Sentinel-2 images for effective mapping of soil salinity in agricultural fields

被引：8

作者：

Al-Gaadi, Khalid A. ^{[1
,2
]}

Tola, ElKamil ^{[1
,2
]}

Madugundu, Rangaswamy ^{[1
]}

Fulleros, Ronnel B. ^{[2
]}

机构：

[1] King Saud Univ, Deanship Sci Res, Precis Agr Res Chair, Riyadh 11451, Saudi Arabia

[2] King Saud Univ, Coll Food & Agr Sci, Dept Agr Engn, Riyadh 11451, Saudi Arabia

来源：

CURRENT SCIENCE | 2021年 / 121卷 / 03期

关键词：

Agricultural lands; multiple linear regression; satellite data simplified brightness index; soil salinity; ELECTRICAL-CONDUCTIVITY;

D O I：

10.18520/cs/v121/i3/384-390

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Salinity is a critical feature for the management of agricultural soil, particularly in arid and semi-arid areas. The present study was conducted to develop an effective soil salinity prediction model using Sentinel-2A (S2) satellite data. Initially, the collected soil samples were analysed for soil salinity (ECe). Subsequently, multiple linear regression analysis was carried out between the obtained ECe values and S2 data, for the prediction of soil salinity models. The relationship between ECe and S2 data, including individual bands, band ratios and spectral indices showed moderate to highly significant correlations (R-2 = 0.43-0.83). A combination of SWIR-1 band and the simplified brightness index was found to be the most appropriate (R-2 = 0.65; P < 0.001) for prediction of soil salinity. The results of this study demonstrate the ability to obtain reliable estimates of EC using S2 data.

引用

页码：384 / 390

页数：7

共 39 条

[1]

Abbas A, 2007, MODSIM 2007: INTERNATIONAL CONGRESS ON MODELLING AND SIMULATION, P2632

[2] The development of an overlay model to predict soil salinity risks by using remote sensing and GIS techniques: a case study in soils around Idku Lake, Egypt [J].