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Introduction: Agricultural powers have launched ambitious programs, such as GAP (Good Agricultural Practice) guidelines or the Low Carbon Agriculture Program (Program ABC). Those programs give a special role to multifunctional landscapes in the process to establish a sustainable agriculture. The purpose of this work is to present the Geo-ABC Project, an innovative project aiming at developing methods to monitoring, at local scale, sustainable cropping system (practices), such as: crop-livestock-forest integration and, at regional scale, cropland sustainable system (landscape patterns) in order to provide spatial indicators to support the ABC Program. Material and Methods: A mixed of different methodological approaches will be applied to study how the local variables are scaled-up to the regional scale, and what spatial, temporal and textural indicators derived from coarse-resolution satellite images can be used to represent cropland system at landscape level. Crop specific distribution modelling, traditional up-scaling and new methods approaches based on indirect satellite-derived variables (temporal, spectral, spatial indicators) will be applied and tested in study cases at Tocantins state (Matopiba Region) and at crop-livestock-forest integration sites. Results and Conclusions: The complete set of methodological approach constitutes one of results of this work, establishing methodological protocols to obtain systematic spatial indicators, at multi-scale level providing metrics to the ABC Program. At the political context, GeoABc will provide spatial-temporal metrics that can be used as inputs for: the monitoring the ABC Program goals (How much?); the survey of information about the adoption of the ABC Program (Where?); the planning of monitoring GHGs within the Sectoral Plan of Climate Change Mitigation and Adaptation (Where to go?); the processes of political decision-making in the assessment of ABC Program (What to do?). At the scientific context, those methods will provide inputs for scientific studies: on the dynamics of land use related with the adoption of low carbon agricultural production systems, for assessment of trends and establishment of future scenarios (land use dynamics); on the dynamics of land use, based on the expansion of low-carbon agriculture production related to the mitigation of environmental impacts (environmental impacts); on the dynamics of land use, based on the expansion of low-carbon agriculture production related to the mitigation of the greenhouse effect: (a) carbon stocks in soil and biomass; (b) reduction of GHGS; (c) water balance-ecosystem services; (iv) on the dynamics of land use, based on the expansion of low-carbon agriculture production and the relation with Climate Change (climate change).

In 2010, the Brazilian Government launched the Low-Carbon Agriculture Plan (Plan ABC) to promote adoption of good management practices by farmers and ranchers nationwide (Brazil, 2011). The selected practices included zero-tillage and integrated crop-livestock systems (also known as mixed farming systems) for cereal grains and beef or dairy production. However, the lack of monitoring procedures to evaluate the implementation and communication to policy makers and society in general may limit governance of the Plan ABC. Remote sensing techniques have strong potential to identify and measure the adoption of the selected practices, particularly regarding integrated crop-livestock systems in comparison with continuous pasture or cropland. The synoptic, multispectral and repetitive characteristics of satellites create a collection of data needed to map land management and understand the crop cycle, mainly in larger countries such as Brazil. In order to monitor crop growth along the season and track the change in management practices, a collection of data constructing a time series is needed. This paper aims at analyzing the spectral behavior of integrated croplivestock systems (ICLS) compared to a neighboring native Cerrado forest and a continuous degraded pasture. The study was conducted in the "Capivara Research Farm" of the National Rice and Bean Research Center of the Brazilian Research Corporation - Embrapa, in Santo Antônio de Goiás, Brazil. The climate had a two welldefined season: a dry season from April to October and a rainy season from November to March. The average annual rainfall is 1505 mm. For this study, ten paddocks were selected with areas ranging from 5.3 ha to 13.1 ha between April 2009 and October 2016. The crop rotation consisted of Brachiaria pasture grass (Urochloa Bryzantha) or fallow in the dry season interchangeably with soybean, rainfed rice and maize+brachiaria pasture grass in the rainy season. Grain crops and maize+brachiaria pasture grass were sown using a no-till planter. We used 97 cloud-free NDVI images of Landsat-8 OLI and Landsat-7 ETM sensors for a 2009-2016 time series, acquired monthly (in average), at 30 m spatial resolution. The focus was to use images of both rainy and dry seasons for the identification of crop cycle. For 12 land use types i.e. native forest, degraded pasture and ten different integrated crop-livestock systems, a mean value of the NDVI was calculated in each polygon-paddock. The results showed that sensor spectral response followed the vegetation growth (Figure 1). Sensors were also able to capture mainly the continuous high amount of forest biomass throughout the years with slight responses due to rainy and dry seasons. Sensors also captured the seasonal variations of crops and pastures that follow the rainy/dry seasons. However, integrated crop-livestock systems have a complex temporal behavior, commonly present in a mixed growth of both crop and pasture that is not easy to separate from crop successions. We are using satellite-derived seasonal NDVI metrics in order to separate different land use types particularly grain croppasture system, based on Tong et al (2017). For example, NDVI of the fields under Brachiaria pasture grass maintained at higher values along the season than the cropped fields (Figure 2).

In 2010, the Brazilian Government launched the Low-Carbon Agriculture Plan (Plan ABC) to promote adoption of good management practices by farmers and ranchers nationwide (Brazil, 2011). The selected practices included zero-tillage and integrated crop-livestock systems (also known as mixed farming systems) for cereal grains and beef or dairy production. However, the lack of monitoring procedures to evaluate the implementation and communication to policy makers and society in general may limit governance of the Plan ABC. Remote sensing techniques have strong potential to identify and measure the adoption of the selected practices, particularly regarding integrated crop-livestock systems in comparison with continuous pasture or cropland. The synoptic, multispectral and repetitive characteristics of satellites create a collection of data needed to map land management and understand the crop cycle, mainly in larger countries such as Brazil. In order to monitor crop growth along the season and track the change in management practices, a collection of data constructing a time series is needed. This paper aims at analyzing the spectral behavior of integrated croplivestock systems (ICLS) compared to a neighboring native Cerrado forest and a continuous degraded pasture. The study was conducted in the "Capivara Research Farm" of the National Rice and Bean Research Center of the Brazilian Research Corporation - Embrapa, in Santo Antônio de Goiás, Brazil. The climate had a two welldefined season: a dry season from April to October and a rainy season from November to March. The average annual rainfall is 1505 mm. For this study, ten paddocks were selected with areas ranging from 5.3 ha to 13.1 ha between April 2009 and October 2016. The crop rotation consisted of Brachiaria pasture grass (Urochloa Bryzantha) or fallow in the dry season interchangeably with soybean, rainfed rice and maize+brachiaria pasture grass in the rainy season. Grain crops and maize+brachiaria pasture grass were sown using a no-till planter. We used 97 cloud-free NDVI images of Landsat-8 OLI and Landsat-7 ETM sensors for a 2009-2016 time series, acquired monthly (in average), at 30 m spatial resolution. The focus was to use images of both rainy and dry seasons for the identification of crop cycle. For 12 land use types i.e. native forest, degraded pasture and ten different integrated crop-livestock systems, a mean value of the NDVI was calculated in each polygon-paddock. The results showed that sensor spectral response followed the vegetation growth (Figure 1). Sensors were also able to capture mainly the continuous high amount of forest biomass throughout the years with slight responses due to rainy and dry seasons. Sensors also captured the seasonal variations of crops and pastures that follow the rainy/dry seasons. However, integrated crop-livestock systems have a complex temporal behavior, commonly present in a mixed growth of both crop and pasture that is not easy to separate from crop successions. We are using satellite-derived seasonal NDVI metrics in order to separate different land use types particularly grain croppasture system, based on Tong et al (2017). For example, NDVI of the fields under Brachiaria pasture grass maintained at higher values along the season than the cropped fields (Figure 2).