Detecting Soil Salinity in Alfalfa Fields using Spatial Modeling and Remote Sensing

Abstract

A new methodology, which integrates field data, geographic information systems, remote sensing, and spatial modeling, was developed to accurately model soil salinity using statistical tools. Ground data from four alfalfa (Medicago sativa L.) fields in the lower Arkansas River basin in Colorado were compared with data derived from Ikonos satellite images with a 4‐m resolution and Landsat satellite images with a 30‐m resolution. For each image, the combination of satellite image bands that had the best correlation with soil salinity was used. Three statistical models were applied to estimate soil salinity from remote sensing: the ordinary least squares (OLS) model, the spatial autoregressive (spatial AR) model, and a modified residual kriging model. A number of criteria were evaluated to select the best model. The results show that both the Ikonos and Landsat satellite images can be used to estimate soil salinity, and regardless of the source of the satellite image used, the modified kriging model provided the best estimates of soil salinity. Although the OLS model met most of the model selection criteria, in most cases it involved some autocorrelation among the residuals. When the same data were tested using the spatial AR model, most of the autocorrelation among the residuals was removed, but the R2 was reduced. In the modified kriging model, where the kriged residuals were combined with the results of the OLS model, there were significant improvements in the R2 for all cases tested in this study. Thus, this study shows that combining field data, geographic information systems, and remote sensing with strong statistical measures can significantly enhance the development of high‐quality soil salinity maps.

Mapping and Modelling Spatial Variation in Soil Salinity in the Al Hassa Oasis Based on Remote Sensing Indicators and Regression Techniques

“…Nonetheless, differences in spatial resolution can have a high impact on predicting soil salinity. Our finding that the prediction of soil salinity based on IKONOS images yields better results than those based on moderate resolution images is in agreement with Eldeiry and Garcia [19]. Given this concern, it is important to take into account spatial resolution as one of the key factors to consider when using satellite imagery to infer soil salinity.…”

“…), while only in limited studies multispectral high spatial resolution images such as IKONOS, were used [19]. Moreover, several studies have been undertaken for mapping and modelling soil salinity over vegetation species other than date palms, and so far, a limited study has been undertaken to map soil salinity in a primarily date palm region.…”

Soil Salinity Mapping and Monitoring in Arid and Semi-Arid Regions Using Remote Sensing Technology: A Review

“…Their results showed that the use of Landsat ETM+ data bands 4, 5 and 7 in combination with all three types of ancillary data yielded the most accurate soil salinity map, with 83.6% overall accuracy. Additionally, Douaoui et al [55], Farifteh et al [56] and Eldeiry and Garcia [57] agreed that an integrated approach using remote sensing techniques in addition to ancillary data such as field data, topography and spatial models geophysical surveys can improve the development of high quality soil salinity maps. Using multispectral sensors for soil salinity research has also been studied by Goossens et al [58].…”

“…Accordingly, numerous researchers have conducted studies on the mapping and delineation of soil salinity using different Spectral Vegetation Indices (SVI). Among the vegetation indices, NDVI, SAVI, Ratio Vegetation Index (RVI) and Tasseled Cap Transformation that consisted of the Soil Brightness Index (SBI), the Green Vegetation Index (GVI), and the Wetness Index (WI) have been used in soil salinity studies [26,29,30,57,[79][80][81]. Due to absorption in the visible range and high reflectance in the NIR range of the electromagnetic spectrum, the NDVI (Table 1) has been widely used to map soil salinity by monitoring halophytic plants [42,51,79].…”

Concerning the possibilities of successional changes revealing in anthropogenically transformed ecosystems on the base of remote sensing and ground-based survey data integration

“…As for our country, the practice of use time series images in the analysis of changes already finds its application in the monitoring of saline lands, technogenically violated forests, wetlands [8][9][10]. The experience of such researches abroad is also quite rich (see, for example, [11][12][13][14][15]). …”