Mapping agricultural intensification in the Brazilian savanna: a machine learning approach using harmonized data from Landsat Sentinel-2.

• Autor(a) principal: BOLFE, E. L.
• Data de Publicação: 2023
• Outros Autores: PARREIRAS, T. C., SILVA, L. A. P. da, SANO, E. E., BETTIOL, G. M., VICTORIA, D. de C., DEL'ARCO SANCHES, I., VICENTE, L. E.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
• Texto Completo: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1155214; https://doi.org/10.3390/ijgi12070263
• topic: Multisensor; Inteligência artificial; Aprendizado de máquina; Intensificação agrícola; Mapeamento agrícola; Harmonized Landsat Sentinel-2; HLS; Machine learning; Agricultural Intensification; Agricultura; Sensoriamento Remoto; Cerrado; Agriculture; Remote sensing; Artificial intelligence

Agricultural intensification practices have been adopted in the Brazilian savanna (Cerrado), mainly in the transition between Cerrado and the Amazon Forest, to increase productivity while reducing pressure for new land clearing. Due to the growing demand for more sustainable practices, more accurate information on geospatial monitoring is required. Remote sensing products and artificial intelligence models for pixel-by-pixel classification have great potential. Therefore, we developed a methodological framework with spectral indices (Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Soil-Adjusted Vegetation Index (SAVI)) derived from the Harmonized Landsat Sentinel-2 (HLS) and machine learning algorithms (Random Forest (RF), Artificial Neural Networks (ANNs), and Extreme Gradient Boosting (XGBoost)) to map agricultural intensification considering three hierarchical levels, i.e., temporary crops (level 1), the number of crop cycles (level 2), and the crop types from the second season in double-crop systems (level 3) in the 2021-2022 crop growing season in the municipality of Sorriso, Mato Grosso State, Brazil. All models were statistically similar, with an overall accuracy between 85 and 99%. The NDVI was the most suitable index for discriminating cultures at all hierarchical levels. The RF-NDVI combination mapped best at level 1, while at levels 2 and 3, the best model was XGBoost-NDVI. Our results indicate the great potential of combining HLS data and machine learning to provide accurate geospatial information for decision-makers in monitoring agricultural intensification, with an aim toward the sustainable development of agriculture.