Consistent estimation of multiple parameters from MODIS top of atmosphere reflectance data using a coupled soil-canopy-atmosphere radiative transfer model

Shi, Hanyu; Xiao, Zhiqiang; Liang, Shunlin; Zhang, Xiaolin

doi:10.1016/j.rse.2016.06.008

Cited by 40 publications

(12 citation statements)

References 73 publications

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“…There have been several studies conducting GSA in bands of certain sensors at TOC level: PROSAIL was used for Landsat TM [19], Sentinel-2 MSI [21], REIS of RapidEye [22] and WVC of HJ-1 [23], PARAS model based on the spectral invariants theory was used for Landsat ETM+ [20] and leaf canopy model for several bands of MODIS [18]. At TOA level another soil-leaf-canopy model with Hapke soil model and two types of leaves (green and brown) was used for Hyperion [26], CHRIS and Landsat TM sensors [27] and a coupled leaf-canopy-atmosphere model where most of the leaf parameters are calculated as percentages of specific leaf weight for MODIS [30]. Due to the narrow bandwidth of Sentinel-3 instruments (average bandwidth is 10 nm) and different models that were used for MODIS instrument direct comparison of the mentioned results with ours is complicated, thus we will discuss only the works devoted to MSI and CHRIS, which have comparable characteristics to Sentinel-3.…”

Section: Olci and Slstr Bandsmentioning

confidence: 99%

“…When dealing with satellite data it is necessary to account for the effect of the atmosphere. Several studies have quantified the atmospheric effects by simulating the propagation of top of canopy (TOC) reflectance to top of atmosphere (TOA) radiance using the models MODTRAN [24,25] for Hyperion on EO-1 (decommissioned in 2017) [26], CHRIS on Proba-1, TM on Landsat 5 and ASTER on Terra [27] and 6S [28] for VEGETATION on SPOT (decomissioned in 2015) [29], MODIS on Aqua and Terra (bands 1-7) [30] and vegetation indices derived from TM and ETM+ on Landsat 5 and 7 respectively [31]. GSA of full range PROSAIL-MODTRAN TOA radiance spectra (400-2500 nm) has recently been reported [32].…”

Section: Introductionmentioning

confidence: 99%

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Global Sensitivity Analysis of the SCOPE Model in Sentinel-3 Bands: Thermal Domain Focus

Prikaziuk¹,

Tol²

2019

Remote Sensing

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Sentinel-3 satellite has provided simultaneous observations in the optical (visible, near infrared (NIR), shortwave infrared (SWIR)) and thermal infrared (TIR) domains since 2016, with a revisit time of 1–2 days. The high temporal resolution and spectral coverage make the data of this mission attractive for vegetation monitoring. This study explores the possibilities of using the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model together with Sentinel-3 to exploit the two sensors onboard of Sentinel-3 (the ocean and land color instrument (OLCI) and sea and land surface temperature radiometer (SLSTR)) in synergy. Sobol’ variance based global sensitivity analysis (GSA) of top of atmosphere (TOA) radiance produced with a coupled SCOPE-6S model was conducted for optical bands of OLCI and SLSTR, while another GSA of SCOPE was conducted for the land surface temperature (LST) product of SLSTR. The results show that in addition to ESA level-2 Sentinel-3 products, SCOPE is able to retrieve leaf area index (LAI), leaf chlorophyll content (Cab), leaf water content (Cw), leaf senescent material (Cs), leaf inclination distribution (LAD). Leaf dry matter content (Cdm) and soil brightness, despite being important, were not confidently retrieved in some cases. GSA of LST in TIR domain showed that plant biochemical parameters—maximum carboxylation rate (Vcmax) and stomata conductance-photosynthesis slope (Ball-Berry m)—can be constrained if prior information on near-surface weather conditions is available. We conclude that the combination of optical and thermal domains facilitates the constraint of the land surface energy balance using SCOPE.

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Section: Olci and Slstr Bandsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Global Sensitivity Analysis of the SCOPE Model in Sentinel-3 Bands: Thermal Domain Focus

Prikaziuk¹,

Tol²

2019

Remote Sensing

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“…EFAST is a variance decomposition method determining what fraction of the variance in the model output can be explained by the variation in each input parameter (i.e., partial variance). The basis of the EFAST method is a parametric transformation that can reduce multidimensional integrals over the input parametric space to one-dimensional quadratures using a search curve that scans the whole input space [52]. Scanning is conducted so that each axis of the parametric space is explored at a different frequency.…”

Section: Model Sensitivity Analysismentioning

confidence: 99%

Impacts of Leaf Age on Canopy Spectral Signature Variation in Evergreen Chinese Fir Forests

Song

et al. 2018

Remote Sensing

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Significant gaps exist in our knowledge of the impact of leaf aging on canopy signal variability, which limits our understanding of vegetation status based on remotely sensed data. To understand the effects of leaf aging at the leaf and canopy scales, a combination of field, remote-sensing and physical modeling techniques was adopted to assess the canopy spectral signals of evergreen Cunninghamia forests. We observed an approximately 10% increase in Near-Infrared (NIR) reflectance for new leaves and a 35% increase in NIR transmittance for mature leaves from May to October. When variations in leaf optical properties (LOPs) of only mature leaves, or both new and mature leaves were considered, the Geometric Optical and Radiative Transfer (GORT) model-simulated canopy reflectance trajectory was more consistent with Landsat observations (R 2 increased from 0.37 to 0.82~0.89 for NIR reflectance, and from 0.35 to 0.67~0.88 for EVI2, with a small RMSE (0.01 to 0.02)). This study highlights the importance of leaf age on leaf spectral signatures, and provides evidence of age-dependent LOPs that have important impacts on canopy reflectance in the NIR band and EVI2, which are used to monitor canopy dynamics and productivity, with important implications for RS and forest ecosystem ecology.

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“…Various approaches have been developed to estimate the parameter through algorithm ensembles or integration of the estimates from the individual algorithms (Liang et al 2017). Progress has been made to integrate multiple satellite data (Ma, Liu, et al 2017) and estimate a group of variables simultaneously through data assimilation approaches (Lewis et al 2012;Ma et al 2018;Shi, Xiao, et al 2016;Shi et al 2017;Xiao et al 2015). In light of virtual constellation, research in this direction deserves special attention.…”

Section: Challenges and Outlookmentioning

confidence: 99%

Remote sensing of earth’s energy budget: synthesis and review

Liang

Wang

et al. 2019

International Journal of Digital Earth

Self Cite

149

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The Earth's climate is largely determined by its energy budget. Since the 1960s, satellite remote sensing has been used in estimating these energy budget components at both the top of the atmosphere (TOA) and the surface. Besides the broadband sensors that have been traditionally used for monitoring Earth's Energy Budget (EEB), data from a variety of narrowband sensors aboard both polar-orbiting and geostationary satellites have also been extensively employed to estimate the EEB components. This paper provides a comprehensive review of the satellite missions, state-of-the art estimation algorithms and the satellite products, and also synthesizes current understanding of the EEB and spatiotemporal variations. The TOA components include total solar irradiance, reflected shortwave radiation/planetary albedo, outgoing longwave radiation, and energy imbalance. The surface components include incident solar radiation, shortwave albedo, shortwave net radiation, longwave downward and upwelling radiation, land and sea surface temperature, surface emissivity, all-wave net radiation, and sensible and latent heat fluxes. Some challenges, and outlook such as virtual constellation of different satellite sensors, temporal homogeneity tests of long time-series products, algorithms ensemble, and products intercomparison are also discussed.

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Consistent estimation of multiple parameters from MODIS top of atmosphere reflectance data using a coupled soil-canopy-atmosphere radiative transfer model

Cited by 40 publications

References 73 publications

Global Sensitivity Analysis of the SCOPE Model in Sentinel-3 Bands: Thermal Domain Focus

Global Sensitivity Analysis of the SCOPE Model in Sentinel-3 Bands: Thermal Domain Focus

Impacts of Leaf Age on Canopy Spectral Signature Variation in Evergreen Chinese Fir Forests

Remote sensing of earth’s energy budget: synthesis and review

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