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.

Integrated crop–livestock systems (ICLS) are among the main viable strategies for sustainable agricultural production. Mapping these systems is crucial for monitoring land use changes in Brazil, playing a significant role in promoting sustainable agricultural production. Due to the highly dynamic nature of ICLS management, mapping them is a challenging task. The main objective of this research was to develop a method for mapping ICLS using deep learning algorithms applied on Satellite Image Time Series (SITS) data cubes, which consist of Sentinel-2 (S2) and PlanetScope (PS) satellite images, as well as data fused (DF) from both sensors. This study focused on two Brazilian states with varying landscapes and field sizes. Targeting ICLS, field data were combined with S2 and PS data to build land use and land cover classification models for three sequential agricultural years (2018/2019, 2019/2020, and 2020/2021). We tested three experimental settings to assess the classification performance using S2, PS, and DF data cubes. The test classification algorithms included Random Forest (RF), Temporal Convolutional Neural Network (TempCNN), Residual Network (ResNet), and a Lightweight Temporal Attention Encoder (L-TAE), with the latter incorporating an attention-based model, fusing S2 and PS within the temporal encoders. Experimental results did not show statistically significant differences between the three data sources for both study areas. Nevertheless, the TempCNN outperformed the other classifiers with an overall accuracy above 90% and an F1-Score of 86.6% for the ICLS class. By selecting the best models, we generated annual ICLS maps, including their surrounding landscapes. This study demonstrated the potential of deep learning algorithms and SITS to successfully map dynamic agricultural systems.

Cotton is the most important fibre culture in the world. In Brazil, cotton cultivation is concentrated in the Cerrado biome, the Brazilian savanna, and is one of the most important commodities in the country. As an annual crop, the updating frequency of the spatial distribution data of cotton fields is extremely important for crop monitoring systems. In order to provide fast and accurate information for crop monitoring, time series of remote- sensing data has been used in the development of several applications in agriculture, since the high temporal resolution of some orbital sensor allows monitoring targets with high spectral-temporal variations in the land surface. However, there are still some challenges to systematize the processing of such a large amount of data available by long time series of remote-sensing imagery. Thus, this study contributes to the construction of models to identify and separate specific crop types with similar spectral behaviour to other crops practised in the same period. The objective of this study was to develop a systematic methodology based on data mining of time series of vegetation indices (VI) to map cotton fields at the regional scale. Field reference data and time series of NDVI and EVI images, obtained from MODIS sensor products during four cropping seasons (from 2012–2013 to 2015–2016), were used to construct mapping models based on decision tree algorithms. Phenological metrics were calculated from the VI time series and used to build classification rules for mapping cotton fields. Our results demonstrate that the proposed method to map cotton fields achieve high accuracy when field data and visual interpretation of NDVI temporal profiles were used for validation (accuracy higher than 95% and 93%, respectively). Comparisons with the official statistics indicated an optimal fit, with linear correlation (r) and coefficient of determination (R²) above 0.93. Therefore, the proposed method was efficient to distinguish cotton fields from other crop types with similar spectral behaviour. In addition, this method can also be applied to other cotton-producing regions and other production seasons, by reusing the models generated through machine learning approaches.

Regenerative agricultural practices are a suitable path to feed the global population. Integrated Crop–livestock systems (ICLSs) are key approaches once the area provides animal and crop production resources. In Brazil, the expectation is to increase the area of ICLS fields by 5 million hectares in the next five years. However, few methods have been tested regarding spatial and temporal scales to map and monitor ICLS fields, and none of these methods use SAR data. Therefore, in this work, we explored the potential of three machine and deep learning algorithms (random forest, long short-term memory, and transformer) to perform early-season (with three-time windows) mapping of ICLS fields. To explore the scalability of the proposed methods, we tested them in two regions with different latitudes, cloud cover rates, field sizes, landscapes, and crop types. Finally, the potential of SAR (Sentinel-1) and optical (Sentinel-2) data was tested. As a result, we found that all proposed algorithms and sensors could correctly map both study sites. For Study Site 1(SS1), we obtained an overall accuracy of 98% using the random forest classifier. For Study Site 2, we obtained an overall accuracy of 99% using the long short-term memory net and the random forest. Further, the early-season experiments were successful for both study sites (with an accuracy higher than 90% for all time windows), and no significant difference in accuracy was found among them. Thus, this study found that it is possible to map ICLSs in the early-season and in different latitudes by using diverse algorithms and sensors.

MODIS time series carries valuable long-term data essential to support several studies such as biogeochemical modelling. However, there is a lack of validation studies applying MODIS data at a fine-scale to monitor pasture management practices. In this study, we assessed the potential of MODIS sensor in monitoring at a fine-scale four intensively managed mixed-pastures fields located in São Paulo State, Brazil. The MODIS spectral response was compared with Sentinel-2, and the ability of the two sensors in predicting aboveground biomass (AGB) and canopy height (CH) was assessed using the Random Forest algorithm. EVI images from MODIS and Sentinel-2 were correlated with field measurements of AGB and CH. The prediction performance of AGB (R²: Sentinel-2 = 38%; MODIS = 42%) and CH (R²: Sentinel-2 = 69%; MODIS = 85%) models was superior using EVI data from MODIS than Sentinel-2, highlighting MODIS ability to monitor small and intensively managed pasture fields.