In:
International Journal of Design & Nature and Ecodynamics, International Information and Engineering Technology Association, Vol. 17, No. 5 ( 2022-10-31), p. 761-766
Abstract:
Hydrological soil group is essential to soil information for several fields of modeling and applications. This information can affect suitable environmental, agricultural, and hydrological development. Laboratory analysis for soil sampling cannot efficiently provide the needed information because these analyses are commonly costly, time-consuming, and limited in retrieving the temporal and spatial variability. In this context, remote sensing is now solid to offer meaningful spatial data for studying soil characteristics on various spatial scales utilizing the different spectral reflectance. For this study, the integration of Geographic Information System (GIS) remote sensing data and survey data with the Artificial Neural Network (ANN) were used to generate a hydrological soil group map and to infer spatial patterns of soils across complete area converges for Alghadaf Wadi in the Western Desert of Iraq. The generated soil information was tested based on the sand, silt, and clay content. The testing result indicated that the differences between actual and predicted values to determine soil classes are agreed well. Therefore, this method is vital for mapping and monitoring soil texture by providing timely, fast repetitive data and relatively cheap.
Type of Medium:
Online Resource
ISSN:
1755-7437
,
1755-7445
DOI:
10.18280/ijdne.170514
Language:
Unknown
Publisher:
International Information and Engineering Technology Association
Publication Date:
2022
detail.hit.zdb_id:
2492146-4
SSG:
21
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