# Estimating Double Cropping Plantations in the Brazilian Cerrado through PlanetScope Monthly Mosaics
## 2 Materials and Methods
### Methods
NCIFI = Norway ’s International Climate and Forests Initiative; NDVI = Normalized Difference Vegetation Index; EVI = Enhanced Vegetation Index; GRNDI = Green–Red Normalized Difference Index; and GLCM = Gray-Level Co-occurrence Matrix. For each site, we recorded its coordinates and LULC class and obtained panoramic pictures using an RGB digital camera with a resolution of 7.1 MPixel. Field data were collected using the AgroTag platform, developed by Embrapa Meio Ambiente, Jaguariuna, SP, Brazil, for recording georeferenced field data. AgroTag has an integrated WebGIS interface that allows access and analysis of data registered in this platform [39]. In the second field trip, conducted in April 2022, we revisited all points classified as annual crops and bare soils in October 2021 to estimate the level of adoption of double cropping by farmers as well as to record the type of second crop adopted by farmers. In the Goiatuba municipality, the most common second crops are maize and sorghum. Field data collected in this study are available upon request to the corresponding author. Besides the four spectral bands of PS images converted into normalized surface reflectance, we included multitemporal information derived from vegetation indices and textural metrics as input parameters for the RF classification. We calculated three vegetation indices (VIs): Normalized Difference Vegetation Index (NDVI) [40] (Equation (1)), Enhanced  
Figure 4: Monthly mosaics of PlanetScope RGB color composites of bands 3 (red), 4 (near-infrared), and 2 (green) from the municipality of Goiatuba, Goiás State, Brazil from October 2021 (**A**), January 2022 (**B**), and March 2022 (**C**). Vegetation Index (EVI) [41] (Equation (2)), and Green-Red Normalized Difference Index (GRNDI) [42] (Equation (3)). \[NDVI=\frac{\rho_{NIR}-\rho_{Red}}{\rho_{NIR}+\rho_{Red}} \tag{1}\]  
\[EVI=2.5\ \times\ \frac{\rho_{NIR}-\rho_{Red}}{\rho_{NIR}+6\ \times\ \rho_{ Red}-7.5\ \times\ \rho_{Blue}+1} \tag{2}\]  
\[GRNDI=\frac{\rho_{Green}-\rho_{Red}}{\rho_{Green}+\rho_{Red}} \tag{3}\]  
where \(\rho\) is the surface reflectance in the blue, green, red, and NIR spectral bands. These VIs take into consideration different combinations of PS spectral bands positioned in the visible and NIR wavelengths. NDVI is the most popular VI in the literature and depicts differences in photosynthetic activities in the plant canopies while EVI was developed to reduce NDVI-related constraints of signal saturation over dense vegetation cover as well as to minimize its soil and atmosphere effects [12; 41]. GRNDI may detect seasonal changes in old/new foliage associated with seasonal changes of native vegetation [43] or crop growing processes. We also derived textural features from the four spectral bands of the PS images based on the GLCM [44]. These attributes have been used by several authors for LULC classification in different landscapes and data sets [45; 46]. The GLCM requires the setting of four parameters by users: Kernel's window size, spectral band, level of quantization, and direction. Small windows (e.g., 3 \(\times\) 3) may amplify noise while large windows (e.g., 31 \(\times\) 31) may over-smooth texture [47]. However, other studies reported improvements in the crop classification when textural features derived from larger Kernel sizes were used [48]. Based on preliminary tests involving a randomly selected smaller area in the municipality of Goiatuba, we selected the following parameters: window size of 7 \(\times\) 7, all four spectral bands, level of quantization of 64 bits, and azimuth direction of 45\({}^{\circ}\). From this matrix, it is possible to extract 10 textural features from, for example, the open-source Sentinel Application Platform (SNAP) image processing software developed by the European Space Agency (ESA). However, some of these features may present redundant spatial context information.