Author
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Houborg, Rasmus |
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Anderson, Martha |
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Submitted to: IEEE IGARSS Annual Proceedings
Publication Type: Proceedings Publication Acceptance Date: 3/26/2008 Publication Date: 7/6/2008 Citation: Houborg, R.M., Anderson M.C., 2008. Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content in crop systems. In: Proceedings of IEEE Transactions on Geoscience and Remote Sensing, July 7-11, 2008, Boston, Massachusetts, Abstract 3530. Interpretive Summary: Technical Abstract: “1. INTRODUCTION” Remotely sensed data in the reflective optical domain function as a unique cost-effective source for providing spatially and temporally distributed information on key biophysical and biochemical parameters of land surface vegetation. Leaf area index (LAI) is a critical structural variable for understanding biophysical processes of vegetation canopies and for quantifying exchange processes of energy and matter between the land surface and the lower atmosphere [1]. Total leaf chlorophyll content (Cab) can assist in determining photosynthetic capacity and productivity [2,3]. Cab is also a good indicator of vegetation stress [4], is strongly related to leaf nitrogen content [5] and could therefore prove valuable for precision crop management [6]. Remote sensing techniques for estimating vegetation characteristics from reflective optical measurements have either been based on the empirical-statistical approach that links vegetation indices (VI) and vegetation parameters using experimental data, or on the inversion of a physical canopy reflectance (CR) model. While the empirical approach is simple and computationally efficient, there is no unique relationship between a sought vegetation parameter and a VI of choice, but rather a family of relationships, each a function of canopy characteristics, soil background effects and external conditions [7,8]. Physically-based models have proven to be a promising alternative as they describe the transfer and interaction of radiation inside the canopy based on physical laws and thus provide an explicit connection between the biophysical variables and the canopy reflectance. The inversion process is ill-posed by nature due to measurement and model uncertainties and because different combinations of model parameters may correspond to almost identical spectra [9]. As a result, additional information is needed to accurately estimate the vegetation parameters. While the use of a priori knowledge (e.g. canopy type and architecture, model parameter ranges) has been shown to be an efficient way to solve ill-posed inverse problems [9,10], this regularization technique typically relies on the existence of experimental data collected at the site of interest. [7] demonstrated how the temporal evolution of LAI could be utilized as another way of regularizing the inverse problem and [11, 12, 13] demonstrated how to take advantage of the spectral radiometric information of pixels belonging to the same land cover type. In this paper the Cab and LAI retrieval capabilities of the REGularized canopy reflectance (REGFLEC) modeling tool [13] are demonstrated for agricultural areas in Maryland and Oklahoma. “2. METHODS” The REGularized canopy reflectance (REGLEC) modeling tool [13] couples leaf optics (PROSPECT), canopy reflectance (ACRM), and atmospheric radiative transfer (6SV1) models. In REGLFEC, leaf structure, vegetation clumping, and leaf inclination angle are assumed to possess limited variability within a given land cover class and are estimated by inverting the canopy reflectance model with the spectral radiometric information of pixels belonging to the same land cover type. Only pixels originating from medium to high density vegetation areas are included in the inversion to maximize the sensitivity of the reflectance signal to the class-specific vegetation parameters and reduce the influence of background effects. Subsequently, a family of pre-computed spectral reflectance relationships (look-up tables), each a function of canopy characteristics, soil background effects and external conditions, are employed for pixel-wise biophysical parameter retrievals. The developed method is entirely image-based, does not rely on impractical in-situ data measurements, can be applied at a range of scales using radiometric information from only 3 spectral bands (green, red, and near-infrared), and |
