HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Free Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in ISI Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via ISI Web of Science (9) Citing Articles via Google Scholar Google Scholar Articles by Chang, K.-W. Articles by Lo, J.-C. Search for Related Content PubMed Articles by Chang, K.-W. Articles by Lo, J.-C. Agricola Articles by Chang, K.-W. Articles by Lo, J.-C. Related Collections Rice Remote Sensing Production Agriculture Site-Specific Analysis Statistics

Remote Sensing

Predicting Rice Yield Using Canopy Reflectance Measured at Booting Stage

^a No. 70-11, Beishiliao Liau, Beishiliao Village, Madou Town, Tainan County, 721, Aletheia Univ., Taiwan, ROC
^b Dep. of Soil and Environmental Sciences, National Chung-Hsing Univ., Taichung, 402, Taiwan, ROC
^c Dep. of Agronomy, Chiayi Station, TARI. Chiayi, Taiwan, ROC

^* Corresponding author (yshen{at}nchu.edu.tw)

Received for publication June 16, 2004. Abilities to estimate rice (Oryza sativa L.) yields within fields from remote sensing images is not only fundamental to applications of precision agriculture, but can also be very useful to food provisions management. Major objectives of this study were to identify spectral characteristics associated with rice yield and to establish their quantitative relationships. Field experiments were conducted at Shi-Ko experimental farm of TARI's Chiayi Station during 1999–2001. Rice cultivar Tainung 67, the major cultivar grown in Taiwan, was used in the study. Various levels of rice yield were obtained via N application treatments. Canopy reflectance spectra were measured during entire growth period, and dynamic changes of characteristic spectrum were analyzed. Relationships among rice yields and characteristic spectrum were studied to establish yield estimation models suitable for remote sensing purposes. Spectrum analysis indicated that the changes of canopy reflectance spectrum were least during booting stages. Therefore, the canopy reflectance spectra during this period were selected for model development. Two multiple regression models, constituting of band ratios (NIR/RED and NIR/GRN), were then constructed to estimate rice yields for first and second crops separately. Results of the validation experiments indicated that the derived regression equations successfully predicted rice yield using canopy reflectance measured at booting stage unless other severe stresses occurred afterward.

Abbreviations: GNDVI, green normalized difference vegetation index • GRN, green band • MSPR, mean squared prediction error • NDVI, normalized difference vegetation index • NIR, near-infrared reflectance • RED, red reflectance • TARI, Taiwan Agriculture Research Institute