases of using unmanned aerial vehicle (UAV) to efficiently and accurately monitor the spatial distribution of urban river pollution. Therefore, in order to get rid of the limitations of traditional ground sampling to evaluate the point source pollution of rivers, the UAV-borne hyperspectral imagery was applied in this paper. It is hoped to grasp the pollution status of the entire river as soon as possible from the surface. However, the retrieval of multiple water quality parameters will lead to cumulative errors, so the Nemerow comprehensive pollution index (NCPI) is introduced to characterize the pollution level of urban water. In the paper, the retrieval results of six regression models including gradient boosting decision tree regression (GBDTR) were compared, trying to find a regression model for the retrieval NCPI in the current scenario. In the first study area, the retrieval accuracy of the training dataset (adjusted_R² = 0.978), and test dataset (adjusted_R² = 0.974) was higher than that of the other regression models. Although the retrieval effect of random forest is similar to that of GBDTR in both training accuracy and image inversion, it is more computationally expensive. Finally, the spatial distribution graphs of NCPI and its technical feasibility in monitoring pollution sources were investigated, in combination with field observations.