Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data

Verrelst, Jochem; Schaepman, Michael E.; Koetz, Benjamin; Kneubühler, Mathias

doi:10.1016/j.rse.2007.11.001

Cited by 280 publications

(128 citation statements)

References 79 publications

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“…Pinter et al (1990) and Huete et al (1992) determined that the amount of soil viewed through a vegetation canopy contributes to the surface anisotropy. In this case, deriving separate normalizations for image subsets based on fraction of soil and green vegetation (Montandon and Small 2008) (Goodin et al 2004;Huete et al 1992;Veraverbeke et al 2010;Verrelst et al 2008). …”

Section: Importance Of Vegetation Type Specific Normalizationmentioning

confidence: 99%

C-correction of optical satellite data over alpine vegetation areas: A comparison of sampling strategies for determining the empirical c-parameter

Reese

Olsson

2011

Remote Sensing of Environment

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Semi-empirical topographic normalization methods (e.g., C-correction) have been widely used to correct illumination differences in optical satellite data. The objective of this study was to examine the precision and accuracy of the C-correction's empirical parameter, c, as a function of the sample from which it was derived. Three sampling methods were compared: a random sample, a sample stratified on north and south aspects, and a sample stratified by cosine of the solar incidence angle, i. In the latter, power allocation was used to determine the quantity of observations for each stratum. Four overlapping satellite images were used (two Landsat 5 TM and two SPOT 5 HRG) with different acquisition dates and large solar zenith angles over an alpine region in Sweden. The sample stratified by cosine of i produced c with the highest precision from repeated trials and had coefficients of determination (R 2 ) twice as high as those from the other sampling methods. Use of power allocation in the cosine of i stratified sample enabled better representation of spectral variability; this was particularly important for the NIR band where the outcome of c differed according to sampling method. Evaluations using t-tests and classification accuracy showed that c derived from the cosine of i stratified sample correctly normalized a larger percentage of the evaluation data. The distribution of cosine of i in the study area, the spectral variability and vegetation types exert influences to consider when sampling to derive c. Although sampling was restricted to alpine vegetation only, some

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Section: Importance Of Vegetation Type Specific Normalizationmentioning

confidence: 99%

C-correction of optical satellite data over alpine vegetation areas: A comparison of sampling strategies for determining the empirical c-parameter

Reese

Olsson

2011

Remote Sensing of Environment

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“…Also the spectral characteristics of understory, forest floor and woody 280 parts were measured from several selected samples with an ASD field spectroradiometer during the 281 above campaign. Field spectra of understory vegetation, bark of branches and bark of trunks (middle 282 part) were converted to absolute reflectance and aggregated (35 vegetated understory spectra, 15 bark 283 spectra) to cover the spectral properties of NPV and background components (figure 1) necessary for 284 the radiative transfer parameterization (Kötz et al, 2004;Verrelst et al, 2008). In a complex 285 ecosystem such as an old-growth forest a background layer is unlikely to be defined by one single 286 ground stratum; shrubs, herbaceous species, CWD, litter, etc.…”

Section: Collection Of Spectral Data Set 272mentioning

confidence: 99%

Effects of woody elements on simulated canopy reflectance: Implications for forest chlorophyll content retrieval

Verrelst

Schaepman

Malenovský

et al. 2010

Remote Sensing of Environment

102

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Effects of woody elements on simulated canopy reflectance:implications for forest chlorophyll content retrieval AbstractAn important bio-indicator of actual plant health status, the foliar content of chlorophyll a and b (Cab), can be estimated using imaging spectroscopy. For forest canopies, however, the relationship between the spectral response and leaf chemistry is confounded by factors such as background (e.g. understory), canopy structure, and the presence of non-photosynthetic vegetation (NPV, e.g. woody elements)--particularly the appreciable amounts of standing and fallen dead wood found in older forests.We present a sensitivity analysis for the estimation of chlorophyll content in woody coniferous canopies using radiative transfer modeling, and use the modeled top-of-canopy reflectance data to analyze the contribution of woody elements, leaf area index (LAI), and crown cover (CC) to the retrieval of foliar Cab content. The radiative transfer model used comprises two linked submodels: one at leaf level (PROSPECT) and one at canopy level (FLIGHT). This generated bidirectional reflectance data according to the band settings of the Compact High Resolution Imaging Spectrometer (CHRIS) from which chlorophyll indices were calculated. Most of the chlorophyll indices outperformed single wavelengths in predicting Cab content at canopy level, with best results obtained by the Maccioni indexWe demonstrate the performance of this index with respect to structural information on three distinct coniferous forest types (young, early mature and old-growth stands). The modeling results suggest that the spectral variation due to variation in canopy chlorophyll content is best captured for stands with medium dense canopies. However, the strength of the up-scaled Cab signal weakens with increasing crown NPV scattering elements, especially when crown cover exceeds 30%. LAI exerts the least perturbations. We conclude that the spectral influence of woody elements is an important variable that should be considered in radiative transfer approaches when retrieving foliar pigment estimates in heterogeneous stands, particularly if the stands are partly defoliated or long-lived. Abstract 11A sensitivity analysis for the detectabilility of chlorophyll content in woody coniferous canopies 12 using radiative transfer modeling is presented. Foliar content of the photosynthetic pigments 13 chlorophyll a and b (Cab) is widely regarded as a bioindicator of the actual plant health status and can 14 be quantified using imaging spectroscopy. At stand level, however, the relationship between spectral 15 response and leaf chemistry tends to be weakened due to confounding factors such as canopy 16 structure, presence of non-photosynthetic vegetation (NPV, e.g. woody elements) and forest 17 background (e.g. understory). Especially in old-growth forests significant fractions of standing and 18 fallen dead woody material are present. We analyzed the contribution of crown wood cover, crown 19 LAI and crown coverage on the retrieval of Cab content on...

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“…CHRIS Mode 3 (Land data), collecting images with 18 channels in the visible and near-infrared regions, from 447 nm to 1035 nm, band width ranges from 10 nm to 16 nm in the visible domain, and from 6 nm to 44 nm in the near-infrared domain, has been widely used in the study of terrestrial vegetation (Table 2). This new hyper spectral sensor provides a new opportunity to effectively improve the retrieval accuracy of LAI [18][19][20] .…”

Section: Data From In Situ Measurementsmentioning

confidence: 99%

Estimation of leaf area index using an angular vegetation index based on in situ measurements and CHRIS/PROBA data

Wang¹,

Zhang²,

Lin³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The Normalized Difference Vegetation Index (NDVI) is widely used for Leaf Area Index (LAI) estimation. It is well documented that the NDVI is extremely subject to the saturation problem when LAI reaches a high value. A new multi-angular vegetation index, the Hotspot-darkspot Difference Vegetation Index (HDVI) is proposed to estimate the high density LAI. The HDVI, defined as the difference between the hot and dark spot NDVI, relative to the dark spot NDVI, was proposed based on the Analytical two-layer Canopy Reflectance Model (ACRM) model outputs. This index is validated using both in situ experimental data in wheat and data from the multi-angular optical Compact High-Resolution Imaging Spectrometer (CHRIS) satellite. Both indices, the Hotspot-Darkspot Index (HDS) and the NDVI were also selected to analyze the relationship with LAI, and were compared with new index HDVI. The results show that HDVI is an appropriate proxy of LAI with higher determination coefficients (R2) for both the data from the in situ experiment (R2=0.7342, RMSE=0.0205) and the CHRIS data (R2=0.7749, RMSE=0.1013). Our results demonstrate that HDVI can make better the occurrence of saturation limits with the information of multi-angular observation, and is more appropriate for estimating LAI than either HDS or NDVI at high LAI values. Although the new index needs further evaluation, it also has the potential under the condition of dense canopies. It provides the effective improvement to the NDVI and other vegetation indices that are based on the red and NIR spectral bands.

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Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data

Cited by 280 publications

References 79 publications

C-correction of optical satellite data over alpine vegetation areas: A comparison of sampling strategies for determining the empirical c-parameter

C-correction of optical satellite data over alpine vegetation areas: A comparison of sampling strategies for determining the empirical c-parameter

Effects of woody elements on simulated canopy reflectance: Implications for forest chlorophyll content retrieval

Estimation of leaf area index using an angular vegetation index based on in situ measurements and CHRIS/PROBA data

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