Leaf water potential of coffee estimated by landsat-8 images

• Published in: PloS one 2020-03, Vol.15 (3), p.e0230013
• Main Authors: Maciel, Daniel Andrade, Silva, Vânia Aparecida, Alves, Helena Maria Ramos, Volpato, Margarete Marin Lordelo, Barbosa, João Paulo Rodrigues Alves de, Souza, Vanessa Cristina Oliveira de, Santos, Meline Oliveira, Silveira, Helbert Rezende de Oliveira, Dantas, Mayara Fontes, Freitas, Ana Flávia de, Carvalho, Gladyston Rodrigues, Oliveira Dos Santos, Jacqueline
• Format: Article
• Language: English
• Subjects: Algorithms; Biology and Life Sciences; Coffea - chemistry; Coffee; Correlation; Correlation analysis; Correlation coefficient; Correlation coefficients; Drought; Drought monitoring; Droughts; Earth Sciences; Ecology and Environmental Sciences; Engineering and Technology; Environmental monitoring; Environmental Monitoring - methods; Hydrogen-Ion Concentration; Landsat; Landsat satellites; Leaf water potential; Leaves; Normalized difference vegetative index; People and places; Physical Sciences; Plant Leaves - chemistry; Plantations; Rain; Reflectance; Remote sensing; Remote Sensing Technology; Research and Analysis Methods; Satellite Imagery; Satellites; Sensors; Technology; Vegetation; Vegetation index; Water; Water - analysis; Water potential
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0230013

Traditionally, water conditions of coffee areas are monitored by measuring the leaf water potential (ΨW) 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 ΨW 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 (R2) 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 R2 of 0.85. Subsequently, the NDVI quadratic algorithm was applied to Landsat-8 images, aiming to spatialize the ΨW estimated in a representative area of regional coffee planting between September 2014 to July 2015. From the proposed algorithm, it was possible to estimate ΨW from Landsat-8/OLI imagery, contributing to drought monitoring in the coffee area leading to cost reduction to the producers.