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Title:   Author  item
                                            HOUBORG, RASMUSitem
                                            Anderson, Martha
  Submitted to: Journal of Applied Remote Sensing (JARS)
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 6/1/2009
                                    Publication  Date: 6/15/2009
                                    Citation: Houborg, R., Anderson, M.C. 2009. Utility of an image-based canopy reflectance modeling tool for remote estimation and LAI and leaf chlorophyll content at regional scales. Journal of Applied Remote Sensing. 2009 CDROM.Interpretive Summary: Radiance data recorded by remote sensors function as a unique source for monitoring the terrestrial biosphere and vegetation dynamics at a range of spatial and temporal scales. This paper describes a novel physically-based approach for estimating LAI and leaf chlorophyll at regional scales. The integrated system of radiative transfer models (atmosphere – canopy – leaf) facilitates canopy biophysical retrievals directly from at-sensor data in three broad spectral bands (green, red and near-infrared) present on most airborne and operational satellite sensors. The model system requires no calibration and may be run for any locality with availability of standard atmospheric state data (i.e. aerosol optical depth, aerosol type, precipitable water vapor, ozone content), a land cover classification and soil map. Applications to agricultural and natural vegetation areas in Maryland and Oklahoma during 2007 demonstrated good utility in detecting spatial and temporal variations in LAI and leaf chlorophyll for a diversity of land cover types. The generated biophysical maps may assist precision crop management and drought and vegetation productivity monitoring at larger scales.
                                Technical Abstract: Radiance data recorded by remote sensors function as a unique source for monitoring the terrestrial biosphere and vegetation dynamics at a range of spatial and temporal scales. A key challenge is to relate the remote sensing signal to critical variables describing land surface vegetation canopies such as leaf area index (LAI) and total leaf chlorophyll content (Cab). This paper describes a novel physically-based approach for estimating LAI and Cab at regional scales that relies on remotely sensed data in the reflective solar domain acquirable from a suite of aircraft and operational satellite sensors. The REGularized canopy reFLECtance (REGFLEC) modeling tool integrates leaf optics (PROSPECT), canopy reflectance (ACRM), and atmospheric radiative transfer (6SV1) model components, facilitating the direct use of at-sensor radiances in green, red and near-infrared wavelengths. REGFLEC adopts a multi-step LUT-based inversion approach and incorporates image-based techniques to reduce the confounding effects of land cover specific vegetation parameters (leaf structure, vegetation clumping, leaf inclination angle and brown pigment concentration of senescent leaf material) and soil reflectance. REGFLEC was applied to agricultural and natural vegetation areas in Maryland (39.0'N, 76.9'W) and Oklahoma (35.2'N, 98.4'W) during 2007. Regional maps were generated using 10 m and 20 m resolution SPOT imagery, and variable environmental and plant development conditions allowed for model validation over a wide range in LAI (0 – 6) and Cab (20 – 75 μg cm-2). Validation against in-situ measurements in fields of corn, wheat, soybean, grass, alfalfa, peanuts and cotton yielded relative root-mean-square deviations on the order of 13% (0.4) for LAI and between 11 – 19% (4.9 – 9.1 μg cm-2) for Cab. REGFLEC demonstrated good utility in detecting spatial and temporal variations in LAI and Cab without requiring site-specific data for calibration. The physical approach presented here can quite easily be applied to other regions and has the potential of being more universally applicable than traditional empirical approaches for retrieving LAI and chlorophyll content.


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