Doan Ha Phong

Main Article Content

Abstract

Abstract. Information on the area and spatial distribution of paddy rice fields is needed for food security, management of water resources, and estimation of Methan emission as well. MODIS remote sensing data including visible bands, near infrared band and short wave infrared band is foundation of calculating vegetation indices such as NDVI, EVI and LSWI. These remote sensing indices are very sensitive and strongly correlative to physiological status of plant, they are useful means for detecting and mapping paddy rice. This paper focus on an algorithm that uses time series of these vegetation indices to identify paddy rice areas based on sensivity of LSWI to the increased surface moisture during the period of flooding and rice transplanting.

Keywords: Remote Sensing, Paddy rice, NDVI, LSWI, Red River Delta.

References

[1] FAOSTAT, Statistical Database of the Food and Agricultural Organization of the United Nations, 2001.
[2] Prather & Ehhalt, Atmospheric chemistry and greenhouse gases. Climate change 2001: The scientific basis, Cambridge University Press (2001), UK. Pp 239–287.
[3] Xiangming Xiao, et al, Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data. International Journal of Remote Sensing (2002), 23, Elsevier-USA, pp 3009– 3022.
[4] Xiangming Xiao, Stephen Boles, et al, Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sensing of Environment (2006), 100, Elsevier-USA, pp 95 – 113.
[5] Le Toan, T., Ribbes, F., et al, Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results. IEEE Transactions on Geoscience and Remote Sensing (1997), 1, IEEE-USA, pp 41–56.
[6] Huete, A., et al, Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment (2002), 83, Elsevier-USA, pp 195– 213.