Quantitative Analysis of Crops Chlorophyll in the Heihe River Basin by Hyperspectral Remote Sensing Image
The continuous fine spectrums obtained by hyperspectral remote sensing provide broad accessibility to precisely estimate physiological and biochemical parameters of ground vegetation. Chlorophyll is responsible for absorbing sunlight, so the real-time and accurate monitoring of chlorophyll becomes an important research field of hyperspectral remote sensing in precision agriculture. By utilizing a spectral index which is sensitive to the chlorophyll content(Chl), this paper tried to calculate the Chl profiles of the arid region in Heihe River Basin based on hyperspectral remote sensing images acquired by Operational Modular Imaging Spectrometer (OMIS) in WATER campaign. In addition, an Absorption Depth Index(ADI) was proposed as an effective indicator of the emissive chlorophyll fluorescence (ChlF) based on the spectral characteristics of Solar Fraunhofer lines, and the ChlF and ADI profiles at 760nm band of each experiment site were worked out. The inversion result of Chl and ChlF were in good conformity with the actual situation, which would serve as important materials for the real-time monitoring of crop growth in the Heihe river basin.
Keywordschlorophyll content fluorescence hyperspectral remote sensing styling Heihe river basin
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- 7.Gamon, J., Filella, I., Peuelas, J.: The dynamic 531-nanometer reflectance signal: A survey of 20 angiosperm species. Photosynthetic Responses to the Environment, 172–177 (1993)Google Scholar
- 8.Zarco-Tejada, P., Miller, J., Noland, T., Mohammed, G., Sampson, P.: Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing 39(7), 1491–1507 (2002)CrossRefGoogle Scholar
- 10.Liu, L.Y., Zhang, Y.J., Wang, J.H., Zhao, C.J.: Research on passive detection of vegetation chlorophyll fluorescence. Imaging Spectroscopy Technology Research and Application Essays (2004)Google Scholar
- 11.Yan, C.Y., Liu, Q.: Model Analysis and Validation by Using Airborne Hyperspectral Remote Sensing Data to Extract Winter Wheat Canopy Chlorophyll Content. Remote Sensing Information 005, 8–14 (2009)Google Scholar
- 14.Kussk, A.: The hot spot of a uniform vegetation cover. J. Remote Sensing 3(4), 645 (1985)Google Scholar
- 15.Xie, D.H., Zhu, Q.J., Wang, J.D., Xu, K.: Analysis on the vertical distribution of biochemical parameters based on a 3D virtual corn canopy scene. Journal of Beijing Normal University (Natural Science) 43(003), 337–342 (2007)Google Scholar
- 17.Yan, C.Y., Niu, Z., Wang, J.H., Liu, L.Y., Huang, W.J.: The assessment of spectral indices applied in chlorophyll content retrieval and a modified crop canopy chlorophyll content retrieval model. Journal of Remote Sensing 9(006), 742–750 (2005)Google Scholar
- 18.Yan, C.Y.: Study on methods and models for vegetation biochemical information retrieval by remote sensing. Beijing: Institute of Remote Sensing Applications. Chinese Academy of Science (2004)Google Scholar
- 19.Liu, L.Y., Zhang, Y.J., Wang, J.H., Zhao, C.J.: Detecting photosynthesis fluorescence under natural sunlight based on Fraunhofer line. Journal of Remote Sensing 10(001), 130–137 (2006)Google Scholar
- 20.Cheng, Z.H., Liu, L.Y.: Estimating light-use efficiency by the separated Solar-induced chlorophyll fluorescence from canopy spectral data. Journal of Remote Sensing 14(002), 356–371 (2010)Google Scholar