# Estimating Double Cropping Plantations in the Brazilian Cerrado through PlanetScope Monthly Mosaics
## 2 Materials and Methods
### Methods  
Figure 3 shows the main steps of our methodological approach. We based our study on six monthly mosaics of PS satellite that were converted into spectral indices and textural attributes based on the Gray-Level Co-occurrence Matrix (GLCM) algorithm. The images were acquired during the 2021/2022 crop growing season. A combination of most relevant multispectral bands, spectral indices, and textural attributes was used as input parameters for the RF classifier [36]. The reference samples for training and validation of RF classification were obtained during two field campaigns conducted in October 2021 and April 2022. We relied on the PS mosaics released by the NICIFI and made available for download in the Google Earth Engine cloud computing platform [37]. The mosaics are prepared primarily to combat deforestation and forest degradation and cover an area limited between 30\({}^{\circ}\) north latitude to 30\({}^{\circ}\) south latitude that corresponds to the world's tropical region [38]. They are composed by the blue (0.455-0.515 \(\upmu\)m), green (0.500-0.590 \(\upmu\)m), red (0.590-0.670 \(\upmu\)m), and NIR (0.780-0.860 \(\upmu\)m) spectral bands and a spatial resolution of 4.77 m. Two mosaics per year were produced between the period of December 2015 to August 2020, becoming monthly since then. In this study, we selected six normalized analytic mosaics from October 2021 to March 2022, which correspond to the crop growing season for most of the crop plantations in the study area. These mosaics are based on the PS surface reflectance data that are atmospherically corrected and normalized to reduce scene-to-scene variability [38]. The seamless are also removed to minimize scene boundaries. According to [PERSON] et al. [38], PS normalized surface reflectance products work well whenever spatial consistency is prioritized and/or training data for machine learning algorithms are considered. Figure 2: Ternary diagram of the soil texture from the municipality of Goiatuba, Goiás State, Brazil. Soil samples were obtained at a soil depth of 0–5 cm in October 2021. For illustration purposes, Figure 4 shows three RGB color composites of PS mosaics of the study area (bands 3, 4, and 2, respectively) from October 2021, January 2022, and March 2022. In the October composite, greenish areas are mostly occupied by either evergreen native vegetation, sugarcane, or irrigated annual crops, while reddish areas correspond mainly to cultivated pastures and rainfed annual crops covered with crop residues. In January, there is a predominance of greenish color which corresponds to the peak of wet season in the study area, when most of the annual crops are also in the peak of crop growing season. In March (end of wet season), the areas with dominant greenish color are reduced in relation to the previous image since not all areas of second cropping are in the peak of growing season. The planting season for the second crop in this municipality usually occurs at the beginning of February (unpublished information provided by local producers). We conducted a field survey in October 2021 to characterize the most representative LULC classes in the study area. We visited 201 sampling points that were previously defined based on visual interpretation of a Landsat 8 RGB color composite of bands 4, 5, and 6 acquired on 27 August 2021. We tried to include all representative LULC classes, both natural Cerrado formations (grasslands, shrublands, and forestlands) and land use classes (cultivated pastures, annual crops, semi-perennial crops, reforestation, and bare soils). These are the representative LULC classes of the municipality, according to the annual LULC maps produced by MapBiomas [6] for the entire country at the 30 m pixel size. Figure 3: Flowchart showing the main steps of the methodological approach considered in this study.