Rice is the primary staple food of more than half of world’s population and plays an especially important role in global economy, food security, water use and climate change. The objective of this thesis was to develop methods for rice monitoring based on Sentinel-1 data and to effectively use the mapping products in various applications concerning food security and global environment.
Specifically, the study aims at providing tools for observation of the rice cultivation systems, by generating products such as map of rice planted area, map of rice start-of-season and phenological stages, and map of rice crop intensity, together with rice crop parameters such as category of rice varieties (long or short cycle), and plant height. The information to be provided is necessary for the estimation of crop production, and for the management of rice ecosystems at the regional scale. The purpose here is not to derive the best possible rice map at each site through intensive calibration or large-scale fieldwork, but to introduce a simple approach that is robust, repeatable and suitable for rapid rice mapping over large extents with cost-effective field work. The overarching goal is to demonstrate that SAR-based operational mapping of rice crops across a diverse range of environments is possible based on the increasing availability of multi-temporal SAR data. The thesis is a timely contribution to remote-sensing applications for food security, since it presents a method to derive sufficiently accurate rice area maps under different conditions that are typical of the diversity of rice environments in Asia.
The methods have been developed and applied to the Mekong River Delta, in Vietnam. This region presents a diversity in rice cultivation practices, in cropping density, from single to triple crop a year, and in crop calendar. Products validation exercise based on in situ data dedicated to validation (1950 independent data points for rice-non rice, and for other parameters, the in situ data over 60 fields for one rice season are used for training, the data of 4 other rice seasons are used for validation. The accuracy of the rice/non rice map was found reaching 98%, the sowing dates have a RMSE of about 4 days, the RMSE in plant height is 7.8 cm, the long/short variety map has 91.7% accuracy and for phenology, only one season has been processed with good detection rate of 59/60. The methodology for rice mapping has also been applied at national scale for Vietnam and Cambodia to test the application of the methods on the mosaic of Sentinel-1 data acquired at different dates. Despite the lack of validation, the results demonstrate that it is possible to use Sentinel-1 data for mapping of rice fields at national level, especially with it capability to have short revisit time (6 days at present), high resolution (10m) and large coverage (250 km).
Finally, the uses of the rice monitoring products as inputs in two process-based models were assessed. The models are ORYZA2000 for rice production estimation and DNDC for methane emission and water demand estimation. Sentinel-1 data retrieved information (sowing date, phenology, long/short variety, plant height) are used as model inputs, giving good agreement with the results making use of ground survey only. It was possible to have an integrated result on rice yield, water use, and methane emissions based on the two process models with inputs from Sentinel-1 data. The preliminary results show good potential to determine the water management in rice field to reduce water use and GHG emission, without reducing much the yield.