Machine learning and multispectral data-based detection of soil salinity in an arid region, Central Iran
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Machine learning and multispectral data-based detection of soil salinity in an arid region, Central Iran Vahid Habibi & Hasan Ahmadi & Mohammad Jafari & Abolfazl Moeini
Received: 1 September 2020 / Accepted: 27 October 2020 # Springer Nature Switzerland AG 2020
Abstract In recent years, indirect methods have been used to estimate soil salinity in agricultural lands. In this research, the electrical conductivity of 93 soil samples from 0 to 30 cm and 0 to 100 cm was measured using the hypercube technique at Sharifabad-Saveh Plain, Iran. Land area parameters such as TWI, TCI, STP, DEM, and LS were used as topographic variables and spatial indices of salinity and vegetation were derived from Landsat 8 images. Soil salinity off crops and gardens was determined at 0–30 cm and 0–100 cm. The data were divided into two series: the training set (70%) and the test set (30%). In order to model and predict salinity, models such as an artificial neural network (ANN), integration of neural network and genetic algorithm (ANN-GA), PLSR, and decision tree (DT) were used. The results of the models’ evaluation based on MSE and R2 indices showed that the ANN-GA model has the highest accuracy in predicting soil properties. This model improved the accuracy of soil salinity prediction by 28%, 42%, and 23% in 0–30 cm and by 20%, 28%, and 25% at 100 cm than ANN, PLSR, and DT. The result showed the 2 dS/m EC at alfalfa and cucurbits farmlands while pistachio orchards have low salinity and bare lands have moderate and high salinity.
V. Habibi : A. Moeini Department of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran H. Ahmadi : M. Jafari (*) Faculty of Natural Resource, University of Tehran, Karaj, Iran e-mail: [email protected]
Keywords Pedometrics . Satellite image . Machine learning . Data mining . Soil classification map
Introduction Soil salinity is one of the most common processes of land degradation and soil degradation in arid and semiarid regions (Jian-li et al. 2011; Ahmadi 2018). Soil salinization causes land degradation and causes water and wind erosion, increasing the number of dust particles, eliminating vegetation, and reducing soil production capacity which intensifies land degradation and desertification. The spatial and temporal distribution of soil salts has made it difficult to accurately determine these parameters in field studies. Most studies on salinity assessment have been indirectly conducted at a small scale (Corwin and Lesch 2005; Kumar et al. 2016). On the other hand, the use of laboratory methods for estimating salinity is generally time consuming and expensive. Also, specifying the temporal and spatial variations of soil characteristics in larger areas is very important in solving agricultural, environmental, and sustainable development problems (Wang and Li 2013; Camera et al. 2016). In order to achieve sustainable development, sufficient and up-to-date information are vital. But updating soil information is expensive and time consuming. Therefore, in ord
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