Synergetic use of Multi-sensor Time Series for the Derivation of Rice Seasonal Information

In past works, it has been demonstrated over various agro-ecological zones, crop establishment methods and water management practices in South-East Asia that spaceborne Synthetic Aperture Radar (SAR) time-series combined with yield crop modeling offers an effective alternative to conventional terrestrial methods and to yield estimations modeled at point level and up-scaled at some arbitrary administrative levels. Although seasonal rice information could have been generated on an operational basis providing accurate estimates, it has been recognized, due to the unfavorable SAR data availability, that the proposed SAR-yield model based solution had significant restrictions in terms of spatial coverage (sub-national level) and monitoring capabilities (i.e. limited to one crop season). Moreover, given the nature of the significant spatial-temporal seasonal rice dynamic, the relatively long revisiting time between subsequent acquisitions has been identified - for the accurate detection of key rice growth stages and inference of biophysical parameters - as an additional drawback. Nowadays, the availability over Europe of systematic Sentinel-1A dual polarization 12-days acquisitions in ascending and descending mode opens new frontiers in the processing, analysis and use of hyper-temporal SAR intensity and coherence stacks. The purpose of this paper is multiple, namely: 1.To demonstrate the robustness of the multi-temporal ?o rule based rice detection algorithm, developed for X-band (Cosmo-SkyMed, TerraSAR-X) and extended to C-band (Rasarsat-2, RISAT-1 and Sentinel-1) data. It is worth mentioning that this algorithm is applicable only if the rice peak of season is reached. 2.To detect the cultivated rice area at earliest stage by means of temporal descriptors derived from SAR ?o time series. It is additionally shown, that in complex agricultural regions, the integration of temporal descriptors extracted from multi-temporal Landsat-8 and in future Sentinel-2 data (hence leading to a temporal-spectral descriptors approach) is doubtless of advantage. Furthermore, given the stable Sentinel-1A baseline, temporal descriptors from multi-temporal coherence can additionally obtained and used to characterize the fields status and their evolution during the non-vegetative phase. 3.To monitor the initial irrigated area, date and duration by combining Sentinel-1 ascending and descending ?o time series. 4.To assess the main seasonal rice phenological moments, i.e. Start of Season, Start of the Vegetative Phase, and Start of Senescence. 5.To infer the Leaf Area Index during the vegetative phase. It is worth mentioning that the retrieval of this bio-physical parameter is performed by combining the detected rice phenological stages with a water cloud model, hence enabling to adapt it according to the different frequencies (i.e. X- and C-band) and phenology. 6.To assess, during the different phenological moments, the fields spatial and temporal variability by integrating very high resolution SAR data (Cosmo-SkyMed and TerraSAR-X StripMap). Based on multi-temporal Sentinel-1A, Landsat-8 OLI, Cosmo-SkyMed and TerraSAR-X data, results over the three major European rice regions located in Italy, Spain and Greece are shown and evaluated. The scalability of the proposed methodology is already on-going in West-Africa and South-East Asia, where adequate Sentinel-1A time series are available.