A crop phenology detection method using time-series MODIS data

• Published in: Remote sensing of environment Vol. 96; no. 3; pp. 366 - 374
• Main Authors: Sakamoto, Toshihiro, Yokozawa, Masayuki, Toritani, Hitoshi, Shibayama, Michio, Ishitsuka, Naoki, Ohno, Hiroyuki
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 30-06-2005; Elsevier Science
• Subjects: Agronomy. Soil science and plant productions; Applied geophysics; Biological and medical sciences; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; Generalities. Biometrics, experimentation. Remote sensing; Internal geophysics; MODIS; Oryza sativa; Paddy field; Phenological stage; Remote sensing; Wavelet transform; Far East; Japan; Paddy field; Wavelet transform; MODIS; Phenological stage; Remote sensing; time series analysis; Heading; Vegetation index; Cultivated soil; Harvesting date; frequency; new methods; filtering; Rice; Validation; detection; Space remote sensing; Planting date; Wavelet transformation; Asia; Phenology; Performance; Multidate observation; errors; statistics
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2005.03.008

Information of crop phenology is essential for evaluating crop productivity and crop management. Therefore we developed a new method for remotely determining phenological stages of paddy rice. The method consists of three procedures: (i) prescription of multi-temporal MODIS/Terra data; (ii) filtering time-series Enhanced Vegetation Index (EVI) data by time-frequency analysis; and (iii) specifying the phenological stages by detecting the maximum point, minimal point and inflection point from the smoothed EVI time profile. Applying this method to MODIS data, we determined the planting date, heading date, harvesting date, and growing period in 2002. And we validated the performance of the method against statistical data in 30 paddy fields. As for the filtering, we adopted wavelet and Fourier transforms. Three types of mother wavelet (Daubechies, Symlet and Coiflet) were used in Wavelet transform. As the results of validation, the wavelet transform performed better than the Fourier transform. Specifically, the case using Coiflet (order
=
4) gave remarkably good results in determining phenological stages and growing periods. The root mean square errors of the estimated phenological dates against the statistical data were: 12.1 days for planting date, 9.0 days for heading date, 10.6 days for harvesting date, and 11.0 days for growing period. The method using wavelet transform with Coiflet (order
=
4) allows the determination of regional characteristics of rice phenology. We proposed this new method using the wavelet transform; Wavelet based Filter for determining Crop Phenology (WFCP).