High spectral remote sensing is a hopeful technology in diagnosing crop nutrition background. With surface spectral measurement and laboratory biochemical analysis, the relationship between crop properties and spectral remote sensing data has been established. Seven chemical components - total chlorophyll, water crude protein, soluble sugar, N, P, K - were analyzed by laboratory chemical analyzing instrument. Foliar spectral property was detected outdoors by surface spectrometer. Chemical concentrations have been related to foliar spectral properties through stepwise multiple regression. The statistical equations between the chemical concentrations and reflectance as well as its several transformations were established. They underscored good estimation performance for chlorophyll, water crude protein, N and K with high squared multiple correlation coefficients (R2) values and high believable level. Especially R2 value of the equation between crude protein concentration and the first derivative of reflectance is 0.9564, which is the best result in the study of the fresh leave biochemistry up to now. On the basis of field experiment, an airborne remote sensing for crop nutrition monitoring was conducted in Shunyi County, Beijing, PR China. The sensor, made by Chinese Academy of Sciences, is in visible and near IR band. By image processing, the crop biochemistry map is obtained.
[1]
G. Andreoli,et al.
Investigation of leaf biochemistry by statistics
,
1995
.
[2]
Jean-Philippe Gastellu-Etchegorry,et al.
Forest canopy chemistry with high spectral resolution remote sensing
,
1996
.
[3]
S. Goetz,et al.
Remote sensing of net primary production in boreal forest stands
,
1996
.
[4]
B. Brisco,et al.
Precision Agriculture and the Role of Remote Sensing: A Review
,
1998
.
[5]
S. Ustin,et al.
Critique of stepwise multiple linear regression for the extraction of leaf biochemistry information from leaf reflectance data
,
1996
.
[6]
J. Dungan,et al.
Reflectance spectroscopy of fresh whole leaves for the estimation of chemical concentration
,
1992
.