Pós-Graduação/Sensoriamento Remoto, Instituto Nacional de Pesquisas Espaciais, São José dos Campos, SP, Brasil.
Empresa de Pesquisa Agropecuária de Minas Gerais, Lavras, MG, Brazil.
PLoS One. 2020 Mar 18;15(3):e0230013. doi: 10.1371/journal.pone.0230013. eCollection 2020.
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.
传统上，通过在压力泵整个过程中测量叶片水势（ΨW）来监测咖啡区的水分条件。但是，人们需要开发可以估算大面积或区域的技术。在这种情况下，本研究的目的是利用从巴西米纳斯吉拉斯州获取的 Landsat-8/OLI 传感器的表面反射率值和植被指数来估算 ΨW。评估了几种使用 OLI 波段和植被指数的算法，通过相关分析，使用归一化差异植被指数（NDVI）的二次算法表现更好，相关系数（R2）为 0.82。进行了留一法交叉验证（LOOCV）以验证模型，NDVI 二次算法的结果最好，平均绝对百分比误差（MAPE）为 27.09％，R2 为 0.85。随后，将 NDVI 二次算法应用于 Landsat-8 图像，旨在对 2014 年 9 月至 2015 年 7 月之间具有代表性的地区咖啡种植区域内估算的 ΨW 进行空间化。通过所提出的算法，可以从 Landsat-8/OLI 图像中估算 ΨW，有助于监测咖啡区的干旱情况，从而降低生产者的成本。
