Detecting Rice Phenology in Paddy Fields with Complex Cropping Pattern Using Time Series MODIS Data

Dewi Kania Sari, Ishak H. Ismullah, Widyo N. Sulasdi, Agung B. Harto

Abstract


Monitoring paddy rice phenology and cropping schedules over wide areas is essential for many applications. Remote sensing provides a viable means to meet the requirement of improved regional-scale data set of paddy rice fields, such as phenological stages. A number of methods have been developed for detecting seasonal vegetation changes by using satellite images. Development of such methods to paddy fields with complex cropping pattern is still challenging. In this study, we developed a method for remotely determining phenological stages of paddy rice that uses time series of two vegetation indices (EVI and LSWI) obtained from MODIS data. We ran the algorithm to determine the planting date, heading date, and harvesting date of paddy rice in 5 districts of West Java Province, using the 8-day composite MODIS Surface Reflectance products (500-m spatial resolution) in 2004. Estimated harvesting dates were then used to calculate paddy rice harvested area. We validated the performance of the method against statistical data in 13 subdistricts. The root mean square errors of the estimated paddy rice harvested area against the statistical data were: 851 Ha for monthly data, 1227 Ha for quarterly data, and 2433 Ha for yearly data. Subdistrict-level comparisons of paddy rice harvested area between the MODIS estimation and statistical data showed moderate correlation, with coefficient of determination (r2) 0.6, 0.7, and 0.6 for monthly, quarterly and yearly data, respectively. The results of this study indicated that the MODIS-based paddy rice phenological detection algorithm could potentially be applied at large spatial scales to monitor paddy rice agriculture on a timely and frequent basis.

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References


Kimball, J., McDonald, K., Running, S., & Frolking, S. Satellite Radar Remote Sensing of Seasonal Growing Seasons for Boreal and Subalpine Evergreen Forests, Remote Sensing of Environment, 90, 243-258, 2004.

Bauman, B., Kropff, M., Tuong, T., Wopereis, M., Berge, H., & Laar, H., ORYZA2000: Modeling lowland rice. Manila, Philippines’ IRRI, 2001.

Digkuhn, M. & Gal, P., Effect of Drainage Date on Yield and Dry Matter Partitioning in Irrigated Rice, Field Crops Research, 46, 117-126, 1996.

Uchida, S., Monitoring of Paddy Rice Planting with Complex Cropping Pattern Using Satellite Remote Sensing Data – A Case of West Java, Indonesia, in Proceedings of the 28th Asian Conference on Remote Sensing (ACRS), 12-16 November 2007, Kuala Lumpur, Malaysia, 2007, http://www.a-a-r-s.org/acrs/ proceeding/ACRS2007/Papers/TS1.1.pdf, (15 September 2009).

Xiao, X., Boles, S., Liu, J., Zhuang, D., Frolking, S., Li, C., Sales, W., & Moore III, B., Mapping Paddy Rice Agriculture in Southern China Using Multi-Temporal MODIS Images, Remote Sensing of Environment, 95, 480-492, 2005.

Xiao, X., Boles, S., Frolking, S., Li, C., Babu, J.Y., Sales, W. & Moore III, B., Mapping Paddy Rice Agriculture in South and Southeast Asia Using Multi-Temporal MODIS images, Remote Sensing of Environment, 100, 95-113, 2006.

Xiao, X., Boles, S., Frolking, S., Salas,W., Moore III, B., Li, C., 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, 23, 3009-3022, 2002.

Maki, M., Ishiahra, M., & Tamura, M., Estimation of Leaf Water Status to Monitor The Risk of Forest Fires by Using Remotely Sensed Data, Remote Sensing of Environment, 90, 441-450, 2004.

Sakamoto, T., Yokozawa, M., Toritani, H., Shibayama, M., Ishitsuka, N. & Ohno, H., A Crop Phenology Detection Method Using Time-Series MODIS data, Remote Sensing of Environment, 96, 366-374, 2005.

Sakamoto, T., Nguyen, N.V., Ohno, H., Ishitsuka, N. & Yokozawa, M., Spatio-Temporal Distribution of Rice Phenology and Cropping Systems in The Mekong Delta With Special Reference to The Seasonal Water Flow of The Mekong and Bassac Rivers, Remote Sensing of Environment, 100, 1-16, 2006.

Indonesian Agency for Agricultural Research and Development (IAARD), Cropping Calendar for Food Crop, Ministry of Agriculture – Indonesia, 2008.

Vermote, E.F., Kotchenova, S.Y. & Ray, J.P., MODIS Surface Reflectance User’s Guide Version 1.2., MODIS Land Surface Reflectance Science Computing Facility, http://modis-sr.ltdri.org, (5 July 2008).

Huete, A. R., Liu, H. Q., Batchily, K., & van Leeuwen, W., A Comparison of Vegetation Indices Global Set of TM Images for EOSMODIS, Remote Sensing of Environment, 59, 440-451, 1997.

Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G., Overview of The Radiometric and Biophysical Performance of The MODIS Vegetation Indices, Remote Sensing of Environment, 83, 195-213, 2002.

Le Toan, T., Ribbes, F., Wang, L., Floury, N., Ding, K., Kong, J., et al., Rice Crop Mapping and Monitoring Using ERS-1 Data Based on Experiment and Modeling Results, IEEE Transactions on Geoscience and Remote Sensing, 1, 41-56, 1997.

Domiri, D.D., Adhyani, N.L. & Nugraheni, S., Paddy Rice Growth Model Using MODIS Data for Estimating Paddy Rice Age, in Proceedings of the 14th PIT MAPIN, 14-15 September 2005, Surabaya, Indonesia, 2005. (in Bahasa Indonesia)

Wahyunto, Ritung, S., & Widagdo, Remote Sensing Technology for Land Resource Inventory and Monitoring Efficiency, Final Report, Indonesian Center for Agricultural Research and Development, Ministry of Agriculture of the Republic of Indonesia, 2003. (in Bahasa Indonesia)

FAO, Agro-ecological Zoning Guidelines. FAO Soils Bulletin 73, FAO, Rome, 1996.

Hermanto, Ciherang Paddy Variety is Becoming More Popular, Warta Penelitian dan Pengembangan Pertanian, 28(2), 2006. (in Bahasa Indonesia)

Vermote, E.F., & Vermeulen, A., MODIS Algorithm Technical Background Document, Atmospheric Correction Algorithm: Spectral Reflectances (MOD09). NASA contract NAS5-96062, 1999.




DOI: http://dx.doi.org/10.5614%2Fitbj.sci.2010.42.2.2

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