Open AccessLeaf water potential of coffee estimated by landsat-8 images
Author(s) -
Daniel Andrade Maciel,
Vânia Aparecida Silva,
Helena Maria Ramos Alves,
Margarete Marin Lordelo Volpato,
João Paulo Rodrigues Alves de Barbosa,
Vanessa Aparecida Feijó de Souza,
Meline Oliveira Santos,
Helbert Rezende de Oliveira Silveira,
Mayara Fontes Dantas,
Ary Ferreira de Freitas,
Gladyston Rodrigues Carvalho,
Jacqueline Oliveira dos Santos
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0230013
Subject(s) - normalized difference vegetation index , vegetation (pathology) , context (archaeology) , remote sensing , correlation coefficient , enhanced vegetation index , environmental science , reflectivity , mathematics , mean absolute percentage error , vegetation index , statistics , mean squared error , leaf area index , geography , agronomy , biology , medicine , physics , archaeology , optics , pathology
Traditionally, water conditions of coffee areas are monitored by measuring the leaf water potential (Ψ W ) throughout a pressure pump. However, there is a demand for the development of technologies that can estimate large areas or regions. In this context, the objective of this study was to estimate the Ψ W by surface reflectance values and vegetation indices obtained from the Landsat-8/OLI sensor in Minas Gerais—Brazil Several algorithms using OLI bands and vegetation indexes were evaluated and from the correlation analysis, a quadratic algorithm that uses the Normalized Difference Vegetation Index (NDVI) performed better, with a correlation coefficient (R 2 ) of 0.82. Leave-One-Out Cross-Validation (LOOCV) was performed to validate the models and the best results were for NDVI quadratic algorithm, presenting a Mean Absolute Percentage Error (MAPE) of 27.09% and an R 2 of 0.85. Subsequently, the NDVI quadratic algorithm was applied to Landsat-8 images, aiming to spatialize the Ψ W estimated in a representative area of regional coffee planting between September 2014 to July 2015. From the proposed algorithm, it was possible to estimate Ψ W from Landsat-8/OLI imagery, contributing to drought monitoring in the coffee area leading to cost reduction to the producers.