In Brazil there is a need for less subjective, more efficient and less expensive methodologies for crop yield forecast. Owing to the continental dimensions of the country, orbital images have been used to estimate the productive potential of crops. In this study, NDVI (Normalized Difference Vegetation Index) time-series, derived from AVHRR/NOAA (Advanced Very High Resolution Radiometer/National Oceanic and Atmospheric Administration) imagery were used for the soybean crop monitoring in a large production region in Brazil in the 2002/2003 and 2003/2004 cropping seasons. NDVI temporal profiles describing the biomass condition of crops throughout the phenological stages were generated in 18 municipalities. Quantitative parameters were measured from the temporal profiles, based on the full time or partial phenological cycle. Linear regressions between the quantitative parameters and the municipal average yields in both seasons have shown that the most significant correlations occurred when the full time period was considered. When considering periods prior to harvest, the correlations showed a tendency to decline. The NDVI monitoring during these two cropping seasons, which presented different weather conditions, could explain a major part of the soybean yield variability at the municipal level. Results showed the potential of the NDVI time-series analysis in generating parameters to be employed by agrometeorological–spectral models for soybean yield estimations. The automatic system for temporal profiles generation developed in this study sped up the analysis and can be used for further studies at a regional scale.

Fast and accurate quantification of the available pasture biomass is essential to support grazing management decisions in intensively managed fields. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely sensed data. Here, we assessed the feasibility of using spectral and textural information derived from PlanetScope imagery for estimating pasture aboveground biomass (AGB) and canopy height (CH) in intensively managed fields and the potential for enhanced accuracy by applying the extreme gradient boosting (XGBoost) algorithm. Our results demonstrated that the texture measures enhanced AGB and CH estimations compared to the performance obtained using only spectral bands or vegetation indices. The best results were found by employing the XGBoost models based only on texture measures. These models achieved moderately high accuracy to predict pasture AGB and CH, explaining 65% and 89% of AGB (root mean square error (RMSE) = 26.52%) and CH (RMSE = 20.94%) variability, respectively. This study demonstrated the potential of using texture measures to improve the prediction accuracy of AGB and CH models based on high spatiotemporal resolution PlanetScope data in intensively managed mixed pastures.

Currently, Brazil is the leading producer of sugarcane in the world, with self-sufficiency in the use of ethanol as a biofuel, as well as being one of the largest suppliers of sugar to the world. This study aimed to develop a predictive model for sugarcane production based on data extracted from aerial imagery obtained from drones or satellites, allowing the precise tracking of plant development in the field. A model based on a semiparametric approach associated with the inverse Gaussian distribution applied to vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI) and Visible Atmospherically Resistant Index (VARI), was developed with data from drone images obtained from two field experiments with randomized replications and four sugarcane varieties. These experiments were performed under conditions identical to those applied by sugarcane farmers. Further, the model validation was carried out by scaling up the analyses with data extracted from Sentinel-2 images of several commercial sugarcane fields. Very often, in countries such as Brazil, sugarcane crops occupy extensive areas. Consequently, the development of tools capable of being operated remotely automatically benefits the management of this crop in the field by avoiding laborious and time-consuming sampling and by promoting the reduction of operation costs. The results of the model application in both sources of data, i.e., data from field experiments as well as the data from commercial fields, showed a suitable level of overlap between the data of predicted yield using VIs generated from drone and satellite images with the data of verified yield obtained by measuring the production of experiments and commercial fields, indicating that the model is reliable for forecasting productivity months before the harvest time.