Share Email Print
cover

Proceedings Paper

Spatiotemporal variability of winter wheat condition based on TM data and geostatistics
Author(s): Suxia Wu; Renzhao Mao; Li Zheng
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

According to the ground resolution characteristic of Thematic Mapper (TM) image, we correspondingly measured the relative chlorophyll contents in four key developmental stages of winter wheat in the Lower plain of the Hai River Basin, North China, and explored their correlation with the reflected spectral values that can be obtained from TM image. Considering not only NDVI but also the relative content of the chlorophyll, 31 RS variables were selected and the relationship between the variables and the relative content of chlorophyll was established. Regression models were built for quantitatively predicting winter wheat growing condition from TM images. Also the spatiotemporal variability of the winter wheat growth status at heading and booting stages were analyzed by geostatictics approach. The correlated spatial variability of the relative content of chlorophyll existed in the case study area, and the range of correlative distance was from 145.4 to 320.0m. The spatially structured variances were between 75% and 21% of the total variances, and the empirical semivariograms in the four stages could be simulated in spherical models. The result showed that it is feasible to use TM data for real-time and highly accurate monitoring of crop growth status and nutrient management of farmland ecosystems.

Paper Details

Date Published: 1 September 2005
PDF: 8 pages
Proc. SPIE 5884, Remote Sensing and Modeling of Ecosystems for Sustainability II, 58841G (1 September 2005); doi: 10.1117/12.616223
Show Author Affiliations
Suxia Wu, Institute of Genetics and Developmental Biology, CAS (China)
Renzhao Mao, Institute of Genetics and Developmental Biology, CAS (China)
Li Zheng, Institute of Genetics and Developmental Biology, CAS (China)


Published in SPIE Proceedings Vol. 5884:
Remote Sensing and Modeling of Ecosystems for Sustainability II
Wei Gao; David R. Shaw, Editor(s)

© SPIE. Terms of Use
Back to Top