Next Article in Journal
Ecosystem Services in a Protected Mountain Range of Portugal: Satellite-Based Products for State and Trend Analysis
Next Article in Special Issue
Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review
Previous Article in Journal
Feature Importance Analysis for Local Climate Zone Classification Using a Residual Convolutional Neural Network with Multi-Source Datasets
Previous Article in Special Issue
Improvement of Clay and Sand Quantification Based on a Novel Approach with a Focus on Multispectral Satellite Images
Open AccessArticle

Multi-Temporal Satellite Images on Topsoil Attribute Quantification and the Relationship with Soil Classes and Geology

1
Department of Soil Science, College of Agriculture Luiz de Queiroz, University of São Paulo, Rua Pádua Dias, 11, Piracicaba, Cx Postal 09, São Paulo, CEP 13416900, Brazil
2
Interdisciplinary Program of Bioenergy, University of São Paulo (USP), University of Campinas (UNICAMP) and São Paulo State University (UNESP), Rua Monteiro Lobato, 80, Cidade Universitária, Campinas, SP 13083852, Brazil
3
Faculty of Agronomy and Veterinary Medicine, University of Brasília, Campus Universitário Darcy Ribeiro, ICC Sul, Asa Norte, Cx Postal 4508, Brasília, CEP 70910960, Brazil
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(10), 1571; https://doi.org/10.3390/rs10101571
Received: 27 July 2018 / Revised: 7 September 2018 / Accepted: 26 September 2018 / Published: 1 October 2018
The mapping of soil attributes provides support to agricultural planning and land use monitoring, which consequently aids the improvement of soil quality and food production. Landsat 5 Thematic Mapper (TM) images are often used to estimate a given soil attribute (i.e., clay), but have the potential to model many other attributes, providing input for soil mapping applications. In this paper, we aim to evaluate a Bare Soil Composite Image (BSCI) from the state of São Paulo, Brazil, calculated from a multi-temporal dataset, and study its relationship with topsoil properties, such as soil class and geology. The method presented detects bare soil in satellite images in a time series of 16 years, based on Landsat 5 TM observations. The compilation derived a BSCI for the agricultural sites (242,000 hectare area) characterized by very complex geology. Soil properties were analyzed to calibrate prediction models using 740 soil samples (0–20 cm) collected of the area. Partial least squares regression (PLSR) based on the BSCI spectral dataset was performed to quantify soil attributes. The method identified that a single image represents 7 to 20% of bare soil while the compilation of the multi-temporal dataset increases to 53%. Clay content had the best soil attribute prediction estimates (R2 = 0.75, root mean square error (RMSE) = 89.84 g kg−1, and accuracy = 74%). Soil organic matter, cation exchange capacity and sandy soils also achieved moderate predictions. The BSCI demonstrates a strong relationship with legacy geological maps detecting variations in soils. From a single composite image, it was possible to use spectroscopy to evaluate several environmental parameters. This technique could greatly improve soil mapping and consequently aid several applications, such as land use planning, environmental monitoring, and prevention of land degradation, updating legacy surveys and digital soil mapping. View Full-Text
Keywords: soil attribute mapping; Landsat TM; bare soil; digital soil mapping; spectral sensing; satellite; soil and food security soil attribute mapping; Landsat TM; bare soil; digital soil mapping; spectral sensing; satellite; soil and food security
Show Figures

Graphical abstract

MDPI and ACS Style

Gallo, B.C.; Demattê, J.A.M.; Rizzo, R.; Safanelli, J.L.; Mendes, W.D.S.; Lepsch, I.F.; Sato, M.V.; Romero, D.J.; Lacerda, M.P.C. Multi-Temporal Satellite Images on Topsoil Attribute Quantification and the Relationship with Soil Classes and Geology. Remote Sens. 2018, 10, 1571.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop