Prediction of soil texture using remote sensing data. a systematic review
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Date
2024-09
Journal Title
Journal ISSN
Volume Title
Publisher
Department of Soil and Geological Sciences
Abstract
Soil particle size fractions play a critical role in determining soil health attributes,
including soil aeration, water infiltration and retention capacity, nutrients, and
organic matter dynamics. Traditional soil mapping methods rely predominantly
on ground-based surveys and laboratory analysis which are reported to be time-
consuming and expensive. To address these challenges, there has been a global
shift towards digital soil mapping (DSM) techniques that utilize remote sensing
data. This review, conducted according to the Preferred Reporting Items for
Systematic Reviews and Meta-Analyses (PRISMA) 2020 guideline, aims to provide
a comprehensive synthesis of the current state of soil texture prediction using
remote sensing data. In particular, the review extract and synthesizes the satellite
images used, identify the derived environmental covariates and their relative
importance, and assesses the prediction models/algorithms used in the
prediction of soil texture. Synthesis and analysis of 70 articles show that clay
content is the most predicted of the three soil particle fractions accounting for
37% of the reviewed studies predominantly from topsoil layer (74.29%). Sentinel
2 and Landsat 8 are reported as the most frequently used satellite images. Among
the covariates derived from these images, NDVI (80.4%) and SAVI (60.8%) are by
far the most derived band ratios (indices). Red (37.3%), NIR (35.3%), Green (33.3%),
Blue (33.3%), and SW2 (29.4%) bands were the five most incorporated as
covariates for soil texture prediction amongst individual satellite bands.
Regarding the DSM algorithms, Random Forest (RF) appeared in most
reviewed articles followed by Support Vector Machines (SVM), and Quantile
Regression Forest (QRF). The comparative model performance analysis
showed that RF and Artificial neural network (ANN) had a good trade-off
across validation metrics indicating their best performance in the prediction of
both clay, sand, and silt. The RF performance showed a decreasing trend with
increasing depth interval for clay and sand prediction and inconsistent for silt
prediction.
Description
doi: 10.3389/frsen.2024.1;18
Keywords
digital soil mapping, environmental covariates, machine learning, satellite images, spatial prediction
Citation
Front. Remote Sens. 5:1461537.