Multi-temporal and multi-sensor solutions are essential to increase timeliness and reliability of land monitoring systems. This paper advocates the exploitation of the temporal contextual information provided by temporally dense SAR and optical data series series through the use of a Hidden Markov model (HMM)-based approach. An efficient strategy to incorporate the C-Band SAR data into the HMM framework, relying so far on Landsat, will be debated and assessed over a dynamic agricultural scenario, i.e. characterized by high temporal and spatial diversity in cropping practices. The site is located in the state of São Paulo (Brazil), where recent ground surveying activities has been conducted.

The paper debates a novel approach for land cover (LC) mapping based on the Hidden Markov Model. The proposed methodology is aimed to address both the urgent demand of off-line (or historic) LC information retrieval and of near-real time LC monitoring. The discrete-time model employs short steps of 16 days, that conveniently fits the Landsat revisit time while providing a continuous and temporally dense representation of the land cover dynamics. Two temporal pattern typologies were identified and modeled within the proposed Markov chain architecture: a seasonal and synchrounous behavior which can be associated to the observables of LC classes such as forest and grasses, and a highly asynchronous behaviour, which characterizes the crop observables. The first typology is addressed by introducing time-dependency in state output probabilities, whereas the latter is rendered through a sequence of (sub-class) states interlinked by means of a 'left-right' based model. Such model inherently incorporates crop growth tracking functionalities as an added value. In this paper the methodology has been tailored to Sao Paulo state (Brazil) scenario, showing overall accuracy above 80% on the test sample. A particular emphasis is attributed to the identification of sugarcane plantations, that are indeed responsible for major land use changes.