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Rice is the most important food security crop in Asia. Information on its seasonal extent forms part of the national accounting of many Asian countries. Synthetic Aperture Radar (SAR) imagery is highly suitable for detecting lowland rice, especially in tropical and subtropical regions, where pervasive cloud cover in the rainy seasons precludes the use of optical imagery. Here, we present a simple, robust, rule-based classification for mapping rice area with regularly acquired, multi-temporal, X-band, HH-polarized SAR imagery and site-specific parameters for classification. The rules for rice detection are based on the well-studied temporal signature of rice from SAR backscatter and its relationship with crop stages. We also present a procedure for estimating the parameters based on "temporal feature descriptors" that concisely characterize the key information in the rice signatures in monitored field locations within each site. We demonstrate the robustness of the approach on a very large dataset. A total of 127 images across 13 footprints in six countries in Asia were obtained between October 2012, and April 2014, covering 4.78 m ha. More than 1900 in-season site visits were conducted across 228 monitoring locations in the footprints for classification purposes, and more than 1300 field observations were made for accuracy assessment. Some 1.6 m ha of rice were mapped with classification accuracies from 85% to 95% based on the parameters that were closely related to the observed temporal feature descriptors derived for Remote Sens. 2014, 6 10775 each site. The 13 sites capture much of the diversity in water management, crop establishment and maturity in South and Southeast Asia. The study demonstrates the feasibility of rice detection at the national scale using multi-temporal SAR imagery with robust classification methods and parameters that are based on the knowledge of the temporal dynamics of the rice crop. We highlight the need for the development of an open-access library of temporal signatures, further investigation into temporal feature descriptors and better ancillary data to reduce the risk of misclassification with surfaces that have temporal backscatter dynamics similar to those of rice. We conclude with observations on the need to define appropriate SAR acquisition plans to support policies and decisions related to food security.

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Butterfly pea flower have great sensory attraction, but they have not yet been used widely in Vietnam. Extracts of butterfly pea flowers can be used conveniently as a natural blue colorant for food products. In this study, the identification of anthocyanin compounds in butterfly pea flowers was performed by UPLC coupled with a UV and Mass spectrometer instrument. Positive and negative ion electrospray MS/MS chromatograms and spectra of the anthocyanin compounds were determined. By analyzing the chromatograms and spectra for each ion, five anthocyanins were identified in the butterfly pea flower extract; these were delphinidin-3-(6”‐p-coumaroyl)-rutinoside, cyanidin 3-(6”-p-coumaroyl)-rutinoside, delphinidin-3-(p-coumaroyl) glucose in both cis- and trans- isomers, cyanidin-3-(p-coumaroyl-glucoside) and delphinidin-3-pyranoside. Additionally, based on their intensity, it was determined that cyanidin-3-(p-coumaroyl-glucoside) was the most abundant anthocyanin, followed by cyanidin 3-(6”-p-coumaroyl)-rutinoside, delphinidin-3-(p-coumaroyl-glucoside), delphinidin-3-(6”-p-coumaroyl)-rutinoside and delphinidin-3-pyranoside. In this study, cyanidin derivatives were discovered in butterfly pea flower extract, where these compounds had not been detected in previous studies.

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