Leaf water potential of coffee estimated by landsat-8 images.

Abstract

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.

Plant phenotyping relevance

Landsat-8反射率・植生指数からコーヒー葉の水ポテンシャルという植物生理形質を推定するアルゴリズムを開発し、交差検証および空間適用を行っており、フェノタイピング手法が中心である。

Code and data availability

The paper reports coffee leaf water potential measurements and Landsat-8/OLI-based regression models, but no author-deposited dataset, code, or model checkpoint with a public URL is provided. The S1 Data (XLSX) supporting file is referenced without a resolvable link in the supplied text, and the Landsat imagery source,

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