A semi-automated processing chain for LULC mapping via CBERS-4 data cubes and spectral indices

Timely and accurate land use and land cover (LULC) maps are essential to support decision-making and implement policies. The China–Brazil Earth Resources Satellite (CBERS) Program was launched to provide data for decision-makers to manage the Brazilian territory. Their data, especially from CBERS-4 Wide-Field Imager (CBERS-4/WFI), are broadly used in deforestation monitoring by remote sensing specialists but less used than data from other image providers for machine learning-based LULC mapping due to the small number of spectral bands and limitations related to clouds and shadows detection. However, with advances in orbital data analysis, the management of CBERS-4/WFI images as data cubes enabled storing and accessing big spatio-temporal analysis-ready data. Brazil Data Cube (BDC) produces multidimensional data cube collections from different medium-resolution satellite data for Brazil, including CBERS-4/WFI. Given the demand for optimizing the generation of accurate information and to answer how to detect subtle variations across harvest periods with high accuracy (improving monitoring initiatives), we present a low-cost, semiautomated, accessible, and robust processing chain (classification scheme) created to generate LULC classifications with few training samples/calibration. It congregates steps from the insertion of LULC samples to the accuracy assessment of the LULC classification, and the core is a dense time series analysis approach from EO data cubes and the Surface Reflectance to Vegetation Indexes (sr2vgi), a tool to automate spectral index calculation. The technical highlights of this processing chain are the detection of interannual changes in management and monitoring with crops still on the stands (intra-harvest monitoring approach). As a proof-of-concept, we used this processing chain to generate LULC maps for the Brazilian Cerrado agricultural belt. The results, presented in the paper entitled CBERS data cubes for land use and land cover mapping in the Brazilian Cerrado agricultural belt, indicate the potential of the approach to provide timely and accurate LULC mapping by detecting different vegetation patterns in CBERS-4/WFI derived time series. Moreover, this processing chain was presented in the course Aggregating crop calendar knowledge and Sentinel-2/MSI big data for crop monitoring, ministered by Michel Chaves at the XX Brazilian Symposium on Remote Sensing (XX SBSR), 2023.