Coupling canopy functioning and radiative transfer models for remote sensing data assimilation

Weiss, Marie; Troufleau, D.; Baret, Frédéric; Chauki, Habiba; Prévot, Laurent; Olioso, Albert; Bruguier, Nadine; Brisson, Nadine

doi:10.1016/s0168-1923(01)00234-9

Cited by 141 publications

(54 citation statements)

References 26 publications

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“…Reliance on wavelengths at the onset of the near-infrared follows from the gradual stabilization of reflectance beyond the red-edge, which settles at higher values for increased chlorophyll levels (Lamb et al, 2002). Largely similar spectral regions were structurally highlighted for N content, resulting from the inherent biochemical linkage between leaf N, chlorophyll molecules and photosynthetic capacity (Sellers et al, 1992;Weiss et al, 2001;Netto et al, 2005;Wu et al, 2008). Consequently, wavelengths positioned in the red-edge were found to be highly sensitive for chlorophyll absorption behaviour and thus to accumulation of nitrogen Zhao et al, 2014).…”

Section: Relevant Wavelengths For Plant Trait Predictionsmentioning

confidence: 99%

Remote sensing of plant trait responses to field-based plant–soil feedback using UAV-based optical sensors

et al. 2017

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Abstract. Plant responses to biotic and abiotic legacies left in soil by preceding plants is known as plant-soil feedback (PSF). PSF is an important mechanism to explain plant community dynamics and plant performance in natural and agricultural systems. However, most PSF studies are shortterm and small-scale due to practical constraints for fieldscale quantification of PSF effects, yet field experiments are warranted to assess actual PSF effects under less controlled conditions. Here we used unmanned aerial vehicle (UAV)-based optical sensors to test whether PSF effects on plant traits can be quantified remotely. We established a randomized agro-ecological field experiment in which six different cover crop species and species combinations from three different plant families (Poaceae, Fabaceae, Brassicaceae) were grown. The feedback effects on plant traits were tested in oat (Avena sativa) by quantifying the cover crop legacy effects on key plant traits: height, fresh biomass, nitrogen content, and leaf chlorophyll content. Prior to destructive sampling, hyperspectral data were acquired and used for calibration and independent validation of regression models to retrieve plant traits from optical data. Subsequently, for each trait the model with highest precision and accuracy was selected. We used the hyperspectral analyses to predict the directly measured plant height (RMSE = 5.12 cm, R 2 = 0.79), chlorophyll content (RMSE = 0.11 g m −2 , R 2 = 0.80), Ncontent (RMSE = 1.94 g m −2 , R 2 = 0.68), and fresh biomass (RMSE = 0.72 kg m −2 , R 2 = 0.56). Overall the PSF effects of the different cover crop treatments based on the remote sensing data matched the results based on in situ measurements. The average oat canopy was tallest and its leaf chlorophyll content highest in response to legacy of Vicia sativa monocultures (100 cm, 0.95 g m −2 , respectively) and in mixture with Raphanus sativus (100 cm, 1.09 g m −2 , respectively), while the lowest values (76 cm, 0.41 g m −2 , respectively) were found in response to legacy of Lolium perenne monoculture, and intermediate responses to the legacy of the other treatments. We show that PSF effects in the field occur and alter several important plant traits that can be sensed remotely and quantified in a non-destructive way using UAVbased optical sensors; these can be repeated over the growing season to increase temporal resolution. Remote sensing thereby offers great potential for studying PSF effects at field scale and relevant spatial-temporal resolutions which will facilitate the elucidation of the underlying mechanisms.

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Section: Relevant Wavelengths For Plant Trait Predictionsmentioning

confidence: 99%

Remote sensing of plant trait responses to field-based plant–soil feedback using UAV-based optical sensors

et al. 2017

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“…They also represent collaborative environments, supporting quantitative data analysis at several scales. Data assimilation will further allow the solid coupling of physical models, linking soilvegetation-atmosphere transfer (SVAT) models to state space estimation algorithms (e.g., Kalman filters) (Choudhury, 2001;Crow and Wood, 2003;Houser et al, 1998;Olioso et al, 1999;Weiss et al, 2001).…”

Section: Trends-integrated Systems Solutions and In Situ Sensingmentioning

confidence: 99%

Spectrodirectional remote sensing: From pixels to processes

Schaepman

2007

International Journal of Applied Earth Observation and Geoinfor

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This paper discusses the historical evolution of imaging spectroscopy in Earth observation as well as directional (or multiangular) research leading to current achievements in spectrodirectional remote sensing. It elaborates on the evolution from two separate research areas into a common approach to quantify the interaction of light with the Earth surface. The contribution of spectrodirectional remote sensing towards an improved understanding of the Earth System is given by discussing the benefits of converging from individual pixel analysis to process models in the land-biosphere domain. The paper concludes with an outlook of research focus and upcoming areas of interest emphasizing towards multidisciplinary approaches using integrated system solutions based on remote and in situ sensing, data assimilation, and state space estimation algorithms. Abstract This paper discusses the historical evolution of imaging spectroscopy in Earth observation as well as directional (or multiangular) research leading to current achievements in spectrodirectional remote sensing. It elaborates on the evolution from two separate research areas into a common approach to quantify the interaction of light with the Earth surface. The contribution of spectrodirectional remote sensing towards an improved understanding of the Earth System is given by discussing the benefits of converging from individual pixel analysis to process models in the land-biosphere domain. The paper concludes with an outlook of research focus and upcoming areas of interest emphasizing towards multidisciplinary approaches using integrated system solutions based on remote and in situ sensing, data assimilation, and state space estimation algorithms. #

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“…This is mostly due to the fact that an implementation of corresponding field measurements, as it has regularly been observed with spatially and temporally limited airborne spectroscopy campaigns (e.g., [8][9][10]), would not be viable in the context of the satellite-based EnMAP. This study focuses leaf area index (LAI) as it is a significant parameter describing the size of the producing layer and it is also important for the estimation of foliage cover, as well as for forecasting crop growth and yield [11,12]. Investigation of LAI thus potentially promotes the understanding of biophysical processes in canopies.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Seasonal Leaf Area Index from Simulated EnMAP Data through Optimized LUT-Based Inversion of the PROSAIL Model

et al. 2015

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Abstract:The upcoming satellite mission EnMAP offers the opportunity to retrieve information on the seasonal development of vegetation parameters on a regional scale based on hyperspectral data. This study aims to investigate whether an analysis method for the retrieval of leaf area index (LAI), developed and validated on the 4 m resolution scale of six airborne datasets covering the 2012 growing period, is transferable to the spaceborne 30 m resolution scale of the future EnMAP mission. The widely used PROSAIL model is applied to generate look-up-table (LUT) libraries, by which the model is inverted to derive LAI information. With the goal of defining the impact of different selection criteria in the inversion process, different techniques for the LUT based inversion are tested, such as several cost functions, type and amount of artificial noise, number of considered solutions and type of averaging method. The optimal inversion procedure (Laplace, median, 4% inverse multiplicative noise, 350 out of 100,000 averages) is identified by validating the results against corresponding in-situ measurements (n = 330) of LAI. Finally, the best performing LUT inversion (R 2 = 0.65, RMSE = 0.64) is adapted to simulated EnMAP data, generated from the airborne acquisitions. The comparison of the retrieval results to upscaled maps of LAI, previously validated on the 4 m scale, shows that the optimized retrieval method can successfully be transferred to spaceborne EnMAP data. OPEN ACCESSRemote Sens. 2015, 7 10322

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Coupling canopy functioning and radiative transfer models for remote sensing data assimilation

Cited by 141 publications

References 26 publications

Remote sensing of plant trait responses to field-based plant–soil feedback using UAV-based optical sensors

Remote sensing of plant trait responses to field-based plant–soil feedback using UAV-based optical sensors

Spectrodirectional remote sensing: From pixels to processes

Retrieval of Seasonal Leaf Area Index from Simulated EnMAP Data through Optimized LUT-Based Inversion of the PROSAIL Model

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