Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images

Abstract

Soil salinization is a global issue resulting in soil degradation, arable land loss and ecological environmental deterioration. Over the decades, multispectral and hyperspectral remote sensing have enabled efficient and cost-effective monitoring of salt-affected soils. However, the potential of hyperspectral sensors installed on an unmanned aerial vehicle (UAV) to estimate and map soil salinity has not been thoroughly explored. This study quantitatively characterized and estimated field-scale soil salinity using an electromagnetic induction (EMI) equipment and a hyperspectral camera installed on a UAV platform. In addition, 30 soil samples (0~20 cm) were collected in each field for the lab measurements of electrical conductivity. First, the apparent electrical conductivity (ECa) values measured by EMI were calibrated using the lab measured electrical conductivity derived from soil samples based on empirical line method. Second, the soil salinity was quantitatively estimated using the random forest (RF) regression method based on the reflectance factors of UAV hyperspectral images and satellite multispectral data. The performance of models was assessed by Lin’s concordance coefficient (CC), ratio of performance to deviation (RPD), and root mean square error (RMSE). Finally, the soil salinity of three study fields with different land cover were mapped. The results showed that bare land (field A) exhibited the most severe salinity, followed by dense vegetation area (field C) and sparse vegetation area (field B). The predictive models using UAV data outperformed those derived from GF-2 data with lower RMSE, higher CC and RPD values, and the most accurate UAV-derived model was developed using 62 hyperspectral bands of the image of the field A with the RMSE, CC, and RPD values of 1.40 dS m−1, 0.94, and 2.98, respectively. Our results indicated that UAV-borne hyperspectral imager is a useful tool for field-scale soil salinity monitoring and mapping. With the help of the EMI technique, quantitative estimation of surface soil salinity is critical to decision-making in arid land management and saline soil reclamation.

Quantitative Estimation of Soil Salinization in an Arid Region of the Keriya Oasis Based on Multidimensional Modeling

“…The results of Bannari [8] illustrated that the red band yields the best ability on features of salt-affected land. Our studies also showed that the red band based on R-FD data has a high relevance to EC; that the combined bands of 2D and 3D indices are mainly concentrated at red edge, near-IR1, near-IR2 in this study site; and that the results of study are similar to peer researchers' conclusion [13,36,37]. Eventually, it found that the two-band indices and three-band indices showed better results than other single spectral bands in this study.…”

Detection model of soil salinization information in the Yellow River Delta based on feature space models with typical surface parameters derived from Landsat8 OLI image

“…Additionally, the chemical weathering is also a primary cause of soil salinization, during which Fe 2 O 3 is formed by the reaction of oxygen present in the atmosphere with primary Fe 2þ minerals in the soil. Moreover, the mass percentage would increase with the aggravation of weathering (Hu et al 2019). With the aggravation of soil salinization, the coverage of non-saline-resistant vegetation and surface soil moisture would decrease.…”

“…Humidity index is the third component of Landsat8 OLI images after Hat Transformation, which can reflects the humidity of soil in the study region (Hu et al 2019). The Yellow River Delta is located in the coastal area, and there is a significant difference in humidity between the coastal and inland areas (Guo et al 2019b).…”

“…The surface can be monitored by the imaging process of satellite remote sensing, in which the electromagnetic energy reflected by the target is used to obtain the information of soil salinization and also by combining the surface ecological environment of the Yellow River Delta and the relevant research works (Zeng et al 2006;Ha et al 2009;Guo et al 2019b;Hu et al 2019). The five typical remote sensing monitoring indices are calculated as follows:…”

Development of a VNIR/SWIR Multispectral Imaging System for Vegetation Monitoring with Unmanned Aerial Vehicles

“…Numerous studies indicate the higher potential for the VNIR/SWIR domain for remote sensing applications, e.g., vegetation trait estimations using wavelengths from the Red Edge to the SWIR domain [15,18,20,24,25,27,28,30,31,[64][65][66][67][68][69]. While this spectral range is available for airborne-or satellite-borne pushbroom sensing systems, for UAV platforms, only a few first studies are known [14,15].…”