# Estimating Double Cropping Plantations in the Brazilian Cerrado through PlanetScope Monthly Mosaics
## 4 Discussion  
In the municipality of Goiatuba, Goias State, Brazil, we found 1330 rural properties registered in the Brazilian Rural Environmental Registering system (CAR--Cadastro Ambiental Rural) on 8 December 2022. The average size of farms in this municipality was 171 ha [52], much smaller than the average size of farms located in the Brazilian Amazon (e.g., 1737 ha; 615 farms with an average size of 1737 ha in the municipality of Sapezal, Mato Grosso State). This means that the recent investigations, such as the one conducted by [PERSON] et al. [19] in the municipality of Sapezal to distinguish single cropping from double cropping based on the MODIS time series data processed through the TWDTW strategy, may not work properly. In other words, the MODIS data processed by the TWDTW work well in large farms such as those found in agricultural frontiers located in the transition zone between Cerrado and Amazonia biomes, which are the cases of the municipalities of Lucas do Rio Verde, Sapezal, and Soriso, among others. For smaller farms, the much better spatial resolution of PS monthly mosaics (4.77 m) offers a more promising possibility of discriminating single cropping from double cropping with high accuracy. In general, most of the agricultural activities in the Cerrado biome using remote sensing data have been monitored based on optical satellite images with a broad range of spatial resolution (10-250 m), often converted into spectral indices or phenological metrics [5; 53]. To the best of our knowledge, only the study conducted by [PERSON] [53] has used GLCM-related textural features derived from a six-month PS mosaic to produce an LULC map over the western Bahia State, Brazil. The image classification based on this object-based strategy showed higher performance (overall accuracy of 82%) than the classification based on pixel-based strategy (overall accuracy of 67%). The GLCM-derived textural features have been more related to the radar remote sensing and to the very high spatial resolution images obtained by unmanned aerial vehicles (UAVs). There are a number of studies available in the literature using these attributes derived from synthetic aperture radar (SAR) data to identify clear-cut deforested areas, selective logged areas, and different stages of secondary vegetation. The UAV-derived optical images have been more frequently used in the agricultural applications. In theory, GLCM-related textural features derived from both the UAV platform and PS mosaics can provide relevant information for identifying different types of LULC classes in the Cerrado biome, since crop planting rows with varying distance, different levels of biomass, and different azimuth angles, among other aspects, produce distinct textural patterns not only in SAR images, but also in optical images. In the list of our 10 most important input parameters for the RF classification, we found the following textural features: correlation, variance, and mean from the mosaic of February. The selection of most relevant textural features is both site-specific and sensor-specific, as  
Figure 10: Percentage of classification confidence for the 2021/2022 crop growing season in the municipality of Goiatuba, Goias State, Brazil, discriminated by land use and land cover (LULC) classes considered in the Random Forest classification. confirmed by other studies such as the one conducted by [PERSON] et al. [47], who reported contrast and dissimilarity as the most important textural attributes associated with the infrared spectral bands of Landsat 8 and RapidEye satellite images. The selection of most important textural features is crucial to reduce computational demand for running RF and other non-parametric machine learning or deep learning classifiers. Besides, as demonstrated in our study, increasing the number of input parameters for the RF classification does not assure increase in the accuracy of the classification results.