Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC)

Houborg, Rasmus; McCabe, Matthew; Cescatti, Alessandro; Gao, Feng; Schull, Mitchell A.; Gitelson, Anatoly A.

doi:10.1016/j.rse.2014.12.008

Cited by 124 publications

(108 citation statements)

References 124 publications

(167 reference statements)

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“…Leaf and canopy chlorophyll have also been shown to be useful quantities for constraining the nominal LUE (β n ) over the course of the growing season (Gitelson et al, 2006Houborg et al, 2011Houborg et al, , 2013Monteith, 1972Monteith, , 1977Peng et al, 2011;Peng and Gitelson, 2012). Chlorophyll is a vital pigment in the photosynthetic apparatus, and advances in the retrieval of leaf and canopy chlorophyll from remote sensing data (Houborg et al, 2015) make it extremely amenable for the ultimate goal of mapping fluxes over larger areas. Houborg et al (2011) demonstrated the utility of using remotely sensed maps of leaf chlorophyll (Chl), defined as the combined mass of chlorophyll a and chlorophyll b per unit leaf area, generated with the REGularized canopy reFLECtance (REGFLEC) inversion system Houborg et al, 2015) for constraining nominal LUE inputs.…”

Section: A Schull Et Al: Modeling Carbon Water and Energy Fluxmentioning

confidence: 99%

“…Chlorophyll is a vital pigment in the photosynthetic apparatus, and advances in the retrieval of leaf and canopy chlorophyll from remote sensing data (Houborg et al, 2015) make it extremely amenable for the ultimate goal of mapping fluxes over larger areas. Houborg et al (2011) demonstrated the utility of using remotely sensed maps of leaf chlorophyll (Chl), defined as the combined mass of chlorophyll a and chlorophyll b per unit leaf area, generated with the REGularized canopy reFLECtance (REGFLEC) inversion system Houborg et al, 2015) for constraining nominal LUE inputs. REGFLEC-derived maps of β n generated over a rain-fed maize production system at the Beltsville Agricultural Research Center (BARC), MD, were used as input to a version of the thermal infrared (TIR) remotesensing-based two-source energy balance model (Anderson et al, 2008;Houborg et al, 2011), which employs an analytical LUE-based model of canopy resistance to compute coupled canopy transpiration and carbon assimilation fluxes (Anderson et al, 2000).…”

Section: A Schull Et Al: Modeling Carbon Water and Energy Fluxmentioning

confidence: 99%

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Thermal-based modeling of coupled carbon, water, and energy fluxes using nominal light use efficiencies constrained by leaf chlorophyll observations

et al. 2015

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Abstract. Recent studies have shown that estimates of leaf chlorophyll content (Chl), defined as the combined mass of chlorophyll a and chlorophyll b per unit leaf area, can be useful for constraining estimates of canopy light use efficiency (LUE). Canopy LUE describes the amount of carbon assimilated by a vegetative canopy for a given amount of absorbed photosynthetically active radiation (APAR) and is a key parameter for modeling land-surface carbon fluxes. A carbonenabled version of the remote-sensing-based two-source energy balance (TSEB) model simulates coupled canopy transpiration and carbon assimilation using an analytical submodel of canopy resistance constrained by inputs of nominal LUE (β n ), which is modulated within the model in response to varying conditions in light, humidity, ambient CO 2 concentration, and temperature. Soil moisture constraints on water and carbon exchange are conveyed to the TSEB-LUE indirectly through thermal infrared measurements of landsurface temperature. We investigate the capability of using Chl estimates for capturing seasonal trends in the canopy β n from in situ measurements of Chl acquired in irrigated and rain-fed fields of soybean and maize near Mead, Nebraska. The results show that field-measured Chl is nonlinearly related to β n , with variability primarily related to phenological changes during early growth and senescence. Utilizing seasonally varying β n inputs based on an empirical relationship with in situ measured Chl resulted in improvements in carbon flux estimates from the TSEB model, while adjusting the partitioning of total water loss between plant transpiration and soil evaporation. The observed Chl-β n relationship provides a functional mechanism for integrating remotely sensed Chl into the TSEB model, with the potential for improved mapping of coupled carbon, water, and energy fluxes across vegetated landscapes.

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Section: A Schull Et Al: Modeling Carbon Water and Energy Fluxmentioning

confidence: 99%

Section: A Schull Et Al: Modeling Carbon Water and Energy Fluxmentioning

confidence: 99%

Thermal-based modeling of coupled carbon, water, and energy fluxes using nominal light use efficiencies constrained by leaf chlorophyll observations

et al. 2015

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“…One of the main difficulties in physically-based approaches is the ill-posedness of the inversion. Several regularization methods can be implemented to reduce the drawback of ill-posedness: model coupling [26], using a priori information [27], spatial constraints [28,29], temporal constraints [30] and combined spatio-temporal constraints [31].…”

Section: Introductionmentioning

confidence: 99%

Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection

Yin

Liu

et al. 2015

Remote Sensing

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Physically-based approaches for estimating Leaf Area Index (LAI) using remote sensing data rely on radiative transfer (RT) models. Currently, many RT models are freely available, but determining the appropriate RT model for LAI retrieval is still problematic. This study aims to evaluate the necessity of RT model selection for LAI retrieval and to propose a retrieval methodology using different RT models for different vegetation types. Both actual experimental observations and RT model simulations were used to conduct the evaluation. Each of them includes needleleaf forests and croplands, which have contrasting structural attributes. The scattering from arbitrarily inclined leaves (SAIL) model and the four-scale model, which are 1D and 3D RT models, respectively, were used to simulate the synthetic test datasets. The experimental test dataset was established through two field campaigns conducted in the Heihe River Basin. The results show that the realistic representation of canopy structure in RT models is very important for LAI retrieval.If an unsuitable RT model is used, then the root mean squared error (RMSE) will increase from 0.43 to 0.60 in croplands and from 0.52 to 0.63 in forests. In addition, an RT model's potential to retrieve LAI is limited by the availability of a priori information on RT model OPEN ACCESSRemote Sens. 2015, 7 4605 parameters. 3D RT models require more a priori information, which makes them have poorer generalization capability than 1D models. Therefore, physically-based retrieval algorithms should embed more than one RT model to account for the availability of a priori information and variations in structural attributes among different vegetation types.

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“…In order to avoid step changes in LAI (particularly across SLC-off stripes) resulting 306 from the use of different MODIS-Landsat pairs, land cover specific adjustment factors calculated from 307 pixel-averaged bias deviations between date-coincident predictions from the optimal and alternative pair 308 dates, are applied to those pixels filled using the second and third pair date. Using time-series Landsat 309 LAI images as input, the land cover classification is generated automatically based on a scheme that 310 performs initial class separation (50 -100 classes) via the unsupervised ISODATA technique, followed 311 by class merging based on phenological similarity (Houborg et al, 2015b). The scheme ensures that 312 ISODATA classes with similar phenology, as determined by the coefficient of efficiency (Section 3.3) 313 computed between class-averaged LAI time-series, are grouped together.…”

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confidence: 99%

“…However, since consistency with the 1 km estimates is 586 ensured, only the relative sub-pixel variability from the 1 km LAI value is affected. Presumably, the 587 definition of model-diagnosed LAI-NDVI relationships with a better account of spatio-temporal 588 variations in soil background, leaf and canopy biochemical and structural characteristics, atmospheric 589 conditions, and view and illumination geometry (Houborg et al, 2015b), would improve 1 km to 250 m 590 downscaling accuracies further. Alternatively, rule-based multivariate linear regressions for LAI 250 591 estimation may be constructed based on MODIS LAI 1km and red and near-infrared band reflectances at 592 250 m resolution, using the same regression tree approach adopted for Landsat data, which is viable 593…”

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confidence: 99%

A Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI)

Houborg

Gao

2016

International Journal of Applied Earth Observation and Geoinfor

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Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC)

Cited by 124 publications

References 124 publications

Thermal-based modeling of coupled carbon, water, and energy fluxes using nominal light use efficiencies constrained by leaf chlorophyll observations

Thermal-based modeling of coupled carbon, water, and energy fluxes using nominal light use efficiencies constrained by leaf chlorophyll observations

Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection

A Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI)

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