Retrieval of canopy biophysical variables from bidirectional reflectance

Combal, B.; Baret, Frédéric; Weiss, Marie; Trubuil, Alain; Macé, D.; Pragnère, Agnès; Myneni, Ranga B.; Knyazikhin, Yuri; Wang, L

doi:10.1016/s0034-4257(02)00035-4

Cited by 560 publications

(128 citation statements)

References 21 publications

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“…Before the LUT generation, several model parameters were set to default values or values derived from prior or expert knowledge in order to reduce the ill-posedness of the inversion problem (Combal et al, 2003). Cbp was set to zero since no senescent leaves were visible during the field experiments.…”

Section: Prosail Based Approachmentioning

confidence: 99%

Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping

Jay

Maupas

Bendoula³

et al. 2017

Field Crops Research

198

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Remote sensing has gained much attention for agronomic applications such as crop management or yield estimation. Crop phenotyping under field conditions has recently become another important application that requires specific needs: the considered remote-sensing method must be (1) as accurate as possible so that slight differences in phenotype can be detected and related to genotype, and (2) robust so that thousands of cultivars potentially quite different in terms of plant architecture can be characterized with a similar accuracy over different years and soil and weather conditions. In this study, the potential of nadir and off-nadir ground-based spectro-radiometric measurements to remotely sense five plant traits relevant for field phenotyping, namely, the leaf area index (LAI), leaf chlorophyll and nitrogen contents, and canopy chlorophyll and nitrogen contents, was evaluated over fourteen sugar beet (Beta vulgaris L.) cultivars, two years and three study sites. Among the diversity of existing remote-sensing methods, two popular approaches based on various selected Vegetation Indices (VI) and PROSAIL inversion were compared, especially in the perspective of using them for phenotyping applications.Overall, both approaches are promising to remotely estimate LAI and canopy chlorophyll content ( ≤ 10 %). In addition, VIs show a great potential to retrieve canopy nitrogen content ( = 10 %). On the other hand, the estimation of leaf-level quantities is less accurate, the best accuracy being obtained for leaf chlorophyll content estimation based on VIs ( = 17 %). As expectedAuthor-produced version of the article published in Field Crops Research, 2017, N°210, p.33-46. The original publication is available at http://www.sciencedirect.com Doi: 10.1016Doi: 10. /j.fcr.2017 when observing the relationship between leaf chlorophyll and nitrogen contents, poor correlations are found between VIs and mass-based or area-based leaf nitrogen content. Importantly, the estimation accuracy is strongly dependent on sun-sensor geometry, the structural and biochemical plant traits being generally better estimated based on nadir and off-nadir observations, respectively.Ultimately, a preliminary comparison tends to indicate that, providing that enough samples are included in the calibration set, (1) VIs provide slightly more accurate performances than PROSAIL inversion, (2) VIs and PROSAIL inversion do not show significant differences in robustness across the different cultivars and years. Even if more data are still necessary to draw definitive conclusions, the results obtained with VIs are promising in the perspective of high-throughput phenotyping using UAVembedded multispectral cameras, with which only a few wavebands are available.

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Section: Prosail Based Approachmentioning

confidence: 99%

Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping

Jay

Maupas

Bendoula³

et al. 2017

Field Crops Research

198

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“…The look-up table (LUT) method is widely used in remote sensing research (e.g. Combal et al, 2003;Bertrand and Royer, 2004;Chander et al, 2004;Mobley et al, 2005). NDVI and LAI have been obtained successfully using remote sensing images.…”

Section: Parameterizations and Evaluationsmentioning

confidence: 99%

Evaluation of MOD16 algorithm using MODIS and ground observational data in winter wheat field in North China Plain

Sun

Wang

Ouyang

et al. 2007

Hydrological Processes

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Abstract:Evapotranspiration (ET ) is an important factor for understanding hydrological processes and climate dynamics. Remote sensing is considered as the most promising tool for estimation of ET over a large spatial scale. The purpose of this study is to analyse and evaluate the MOD16 algorithm, developed by Nishida et al. (2003a,b), in winter wheat fields by using MODIS land products, MOD11-land surface temperature and MOD13-standard normalized difference vegetation index (NDVI), as well as observations at the Yucheng Experimental Station, China in 2002. The original MOD16 is a dual-source ET model in which the evaporation fraction (EF ) for bare soil surface is estimated using the VI-Ts diagram method, and EF for vegetation is determined by a function of canopy resistance, aerodynamic resistance and air temperature. We analysed a radiation budget sub-model and found that the estimate for solar radiation is acceptable only on cloud-free days. We also found that, in the original MOD16, seasonal variations of minimum canopy resistance and physiological temperatures are not considered, which results in overestimation of canopy resistance when leaf area index (LAI) is less than 2Ð5. Because the strong unstable thermal stratification over the dry bare soil surface is ignored in the original algorithm, wind speed is overestimated and aerodynamic resistance above the vegetation surface is underestimated. We then modified the original MOD16 algorithm. The result shows that both EF and ET for vegetation estimated with the modified algorithm are in consistent with both the observations of an eddy covariance system and the calculations using the Penman-Monteith method.

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“…8 The canopy LAI can be spectrally estimated by means of empirical-statistical methods 9,10 or through inversion of physically-based radiative transfer models. [11][12][13][14][15] Most empirical-statistical methods rely on vegetation indices, either formulated using classical red and near infrared (nIr) wavebands 16 or optimised through band selection in the hyperspectral feature space. [17][18][19] Still other studies use features in the red-edge to estimate canopy bio-physical variables 20,21 or establish multivariate regression approaches.…”

Section: Jsimentioning

confidence: 99%

“…11,42 Second, the inversion process is generally ill-posed. 12,43 The ill-posedness of the inverse problem leads to unstable model inversions, since important model parameters [especially LAI, soil brightness and the average leaf inclination angle (ALA)] counterbalance each other. 44,45 To regularise the inversion, one may use prior information, for example derived from knowledge of crop type and development stage.…”

Section: Jsimentioning

confidence: 99%

“…44,45 To regularise the inversion, one may use prior information, for example derived from knowledge of crop type and development stage. 12,25 alternatively, it is possible to obtain additional information from the data itself, for example by analysing the colour texture of pixels representing the same agricultural field. 43,46 To invert rTMs, a range of techniques has been proposed (for reviews see references 47 and 48).…”

Section: Jsimentioning

confidence: 99%

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Inverting the PROSAIL canopy reflectance model using neural nets trained on streamlined databases

Atzberger

2010

J. Spectral Imaging

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all rights reserved JSI By providing both spatial and temporal information, remote sensing may function as a cost-effective source of data for precision agriculture with potentially positive benefits for both the farmer and the environment.1,2 amongst other bio-physical variables, the leaf area index (LAI) is an important indicator of actual crop growth, because it is related to ground coverage and photosynthetically active radiation (Par) interception efficiency. 3,4 remotely sensed maps of the LAI are therefore considered helpful for decision making and yield predictions. 5,6 To assess crop productivity at regional to global scales, mapped temporal LAI profiles were, for example, assimilated into mechanistic crop growth models 7 or integrated into Par interception formulae. 8The canopy LAI can be spectrally estimated by means of empirical-statistical methods 9,10 or through inversion of physically-based radiative transfer models.11-15 Most empirical-statistical methods rely on vegetation indices, either formulated using classical red and near infrared (nIr) wavebands 16 or optimised through band selection in the hyperspectral feature space. [17][18][19] Still other studies use features in the red-edge to estimate canopy bio-physical variables 20,21 or establish multivariate regression approaches. The widely used PROSAIL radiative transfer model was coupled with a simple soil reflectance parameterisation to estimate the leaf area index (LAI) of winter wheat (Triticum aestivum) from ground-based spectrometer data. To avoid time-consuming numerical optimisations, a neural net (NN) was used for model inversion. The NN was trained on 3000 spectral patterns generated by the reflectance model. The training database was previously streamlined to provide good approximation of the response surface while keeping the net compact. Streamlining was achieved by retaining only those synthetic spectra that belong both to the simulated and actual measurement spaces. The estimated LAI (n obs = 15) compared well with completely independent reference measurements taken four times during the 2000 growing season in four commercial winter wheat fields (1.8 ≤ LAI ≤ 8.1). The coefficient of determination (R 2 ) between measured and estimated LAI was 0.87 with a root mean squared error (RMSE) of 0.89 (m 2 m -2 ). Even for LAIs exceeding 3-4, saturation effects were low. Three measurement dates yielded RMSE lower than 0.8. Only during stem elongation did RMSE exceed 1. Higher errors for this time period were attributed to abrupt changes in the canopy structure (i.e. average leaf angle) not taken into account. Compared to the normalised difference vegetation index (NDVI), the inversion of PROSAIL using hyperspectral reflectances performed well, with errors reduced by more than 50% as compared to the NDVI model (RMSE: 1.91 m 2 m -2 ).

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Retrieval of canopy biophysical variables from bidirectional reflectance

Cited by 560 publications

References 21 publications

Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping

Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping

Evaluation of MOD16 algorithm using MODIS and ground observational data in winter wheat field in North China Plain

Inverting the PROSAIL canopy reflectance model using neural nets trained on streamlined databases

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