Abstract
Monitoring agricultural crops is necessary for decision-making in the field. However, it is known that in some regions and periods, cloud cover makes this activity difficult to carry out in a systematic way throughout the phenological cycle of crops. This circumstance opens up opportunities for techniques involving radar sensors, resulting in images that are free of cloud effects. In this context, the objective of this work was to obtain a normalized different vegetation index (NDVI) cloudless product (NDVInc) by modeling Sentinel 2 NDVI using different regression techniques and the Sentinel 1 radar backscatter as input. To do this, we used four pairs of Sentinel 2 and Sentinel 1 images on coincident days, aiming to achieve the greatest range of NDVI values for agricultural crops (soybean and maize). These coincident pairs were the only ones in which the percentage of clouds was not equal to 100% for 33 central pivot areas in western Bahia, Brazil. The dataset used for NDVInc modeling was divided into two subsets: training and validation. The training and validation datasets were from the period from 24 June 2017 to 19 July 2018 (four pairs of images). The best performing model was used in a temporal analysis from 02 October 2017 to 08 August 2018, totaling 55 Sentinel 2 images and 25 Sentinel 1 images. The selection of the best regression algorithm was based on two validation methodologies: K-fold cross-validation (k = 10) and holdout. We tested four modeling approaches with eight regression algorithms. The random forest was the algorithm that presented the best statistical metrics, regardless of the validation methodology and the approach used. Therefore, this model was applied to a time series of Sentinel 1 images in order to demonstrate the robustness and applicability of the model created. We observed that the data derived from Sentinel 1 allowed us to model, with great reliability, the NDVI of agricultural crops throughout the phenological cycle, making the methodology developed in this work a relevant solution for the monitoring of various regions, regardless of cloud cover.
Highlights
It is necessary to adopt more efficient mechanisms in agricultural monitoring to identify vulnerable regions in crops, for the purpose of avoiding economic losses [1]
Remote sensing products have become significant in this matter, since they help with the monitoring of large areas and the identification of crop conditions in a systematic and fast way [2]
This set consists of an normalized different vegetation index (NDVI) image of Sentinel 2 and the backscatter coefficients in the different polarizations of Sentinel 1
Summary
It is necessary to adopt more efficient mechanisms in agricultural monitoring to identify vulnerable regions in crops, for the purpose of avoiding economic losses [1]. Remote sensing products have become significant in this matter, since they help with the monitoring of large areas and the identification of crop conditions in a systematic and fast way [2]. These products allow the estimation of important biophysical parameters for decision-making [3,4,5,6]. There is no satellite, at least with free access, that can monitor the daily temporal frequency and with enough surface detail for farm-level analysis [8]. In order to obtain images with daily frequency and spatial detail, the use of intercalibrations between orbital sensors [9], along with fusion techniques of images from satellites with complementary spatial and temporal characteristics [10,11,12], have been utilized
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