Abstract
The mapping of soil nutrients is a key issue for numerous applications and research fields ranging from global changes to environmental degradation, from sustainable soil management to the precision agriculture concept. The characterization, modeling and mapping of soil properties at diverse spatial and temporal scales are key factors required for different environments. This paper is focused on the use and comparison of soil chemical analyses, Visible near infrared and shortwave infrared VNIR-SWIR spectroscopy, partial least-squares regression (PLSR), Ordinary Kriging (OK), and Landsat-8 operational land imager (OLI) images, to inexpensively analyze and predict the content of different soil nutrients (nitrogen (N), phosphorus (P), and potassium (K)), pH, and soil organic matter (SOM) in arid conditions. To achieve this aim, 100 surface samples of soil were gathered to a depth of 25 cm in the Wadi El-Garawla area (the northwest coast of Egypt) using chemical analyses and reflectance spectroscopy in the wavelength range from 350 to 2500 nm. PLSR was used firstly to model the relationship between the averaged values from the ASD spectroradiometer and the available N, P, and K, pH and SOM contents in soils in order to map the predicted value using Ordinary Kriging (OK) and secondly to retrieve N, P, K, pH, and SOM values from OLI images. Thirty soil samples were selected to verify the validity of the results. The randomly selected samples included the spatial diversity and characteristics of the study area. The prediction of available of N, P, K pH and SOM in soils using VNIR-SWIR spectroscopy showed high performance (where R2 was 0.89, 0.72, 0.91, 0.65, and 0.75, respectively) and quite satisfactory results from Landsat-8 OLI images (correlation R2 values 0.71, 0.68, 0.55, 0.62 and 0.7, respectively). The results showed that about 84% of the soils of Wadi El-Garawla are characterized by low-to-moderate fertility, while about 16% of the area is characterized by high soil fertility.
Highlights
Soil is a very complex ecosystem made up of biotic and abiotic factors that strongly differ from one environment to another
The partial least-squares regression (PLSR) was applied to the reflectance spectra measured in the arid conditions of Wadi El-Garawla to model, predict, and map the available N, P, and K, pH, and soil organic matter (SOM) from in situ analysis, Vis-NEARNEAR spectroscopy, and satellite operational land imager (OLI) data
These results demonstrate the effectiveness of satellite images (OLI) in predicting different soil properties, and these results are consistent with other independent investigations [18]
Summary
Soil is a very complex ecosystem made up of biotic and abiotic factors that strongly differ from one environment to another. Today, one of the major challenges to be faced is the need to develop low-cost methods for mapping soil properties over large areas and, on the other hand, it is important to consider that agricultural management needs a rapid analysis to identify the deficiency of elements in the soil and crops. To cope with this issue, Vis-NIR reflectance spectroscopy coupled with satellite data can suitably complement in situ analyses [29,30]. The soils were classified in two orders—Entisols and Aridisols—and divided into five subgroups: Typic Calcigypsids, Typic Haplogypsids, Typic Haplocalcids, Typic TorriPsamments, and Lithic Torriorthents [39]
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