Rice monitoring using radar remote sensing
Creators: name: Hoa Phan, Thi
Publisher: name: GEO Knowledge Hub
Year: 2020

description: Abstract 
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. We also investigated on how the products derived from EO Sentinel-1 data can be integrated in process-based models for rice production estimation and methane emission estimation. The test region is one of the world's major rice regions: 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. The first step was to understand the temporal variation of the backscatter Sentinel-1 backscatter of rice fields, at VH and VV polarizations. For this purpose, in-situ data have been collected on 60 fields during 2 years, for the 5 rice seasons. It was found that backscatter time series of rice fields show very specific temporal behavior, with respect to other land use land cover types. The temporal and polarization variations of the rice backscatter have been interpreted with respect to physical interaction mechanisms to relate the backscatter dynamics to the key phenological stages, when the plants change its morphology and biomass. Because the same trend of temporal curves was observed over 5 rice seasons, it was possible to derive a mean curve to be used in the methodology developed for detecting rice phenology, and deriving information such as the date of sowing, the rice varieties of long and short duration cycle, or plant height, at each SAR acquisition date. The methods have been developed and applied to the Mekong delta. Products validation provides a good agreement with the reference data sets: 98% in rice/non-rice accuracy, the sowing dates RMSE of about 4 days, plant height RMSE of 7.8 cm, the viii long/short variety map has 91.7% accuracy and for phenology, only one season has been processed with good detection rate of 59/60. Finally, the use of the rice monitoring products as inputs in two process-based models was 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) were used as model inputs, giving good agreement with the results making use of ground survey only. Based on the two process models with inputs from Sentinel-1 data, it was possible to have an integrated result on rice yield, water use, and methane emissions. The preliminary results show a good potential for the optimization of water management in rice fields in order to reduce water use and GHG emission, without reducing the yield. To achieve the objective which is the effective use of Sentinel-1 data for rice monitoring for food security and global environment, more works need to be done concerning the consolidation of the rice monitoring method development and the integration of Sentinel-1 derived information in models aiming at estimating and predicting rice production, methane emission and water use.