The water quality index (WQI) has been used to identify threats to water quality and to support better water resource management. This study combines a machine learning algorithm, WQI, and remote sensing spectral indices (difference index, DI; ratio index, RI; and normalized difference index, NDI) through fractional derivatives methods and in turn establishes a model for estimating and assessing the WQI. The results show that the calculated WQI values range between 56.61 and 2,886.51. We also explore the relationship between reflectance data and the WQI. The number of bands with correlation coefficients passing a significance test at 0.01 first increases and then decreases with a peak appearing after 1.6 orders. WQI and DI as well as RI and NDI correlation coefficients between optimal band combinations of the peak also appear after 1.6 orders with R2 values of 0.92, 0.58 and 0.92. Finally, 22 WQI estimation models were established by POS-SVR to compare the predictive effects of these models. The models based on a spectral index of 1.6 were found to perform much better than the others, with an R2 of 0.92, an RMSE of 58.4, and an RPD of 2.81 and a slope of curve fitting of 0.97.

Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the space-borne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model’s SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0–10 cm) from the farmland (2.5 × 104 m2) of Fukang City, Xinjiang Uygur AutonomousRegion, China. The random forest (RF) method and extreme learning machine (ELM) were used to estimate the SMC using six methods of pretreatments combined with four optimal spectral indices. The validation accuracy of the estimated method clearly increased compared with that of linear models. The combination of pretreatments and indices by our assessment effectively eliminated the interference and the noises. Comparing two machine learning algorithms showed that the RF models were superior to the ELM models, and the best model was PIR (R2val = 0.907, RMSEP = 1.477, and RPD = 3.396). The SMC map predicted via the best scheme was highly similar to the SMC map measured. We conclude that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy (R2val = 0.907) via UAV hyperspectral imagery on a regional scale. Ultimately, our program might improve management and conservation strategies for agroecosystem systems in arid regions.

Traditional machine learning methods for water body extraction need complex spectral analysis and feature selection which rely on wealth of prior knowledge. They are time-consuming and hard to satisfy our request for accuracy, automation level and a wide range of application. We present a novel deep learning framework for water body extraction from Landsat imagery considering both its spectral and spatial information. The framework is a hybrid of convolutional neural networks (CNN) and logistic regression (LR) classifier. CNN, one of the deep learning methods, has acquired great achievements on various visual-related tasks. CNN can hierarchically extract deep features from raw images directly, and distill the spectral–spatial regularities of input data, thus improving the classification performance. Experimental results based on three Landsat imagery datasets show that our proposed model achieves better performance than support vector machine (SVM) and artificial neural network (ANN).

The Ebinur Lake is a closed inland lake located within the arid region of the Xinjiang Autonomous Region in the northwestern part of China, near the Kazakhstan border. The shrinkage of the lake area is believed to be caused by ecological environmental deterioration and has become an important restraining factor for the social development of the local population. Of all the lakes in the Xinjiang Autonomous Region, the Ebinur Lake is the most severely impacted water body. The lake has undergone change in size naturally for over thousands of years due to natural causes. However, the authors observed the dramatic changes in the freshwater resources of this region from the aerial images from 1972 to 2013. Thus, this paper traces and analyzes the change in the Ebinur Lake surface area in the past 41 years. A set of six satellite images acquired between 1972 and 2013 was employed to map the change in the surface area of the Ebinur Lake using the water index approach. The authors applied the traditional normalized difference water index (NDWI) and the modified normalized difference water index (MNDWI) to quantify the change in the water body area of the Ebinur Lake during the study period. The results indicate that the lake area has experienced a dramatic decrease of 31.4% from 1972 to 2013. The paper also examines the natural processes and human activities that may have contributed to the decrease in the lake area. The results show that the decrease in total lake area appears to coincide with periods of rapid land reclamation in the study area. Moreover, the uncontrolled land reclamation activities, such as irrigation, can increase the sedimentation in the Ebinur Lake thereby reducing the lake size. Reduction of the lake area has a negative ecological impact on the environment and on human life and property. The lake area is the most important factor to ensure the environment of the watershed and the key index to measure the environment balance.

Controlling and managing surface source pollution depends on the rapid monitoring of total nitrogen in water. However, the complex factors affecting water quality (plant shading and suspended matter in water) make direct estimation extremely challenging. Considering the spectral response mechanisms of emergent plants, we coupled discrete wavelet transform (DWT) and fractional order discretization (FOD) techniques with three machine learning models (random forest (RF), bagging algorithm (bagging), and eXtreme Gradient Boosting (XGBoost)) to mine this potential spectral information. A total of 567 models were developed, and airborne hyperspectral data processed with various DWT scales and FOD techniques were compared. The effective information in the hyperspectral reflectance data were better emphasized after DWT processing. After DWT processing the original spectrum (OR), its sensitivity to TN in water was maximally improved by 0.22, and the correlation between FOD and TN in water was optimally increased by 0.57. The transformed spectral information enhanced the TN model accuracy, especially for FOD after DWT. For RF, 82% of the model R2 values improved by 0.02~0.72 compared to the model using FOD spectra; 78.8% of the bagging values improved by 0.01~0.53 and 65.0% of the XGBoost values improved by 0.01~0.64. The XGBoost model with DWT coupled with grey relation analysis (GRA) yielded the best estimation accuracy, with the highest precision of R2 = 0.91 for L6. In conclusion, appropriately scaled DWT analysis can substantially improve the accuracy of extracting TN from UAV hyperspectral images. These outcomes may facilitate the further development of accurate water quality monitoring in sophisticated global waters from drone or satellite hyperspectral data.