Soil salinity digital mapping using random forest model in saline lands of Eyvanekey plain

Document Type : Research Paper

Authors

1 Soil Science Department Faculty of Agriculture,, University of Tehran, Karaj,

2 Soil Science Department, Faculty of Agriculture, University of Tehran, Tehran, Iran.

Abstract

This study aimed to investigate the spatial changes of soil salinity using RF model in a part of Eyvanekey Plain (Semnan Province 2018). Grid sampling with 100 m intervals (106 samples) was taken from 105 ha of soils developed on marl and gravely alluviums. The land uses were pistachio plantations with furrow irrigation and abandoned land. The maximum EC was (173.2 and 34 dS/m) in the abandoned and furrow irrigation pistachio plantations respectively. The main factors of salinization were saline marls, saline irrigation water, and high PET. The R2 for the salinity prediction map by RF model was 0.49, and the most important covariates were normalized difference salinity index (NDSI), topographic wetness index (TWI), Channel Network Base Level (CNBL), normalized difference vegetation index (NDVI), and modified soil vegetation index (SAVI). Spectral ratio indices derived from Landsat 8 contributed the most to the soil salinity prediction. Out of 5 main auxiliary variables, 3 variables are related to spectral ratio indices and the reason was the presence of salt on the soil in the studied area. Using NDVI with other salinity and moisture indices improved the salinity prediction model. Examining the results of covariates correlation and the implementation of recursive feature elimination showed that many covariates increase model complexity and prediction error. Recursive feature elimination helped to simplify the model by identifying the most important covariates. The salinity prediction map by random forest was consistent with the field observations and clearly defined the critical saline area.

Keywords

Main Subjects

Soil Salinity Digital Mapping using Random Forest Model in Saline Lands of Eyvanekey Plain

EXTENDED ABSTRACT

Introduction

Soil salinization is a prevalent form of land degradation in arid regions which threatens soil productivity, agricultural sustainability, and food security. Since, the salinity can be caused by anthropogenic activities, in addition to natural sources, many studies have been conducted to study the spatial and temporal changes and their constituents. Monitoring and evaluating spatiotemporal dynamics of soil salinity are important due to high variability of salinity. The classical methods of study of soil salinity are time and cost-consuming compared to digital soil mapping. Monitoring and lab works in digital soil mapping could be reduced.

Purpose

The purpose of this study was characterizing and mapping secondary salinity by using random forest model at a field scale in Eyvanekey plain for precision and sustainable management of saline soils in study area.

Research method

106 surface samples were taken in 2018 by grid with 100 m intervals from 105 ha of soils developed on marl and gravely alluviums parent materials and the necessary measurements were made on them. The land uses were pistachio tree with furrow irrigation and abandoned land. According to SCORPAN model, soil salinity data as target variable and spectral indices derived from satellite images and train attributes derived from DEM as environmental covariates were prepared in 30 m resolution. Random forest model was used to connect soil salinity data and environmental covariates to predict salinity map. Also recursive feature elimination method was used to determine the suitable amount and type of environmental covariates for avoiding prediction model complexity and error.

Results

The hot and arid climate combined with other environmental parameters demonstrate the unsuitability of the study area for agricultural purposes. All the study area was classified as hypersaline soil (ECe > 4 dS/m). EC amount range was between 4.25 to 173.2 dS/m with mean amount 51.98 dS/m. According to salinity map the highest values of ECe were at the middle of the field, in pistachio trees land use, followed by the abandoned area located on the downward slope that receives the runoff water which has passed through the higher saline area. The main factors of salinity in study area were saline parent materials, the quality of irrigation water, high evaporation and transpiration and capillary rise of solutes. The coefficient of determination (R2) of the salinity prediction map by random forest model was equal to 0.49 and the most important covariate for salinity mapping were normalized difference salinity index (NDSI), topographic wetness index (TWI), Channel Network Base Level (CNBL), normalized difference vegetation index (NDVI), and modified soil vegetation index (SAVI). Spectral ratio indices derived from Landsat 8 had an important contribution in the soil salinity prediction model and out of 5 main environmental covariates, 3 were related to spectral ratio indices. Also, the results showed that use of NDVI withother spectral ratio indices like salinity and moisture indices improved salinity prediction model. In general, the use of spectral ratio indices derived from satellite images are very useful for salinity studies due to provide an overview of the salinity variation in study area, but always the uncertainty of the data should be considered. It should be considered that the amount and mineralogy of salt, soil moisture, soil colour and roughness could effect on salt reflection and as a result on the remote sensing derived data. Examining the results of environmental covariates correlation and the implementation of recursive feature elimination showed that the presence of many environmental covariates increased model complexity and prediction error. Recursive feature elimination results helped to simplify the model by identifying the most important environmental covariates for making random forest model. The salinity prediction map by random forest was consistent with the field observations and clearly defined the critical saline area in middle part of the field.

Conclusion

The availability of suitable land for agriculture use in arid region is scarce and having information about limitation factors like salinity is essential to prevent soil degradation. To improve the existing fragile conditions, characterize saline soils and monitoring salinity changes is necessary. salinity map produced by RF was reliable and helped to see the extent and severity of salinity in each single pixel of study area. The salinity prediction map was in consistent with the field observations and showed the middle part of the study area as a critical saline area. The digital nature of these maps allows information to be updated at a lower cost and faster in future in the study area.

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Iranian Journal of Soil and Water Research
Volume 55, Issue 3
June 2024
Pages 431-447

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