For agronomic, environmental, and economic reasons, the need for spatialized information about agricultural practices is expected to rapidly increase. In this context, we reviewed the literature on remote sensing for mapping cropping practices. The reviewed studies were grouped into three categories of practices: crop succession (crop rotation and fallowing), cropping pattern (single tree crop planting pattern, sequential cropping, and intercropping/agroforestry), and cropping techniques (irrigation, soil tillage, harvest and post-harvest practices, crop varieties, and agro-ecological infrastructures). We observed that the majority of the studies were exploratory investigations, tested on a local scale with a high dependence on ground data, and used only one type of remote sensing sensor. Furthermore, to be correctly implemented, most of the methods relied heavily on local knowledge on the management practices, the environment, and the biological material. These limitations point to future research directions, such as the use of land stratification, multi-sensor data combination, and expert knowledge-driven methods. Finally, the new spatial technologies, and particularly the Sentinel constellation, are expected to improve the monitoring of cropping practices in the challenging context of food security and better management of agro-environmental issues.

In response to the need for generic remote sensing tools to support large-scale agricultural monitoring, we present a new approach for regional-scale mapping of agricultural land-use systems (ALUS) based on object-based Normalized Difference Vegetation Index (NDVI) time series analysis. The approach consists of two main steps. First, to obtain relatively homogeneous land units in terms of phenological patterns, a principal component analysis (PCA) is applied to an annual MODIS NDVI time series, and an automatic segmentation is performed on the resulting high-order principal component images. Second, the resulting land units are classified into the crop agriculture domain or the livestock domain based on their land-cover characteristics. The crop agriculture domain land units are further classified into different cropping systems based on the correspondence of their NDVI temporal profiles with the phenological patterns associated with the cropping systems of the study area. A map of the main ALUS of the Brazilian state of Tocantins was produced for the 2013–2014 growing season with the new approach, and a significant coherence was observed between the spatial distribution of the cropping systems in the final ALUS map and in a reference map extracted from the official agricultural statistics of the Brazilian Institute of Geography and Statistics (IBGE). This study shows the potential of remote sensing techniques to provide valuable baseline spatial information for supporting agricultural monitoring and for large-scale land-use systems analysis.

This study explored the suitability of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectrometer (MODIS) obtained for six sugar management zones, over nine years (2002–2010), to forecast sugarcane yield on an annual and zonal base. To take into account the characteristics of the sugarcane crop management (15-month cycle for a ratoon, accompanied with continuous harvest in Western Kenya), the temporal series of NDVI was normalized through an original weighting method that considered the growth period of the sugarcane crop (wNDVI), and correlated it with historical yield datasets. Results when using wNDVI were consistent with historical yield and significant at P-value = 0.001, while results when using traditional annual NDVI integrated over the calendar year were not significant. This correlation between yield and wNDVI is mainly drawn by the spatial dimension of the data set (R² = 0.53, when all years are aggregated together), rather than by the temporal dimension of the data set (R² = 0.1, when all zones are aggregated). A test on 2012 yield estimation with this model realized a RMSE less than 5 t·ha⁻¹. Despite progress in the methodology through the weighted NDVI, and an extensive spatio-temporal analysis, this paper shows the difficulty in forecasting sugarcane yield on an annual base using current satellite low-resolution data. This is particularly true in the context of small scale farmers with fields measuring less than the size of MODIS 250 m pixel, and in the context of a 15-month crop cycle with no seasonal cropping calendar. Future satellite missions should permit monitoring of sugarcane yields using image resolutions that facilitate extraction of crop phenology from a group of individual plots.

The main pioneer frontier considered by geographers is the Amazonian pioneer frontier. The occupation of the Brazilian territory has been carried out through successive economic cycles. Currently, the expansion of soybean crops in Amazonia is considered as the last economic cycle involving new migrations to still unexplored areas. Mapping this frontier is necessary in order to better understand its drivers and think about efficient land use policies to struggle its progress. In this paper, we propose an innovative methodology for mapping the agricultural frontier in the Amazonian state of Mato Grosso (Brazil) using satellite data acquired during the 2000–2006 period. We assume that the frontier evolves through successive land-use stages such as wildlands, non-croplands, extensive croplands and intensive croplands areas. The transitions between two stages can then be monitored through land-use change analysis. A particular emphasis is given on three land-use transitions named “deforestation”, “economic” and “intensification sub-frontiers”. In order to adapt this model for the use of remote sensing data, we defined (1) a relevant spatial scale corresponding to an agrarian locality, (2) relevant indices to qualify the land-use types and (3) rules applied on land-use types to define the frontier’s location. Our results highlight the dynamic of the agricultural frontier towards northern areas. We identified four main agricultural regions with different levels of agricultural intensification. We finally discuss these results with respect to governmental policies and economics for a better understanding of the frontier’s dynamic.

Human pressure on ecosystems has undesirable impacts on human well-being. After the Millennium Project, much interdisciplinary research has been developed worldwide aiming to understand these impacts on ecosystem flows and processes, and to learn about the costs and the benefits of ecosystem services for production. Soil provides many ecosystem services, since its multi-functionality is the basis for food production, water filtration, nutrient cycling, and other goods essential to life. This article presents the main concepts and classifications of soil ecosystem services and of its functions; the indicators and the methods for assessment, modeling, and valuation of ecosystem services; some recent applications to assess and evaluate impacts of agricultural management practices on soil ecosystem services; as well as challenges and opportunities for research and for development of public policies related to agro-environmental sustainability in Brazil. Although the role of soil in supplying ecosystem services is yet undervalued, scientists are gradually recognizing soil processes and functions as fundamental to assess ecosystem services and the effects of land use and management on them. Interdisciplinary approaches to integrate science and public policies are necessary to build governance based on ecosystem services.