Multi-temporal, multi-sensor retrieval of terrestrial vegetation properties from spectral–directional radiometric data

Mousivand, Alijafar; Menenti, Massimo; Gorte, B.G.H.; Verhoef, W.

doi:10.1016/j.rse.2014.10.030

Cited by 53 publications

(46 citation statements)

References 68 publications

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“…In fact, ELEV and sun-target-sensor geometry (VZA, SZA and RAA) are typically known and thus kept fixed in applications. Similarly, by discarding insensitive variables from the sampling scheme it is possible to simplify the computational load and inversion problem for mapping applications (e.g., as in [26,78]). …”

Section: Interpreting Sensitivity Analysis Resultsmentioning

confidence: 99%

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Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis

et al. 2016

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Abstract:Physically-based radiative transfer models (RTMs) help understand the interactions of radiation with vegetation and atmosphere. However, advanced RTMs can be computationally burdensome, which makes them impractical in many real applications, especially when many state conditions and model couplings need to be studied. To overcome this problem, it is proposed to substitute RTMs through surrogate meta-models also named emulators. Emulators approximate the functioning of RTMs through statistical learning regression methods, and can open many new applications because of their computational efficiency and outstanding accuracy. Emulators allow fast global sensitivity analysis (GSA) studies on advanced, computationally expensive RTMs. As a proof-of-concept, three machine learning regression algorithms (MLRAs) were tested to function as emulators for the leaf RTM PROSPECT-4, the canopy RTM PROSAIL, and the computationally expensive atmospheric RTM MODTRAN5. Selected MLRAs were: kernel ridge regression (KRR), neural networks (NN) and Gaussian processes regression (GPR). For each RTM, 500 simulations were generated for training and validation. The majority of MLRAs were excellently validated to function as emulators with relative errors well below 0.2%. The emulators were then put into a GSA scheme and compared against GSA results as generated by original PROSPECT-4 and PROSAIL runs. NN and GPR emulators delivered identical GSA results, while processing speed compared to the original RTMs doubled for PROSPECT-4 and tripled for PROSAIL. Having the emulator-GSA concept successfully tested, for six MODTRAN5 atmospheric transfer functions (outputs), i.e., direct and diffuse at-surface solar irradiance (E di f , E dir ), direct and diffuse upward transmittance (T dir , T di f ), spherical albedo (S) and path radiance (L 0 ), the most accurate MLRA's were subsequently applied as emulator into the GSA scheme. The sensitivity analysis along the 400-2500 nm spectral range took no more than a few minutes on a contemporary computer-in comparison, the same analysis in the original MODTRAN5 would have taken over a month. Key atmospheric drivers were identified, which are on the one hand aerosol optical properties, i.e., aerosol optical thickness (AOT), Angstrom coefficient (AMS) and scattering asymmetry variable (G), mostly driving diffuse atmospheric components, E di f and T di f ; and those affected by atmospheric scattering, L 0 and S. On the other hand, as expected, AOT, AMS and columnar water vapor (CWV) in the absorption regions mostly drive E dir and T dir atmospheric functions. The presented emulation schemes showed very promising results in replacing costly RTMs, and we think they can contribute to the adoption of machine learning techniques in remote sensing and environmental applications.

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Section: Interpreting Sensitivity Analysis Resultsmentioning

confidence: 99%

“…By identifying variables of lesser influence, models can be greatly simplified, which facilitates practical applications such as inversion [26]. To achieve this, a sensitivity analysis is required.…”

Section: Introductionmentioning

confidence: 99%

Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis

et al. 2016

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“…These so-called "regularisation" methods [47][48][49][50][51][52][53][54] assume temporal and/or spatial correlation as part of the prior distribution, resulting in a much reduced uncertainty [16,21]. In a similar vein, there are DA methods that exploit predictions of the land surface state from a dynamic vegetation model (typically a function of LAI, FAPAR) [55].…”

Section: The Eo-ldas Approachmentioning

confidence: 99%

Estimation of FAPAR over Croplands Using MISR Data and the Earth Observation Land Data Assimilation System (EO-LDAS)

et al. 2017

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Abstract:The Fraction of Absorbed Photosynthetically-Active Radiation (FAPAR) is an important parameter in climate and carbon cycle studies. In this paper, we use the Earth Observation Land Data Assimilation System (EO-LDAS) framework to retrieve FAPAR from observations of directional surface reflectance measurements from the Multi-angle Imaging SpectroRadiometer(MISR) instrument. The procedure works by interpreting the reflectance data via the semi-discrete Radiative Transfer (RT) model, supported by a prior parameter distribution and a dynamic regularisation model and resulting in an inference of land surface parameters, such as effective Leaf Area Index (LAI), leaf chlorophyll concentration and fraction of senescent leaves, with full uncertainty quantification. The method is demonstrated over three agricultural FLUXNET sites, and the EO-LDAS results are compared with eight years of in situ measurements of FAPAR and LAI, resulting in a total of 24 site years. We additionally compare three other widely-used EO FAPAR products, namely the MEdium Resolution Imaging Spectrometer (MERIS) Full Resolution, the MISR High Resolution (HR) Joint Research Centre Two-stream Inversion Package (JRC-TIP) and MODIS MCD15 FAPAR products. The EO-LDAS MISR FAPAR retrievals show a high correlation with the ground measurements (r 2 > 0.8), as well as the lowest average RMSE (0.14), in line with the MODIS product. As the EO-LDAS solution is effectively interpolated, if only measurements that are coincident with MISR observations are considered, the correlation increases (r 2 > 0.85); the RMSE is lower by 4-5%; and the bias is 2% and 7%. The EO-LDAS MISR LAI estimates show a strong correlation with ground-based LAI (average r 2 = 0.76), but an underestimate of LAI for optically-thick canopies due to saturation (average RMSE = 2.23). These results suggest that the EO-LDAS approach is successful in retrieving both FAPAR and other land surface parameters. A large part of this success is based on the use of a dynamic regularisation model that counteracts the poor temporal sampling from the MISR instrument.

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“…The MODIS LAI values are overestimated by about 12% (RMSE = 0.66) than field measurements when all biomes are taken into consideration [15]. In addition, the accuracy of [21]. The RMSE of LAI inversions from multi-sensor dataset in this study with field measurements was 0.42, and the accuracy of the LAI inversion was convincing.…”

Section: Comparison Of Lai Inversions With Existing Studiesmentioning

confidence: 50%

“…Therefore, an effective method to increase the number of observations with 30 m spatial resolution is to use multi-sensor observations. For example, Mousivand et al is producing vegetation state variables (including LAI, fCover and chlorophyll content) using CHRIS, TM and ASTER images over the agricultural test site in Barrax, Spain [21].…”

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confidence: 99%

Leaf Area Index Retrieval Combining HJ1/CCD and Landsat8/OLI Data in the Heihe River Basin, China

Zhao

Liu

et al. 2015

Remote Sensing

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Abstract:The primary restriction on high resolution remote sensing data is the limit observation frequency. Using a network of multiple sensors is an efficient approach to increase the observations in a specific period. This study explores a leaf area index (LAI) inversion method based on a 30 m multi-sensor dataset generated from HJ1/CCD and Landsat8/OLI, from June to August 2013 in the middle reach of the Heihe River Basin, China. The characteristics of the multi-sensor dataset, including the percentage of valid observations, the distribution of observation angles and the variation between different sensor observations, were analyzed. To reduce the possible discrepancy between different satellite sensors on LAI inversion, a quality control system for the observations was designed. LAI is retrieved from the high quality of single-sensor observations based on a look-up table constructed by a unified model. The averaged LAI inversion over a 10-day period is set as the synthetic LAI value. The percentage of valid LAI inversions increases significantly from 6.4% to 49.7% for single-sensors to 75.9% for multi-sensors. LAI Keywords: multi-sensor dataset; the middle reach of the Heihe River Basin; leaf area index; HJ1/CCD; Landsat8/OLI

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Multi-temporal, multi-sensor retrieval of terrestrial vegetation properties from spectral–directional radiometric data

Cited by 53 publications

References 68 publications

Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis

Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis

Estimation of FAPAR over Croplands Using MISR Data and the Earth Observation Land Data Assimilation System (EO-LDAS)

Leaf Area Index Retrieval Combining HJ1/CCD and Landsat8/OLI Data in the Heihe River Basin, China

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