Page Banner

United States Department of Agriculture

Agricultural Research Service

Title: Utility of an image-based canopy reflectence modeling tool for remote estimation of LAI and leaf chlorophyll content in crop systems.

item Houborg, Rasmus
item Anderson, Martha

Submitted to: IEEE IGARSS Annual Proceedings
Publication Type: Proceedings
Publication Acceptance Date: March 26, 2008
Publication Date: July 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.

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 can be used for agricultural fields characterized by widely varying distributions of model variables. “3. RESULTS AND CONCLUSIONS” REGFLEC was applied to remotely sensed reflectance observations of agricultural fields in Maryland and Oklahoma. Using 1 m resolution aircraft and 10 and 20 m resolution SPOT-5 imagery, REGFLEC effectuated robust biophysical parameter retrievals for a diversity of agricultural fields characterized by a wide range in leaf chlorophyll levels and intermixed green and senescent leaf material. Relative root-mean-square deviations were on the order of 10 %. Variations in background reflectance can easily confound the detection of relatively subtle differences in canopy reflectance due to changes in leaf chlorophyll content [5], and the high Cab estimation accuracies reported here were due in part to an effective novel correction for background effects. REGFLEC facilitates reliable entirely image-based biophysical parameter retrievals with a minimum of input requirements: 1) At-sensor radiance data in 3 spectral bands available on practically any airborne and satellite based sensor system. A key advantage is the direct use of readily available radiance data as REGFLEC couples atmospheric correction (6SV1) and canopy reflectance modeling routines, 2) Atmospheric state parameters that can all be acquired with reasonable accuracy from operational satellite products, 3) Land cover classification, and 4) an approximate first estimate of the background reflectance signal from a nearby bare soil or partially vegetated field. REGFLEC holds promise as a valuable tool for predicting LAI and leaf chlorophyll in the context of precision farming, for determining vegetation stress and photosynthetic productivity, and for reliable biophysical parameter estimations at a range of scales. “4. REFERENCES” [1] S.M. Moran, S.J. Maas, and P.J. Pinter Jr., “Combining remote sensing and modeling for estimating surface evaporation and biomass production”, Remote Sensing of Environment, 12, 335– 353, 1995. [2] E. Boegh, H. Soegaard, N. Broge, C.B. Hasager, N.O Jensen, K. Schelde, & A. Thomsen, “Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture”, Remote Sensing of Environment, 81, 179-193, 2002. [3] A.A. Gitelson, A. Viña, S.B. Verma, D.C. Rundquist, T.J. Arkebauer, G. Keydan, B. Leavitt, V. Ciganda, G.G. Burba, and A.E. Suyker, “Relationship between gross primary production and chlorophyll content in crops: Implications for the synoptic monitoring of vegetation productivity”, Journal of Geophysical Research, 111, D08S11, 2006. [4] J. Penuelas, and I. Filella, “Visible and near-infrared reflectance techniques for diagnosing plant physiological status”, Trends in Plant Science, 3, 151-156, 1998. [5] C.S.T. Daughtry, C.K. Walthall, M.S. Kim, E. Brown de Costoun, and J.E. McMurtrey III, “Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance” Remote Sensing of Environment, 74, 229-239, 2000. [6] S.M. Moran, Y. Inoue, and E.M. Barnes, “Opportunities and limitations for image-based remote sensing in precision crop management”, Remote Sensing of Environment, 61, 319-346, 1997. [7] R. Houborg, H. Soegaard, and E. Boegh, “Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data”, Remote Sensing of Environment, 106, 39-58, 2007. [8] R. Colombo, D. Bellingeri, D. Fasolini, and C.M. Marino, “Retrieval of leaf area index in different vegetation types using high resolution satellite data”, Remote Sensing of Environment, 86, 120-131, 2003. [9] B. Combal, F. Baret, M. Weiss, A.Trubuil, D. Macé, A. Pragnère, R. Myneni, Y. Knyazikhin, and L. Wang, “Retrieval of canopy biophysical variables from bidirectional reflectance using prior information to solve the ill-posed inverse problem”, Remote Sensing of Environment, 84, 1-15, 2002. [10] B. Koetz, F. Baret, H. Poilve, and J. Hill, “Use of coupled canopy structure dynamic and radiative tran

Last Modified: 11/30/2015
Footer Content Back to Top of Page