Avaliação da sustentabilidade hídrica da cultura canavieira através do uso de indicadores extraídos de modelos espaciais Water sustainability assessment for sugarcane based on spatial indicators RESUMO

National audienceThis article aimed to assess the sugarcane expansion process in four microregionsconsidered sugarcane expansion hotspots in the State of Goiás, Brazil, from the use ofindicators proposed by the Indicators System of Sugarcane Water Sustainability Assessment- SISH-Cana (Ferraz, 2012) Thus, it became possible to perform an analytical andcomparative evaluation of the sugarcane expansion process occurred in 2005/2006 to2010201 period, in the following microregions of South of the State of Goiás, Brazil:Sudoeste de Goiás, Vale do Rio dos Bois, Quirinópolis and Meia Ponte, adopted as territorialunits of analysis. The results of the indicators suggest that the proportions of sugarcaneoccupation in microregions studied are still relatively small in relation to the total areas oftheir territories. They also suggest that the sugar-alcohol sector has expanded mainly on themost appropriate areas, precisely those with slope gradients lower than 12 and with soilswith preferential agricultural suitability, where it becomes possible to develop sugarcaneactivity with high technological level. They have also shown that sugarcane expansionprocess is occurring primarily on areas traditionally occupied with agriculture, but alsopointed out the trend of gradual pastures replacement, while the native vegetation removalis relatively small. In relation to the balance between demand and water availability, theresults demonstrated the sugarcane culture still has a high possibility to develop in themicroregions considered

Assessing the optimal preprocessing steps of MODIS time series to map cropping systems in Mato Grosso, Brazil

The adoption of new cropping practices such as integrated Crop-Livestock systems (iCL) aims at improving the land use sustainability of the agricultural sector in the Brazilian Amazon. The emergence of such integrated systems, based on crop and pasture rotations over and within years, challenges the remote sensing community who needs to implement accurate and efficient methods to process satellite image time series (SITS) in order to come up with a monitoring protocol. These methods generally include a SITS preprocessing step which can be time consuming. The aim of this study is to assess the importance of preprocessing operations such as temporal smoothing and computation of phenological metrics on the mapping of main cropping systems (i.e. pasture, single cropping, double cropping and iCL), with a special emphasis on the iCL class. The study area is located in the state of Mato Grosso, an important producer of agriculture commodities located in the Southern Brazilian Amazon. SITS were composed of a set of 16-day composites of MODIS Vegetation Indices (MOD13Q1 product) covering a one year period between 2014 and 2015. Two widely used classifiers, i.e. Random Forest (RF) and Support Vector Machine (SVM), were tested using five data sets issued from a same SITS but with different preprocessing levels: (i) raw NDVI; (ii) raw NDVI + raw EVI; (iii) smoothed NDVI; (iv) NDVI-derived phenometrics; (v) raw NDVI + phenometrics. Both RF and SVM classification results showed that the “raw NDVI + raw EVI” data set achieved the highest performance (RF OA = 0.96, RF Kappa = 0.94, SVM OA = 0.95, SVM Kappa = 0.93), followed closely by the “raw NDVI” and the “raw NDVI + phenometrics” datasets. The “NDVI-derived phenometrics” alone achieved the lowest accuracies (RF OA = 0.58 and SVM OA = 0.66). Considering that the implementation of preprocessing steps is computationally expensive and does not provide significant gains in terms of classification accuracy, we recommend to use raw vegetation indices for mapping cropping practices in Mato Grosso, including the integrated Crop-Livestock systems