# Estimating Double Cropping Plantations in the Brazilian Cerrado through PlanetScope Monthly Mosaics
## 1 Introduction
[19], among others, proposed the use of the Time-Weighted Dynamic Time Warping (TWDTW) method applied to the MODIS data to identify large-scale crop successions and rotations in an ecotone region between Cerrado and Amazon in the Mato Grosso State, Brazil (soybean as the first crop and maize and millet as the second crop), with promising results. The rationale is the use of logistic functions to find the number of peaks in spectral indices in a specific crop calendar. However, differences in climate, crop type, and planting dates can cause differences in crop growth patterns, affecting the accuracy significantly. Another drawback is the 250 m coarse resolution of the MODIS sensor, which is quite sensitive to the occurrences of multiple targets within a single pixel. More recently, NASA proposed the Harmonized Landsat and Sentinel-2 (HLS) initiative to produce analysis-ready, surface reflectance images acquired by the Landsat Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) sensors at a 2-4 day frequency and a 10-30 m spatial resolution [20]. Therefore, in theory, these images are more suitable for mapping crop intensification besides presenting the possibility of mapping smaller areas (typically, >2-3 ha), as demonstrated by the studies conducted, for example, by [PERSON] et al. [21] and [PERSON] et al. [22]. [PERSON] et al. [23] also demonstrated the potential of HLS to monitor specific crop (soybean) in the western Bahia State, Brazil. However, monitoring the double cropping system in the Cerrado biome using optical remote sensing data is not an easy task, mainly because of the persistent cloud coverage during the rainy season, even if the frequency of data acquisition is reduced to 2-4 days. With the launch of the PlanetScope (PS) constellation of nanosatellites, we have the opportunity to use the near daily-based satellite data with a pixel size of \(\sim\)5 m to monitor rainfed crop production with a reasonable number of cloud-free images during the crop growing season. The PS satellite constellation that was launched by the Planetis composed of more than 200 CubeSats that acquire daily images in four multispectral bands in the blue, green, red, and near-infrared (NIR) spectral regions [24]. The monthly mosaics of PS, released by Norway's International Climate & Forests Initiative (NICFI) [25], correspond to the combination of the best daily acquisitions during the month so that they are mostly cloud-free. Consequently, the NICFI images provide the best available multispectral data set to map double cropping systems in tropical regions. Several studies have already demonstrated the potential of PS monthly mosaics for different applications, including tree cover monitoring over tropical regions, based on the U-net deep learning model [26], cropland mapping in smallholder landscapes in Mozambique, based on the Random Forest (RF) machine learning model [27], planted Eucalyptus mortality in Mato Grosso State, Brazil, the logistic regression model [28], and land-use following deforestation in Ethiopia based on the U-net model [29], among others. However, as pointed out by [PERSON] et al. [26], the main drawback of NICFI mosaics is the variation in the reflectance values between different Planet satellite sensors and between dates within the same mosaics. As reported in the NICFI documentation [25], the absolute radiometric calibration is not guaranteed in the normalized surface reflectance base maps. Therefore, it seems that the performance of PS monthly mosaics for different applications is site-specific. In this study, we assessed the potential of PS monthly mosaics acquired from October 2021 to April 2022 (rainy season) to identify double cropping systems and other land use and land cover (LULC) classes in the municipality of Goiatuba, southwestern Goias State, Brazil. We selected this study area because it is representative of the Cerrado's municipalities presenting large areas with double cropping systems and because of its regional socio-economic importance.