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The robust and operative estimation of water requirements in crops is a premise that water management specialists must fulfill. The aim of this study was to implement the Bayesian approach (EB) to estimate the crop coefficient (Kc) from the normalized difference vegetation index (NDVI), and to compare them to the frequentist approaches, general and specific equations for a crop. The Bayesian model was developed based on probability distributions of parameters (a and b) a priori, gathered from 18 studies, which estimated the linear relation (Kc-NDVI). In the first stage, the approaches were compared in seven sample sizes (TM) (5, 10, 30, 60, 90, 120 and 150), in which 1000 repetitions were carried out without replacements. In order to validate the adjustments of each TM, a base of 156 experimental data from Kc-NDVI were used. In the second stage, the EB was evaluated regarding equations adjusted to specific crops, and the first and second quartiles of the data were added to perform validation. Results first showed that the EB surpassed the frequentist methods and general equations in all TM. EB obtained low uncertainties with only five TM, unlike the other methods, which needed more than 30 records to obtain similar results. The comparison between EB and specific equations corroborated that five random data gathered in the first quartile were enough to obtain low uncertainties. This proposed methodology is operative and the Kc data can be estimated from the first phenological stages with a high certainty and few NDVI data. [ABSTRACT FROM AUTHOR]

In this paper, we evaluated satellite images from Sentinel 2 to estimate the severity of needle cast in pine and field evaluations. Three indexes were used: a) Normalized Difference Vegetation Index (NDVI), b) Moisture Stress Index (MSI), and c) Soil-adjusted Vegetation Index (SAVI). These indexes were obtained from the combination of satellite bands acquired monthly during February to July 2017. The values obtained by the indexes were correlated with the severity of needle cast of pine, estimated in 24 sampling sites. The values obtained from MSI correlated positively with the observed values of severity (0.70783, p<0.0001), the values obtained from NDVI had a moderate positive correlation with severity (0.53316, p<0.0001). Nevertheless, the data obtained from SAVI had a low positive correlation with severity (0.24844, p=0.0062). The results showed that the use of satellite images from Sentinel 2 and MSI can be used like a tool for monitoring the severity of Lophodermium sp. in pine forest. [ABSTRACT FROM AUTHOR]

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