Use of coupled canopy structure dynamic and radiative transfer models to estimate biophysical canopy characteristics

Koetz, Benjamin; Baret, Frédéric; Poilvé, Hervé; Hill, Joachim

doi:10.1016/j.rse.2004.11.017

Cited by 213 publications

(128 citation statements)

References 42 publications

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“…The view zenith angle was set at hotspot position ie 30 o in the backscattering direction of the principal plane (ie relative azimuth of 0°). As during the course of the crop season the sun illumination geometry varied, four different LUTs were built, each corresponding to four specific dates of observation (Koetz et al, 2005). The sun zenith angles were 20, 27, 30, and 37° corresponding to 52, 66, 84, and 100 DAS during the growing season of soybean crop.…”

Section: Methodsmentioning

confidence: 99%

Comparative Evaluation of Inversion Approaches of the Radiative Transfer Model for Estimation of Crop Biophysical Parameters

Mridha¹,

Sahoo²,

Sehgal³

et al. 2015

International Agrophysics

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A b s t r a c t. The inversion of canopy reflectance models is widely used for the retrieval of vegetation properties from remote sensing. This study evaluates the retrieval of soybean biophysical variables of leaf area index, leaf chlorophyll content, canopy chlorophyll content, and equivalent leaf water thickness from proximal reflectance data integrated broadbands corresponding to moderate resolution imaging spectroradiometer, thematic mapper, and linear imaging self scanning sensors through inversion of the canopy radiative transfer model, PROSAIL. Three different inversion approaches namely the look-up table, genetic algorithm, and artificial neural network were used and performances were evaluated. Application of the genetic algorithm for crop parameter retrieval is a new attempt among the variety of optimization problems in remote sensing which have been successfully demonstrated in the present study. Its performance was as good as that of the look-up table approach and the artificial neural network was a poor performer. The general order of estimation accuracy for parameters irrespective of inversion approaches was leaf area index > canopy chlorophyll content > leaf chlorophyll content > equivalent leaf water thickness. Performance of inversion was comparable for broadband reflectances of all three sensors in the optical region with insignificant differences in estimation accuracy among them.

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Section: Methodsmentioning

confidence: 99%

Comparative Evaluation of Inversion Approaches of the Radiative Transfer Model for Estimation of Crop Biophysical Parameters

Mridha¹,

Sahoo²,

Sehgal³

et al. 2015

International Agrophysics

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“…Since the 1980s, remote sensing techniques with low-cost and good temporal availability have been frequently used for the repetitive monitoring of vegetation dynamics and plant phenology modelling [32][33][34]. Measured by the remote sensors, the characteristics of vegetation dynamics are usually represented by vegetative indicators (VIs).…”

Section: Introductionmentioning

confidence: 99%

“…Houborg and Boegh [44] retrieved the LAI data using the reflectance data with inverse and forward canopy reflectance models and obtained the reliable quantitative estimates of LAI with an overall root mean square deviation of 0.74. Koetz et al [34] integrated the canopy structure dynamics and radiative transfer models to simulate the LAI data using the air temperature and reflectance data with an average R 2 of 0.663. As the integration between climate variables and remote sensing data, temperature as a climate control for phenology has been studied for a long time [19,45].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal LAI Modelling by Integrating Climate and MODIS LAI Data in a Mesoscale Catchment

Sun

Schulz

2017

Remote Sensing

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Vegetation is often represented by the leaf area index (LAI) in many ecological, hydrological and meteorological land surface models. However, the spatio-temporal dynamics of the vegetation are important to represent in these models. While the widely applied methods, such as the Canopy Structure Dynamic Model (CSDM) and the Double Logistic Model (DLM) are solely based on cumulative daily mean temperature data as input, a new spatio-temporal LAI prediction model referred to as the Temperature Precipitation Vegetation Model (TPVM) is developed that also considers cumulative precipitation data as input into the modelling process. TPVM as well as CDSM and DLM model performances are compared and evaluated against filtered LAI data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The calibration/validation results of a cross-validation performed in the meso-scale Attert catchment in Luxembourg indicated that the DLM and TPVM generally provided more realistic and accurate LAI data. The TPVM performed superiorly for the agricultural land cover types compared to the other two models, which only used the temperature data. The Pearson's correlation coefficient (CC) between TPVM and the field measurement is 0.78, compared to 0.73 and 0.69 for the DLM and CSDM, respectively. The phenological metrics were derived from the TPVM model to investigate the interaction between the climate variables and the LAI variations. These interactions illustrated the dominant control of temperature on the LAI dynamics for deciduous forest cover, and a combined influence of temperature with precipitation for the agricultural land use areas.

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“…Although hyperspectral remote sensing systems are known to provide additional information on vegetation vitality in waveband ranges that are not accounted for by most multispectral sensors [35,36], up to date such systems are rarely used on an operational basis for the characterization of agricultural crops. Several sources of a priori information about the variables can be considered for remote sensing applications, including ancillary data measured on site, estimates provided by another sensor, knowledge of the dynamic evolution of the biophysical variables over time [37,38], and knowledge about the typical distribution of the input variables in a particular development stage [24]. Land cover classification schemes help to split the problem into sub-domains for which prior information is attributed separately [39,40].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Automated Canopy Characterization from Hyperspectral Data by a Novel Two Step Radiative Transfer Model Inversion Approach

Dorigo

Richter

Baret³

et al. 2009

Remote Sensing

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Automated, image based methods for the retrieval of vegetation biophysical and biochemical variables are often hampered by the lack of a priori knowledge about land cover and phenology, which makes the retrieval a highly underdetermined problem. This study addresses this problem by presenting a novel approach, called CRASh, for the concurrent retrieval of leaf area index, leaf chlorophyll content, leaf water content and leaf dry matter content from high resolution solar reflective earth observation data. CRASh, which is based on the inversion of the combined PROSPECT+SAILh radiative transfer model (RTM), explores the benefits of combining semi-empirical and physically based approaches. The approach exploits novel ways to address the underdetermined problem in the context of an automated retrieval from mono-temporal high resolution data. To regularize the inverse problem in the variable domain, RTM inversion is coupled with an OPEN ACCESSRemote Sens. 2009, 1 1140 automated land cover classification. Model inversion is based on a two step lookup table (LUT) approach: First, a range of possible solutions is selected from a previously calculated LUT based on the analogy between measured and simulated reflectance. The final solution is determined from this subset by minimizing the difference between the variables used to simulate the spectra contained in the reduced LUT and a first guess of the solution. This first guess of the variables is derived from predictive semi-empirical relationships between classical vegetation indices and the single variables. Additional spectral regularization is obtained by the use of hyperspectral data. Results show that estimates obtained with CRASh are significantly more accurate than those obtained with a tested conventional RTM inversion and semi-empirical approach. Accuracies obtained in this study are comparable to the results obtained by various authors for better constrained inversions that assume more a priori information. The completely automated and image-based nature of the approach facilitates its use in operational chains for upcoming high resolution airborne and spaceborne imaging spectrometers.

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Use of coupled canopy structure dynamic and radiative transfer models to estimate biophysical canopy characteristics

Cited by 213 publications

References 42 publications

Comparative Evaluation of Inversion Approaches of the Radiative Transfer Model for Estimation of Crop Biophysical Parameters

Comparative Evaluation of Inversion Approaches of the Radiative Transfer Model for Estimation of Crop Biophysical Parameters

Spatio-Temporal LAI Modelling by Integrating Climate and MODIS LAI Data in a Mesoscale Catchment

Enhanced Automated Canopy Characterization from Hyperspectral Data by a Novel Two Step Radiative Transfer Model Inversion Approach

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