Multiscale analysis and validation of the MODIS LAI productI. Uncertainty assessment

Tian, Yuhong; Woodcock, Curtis E.; Wang, Yujie; Privette, Jeffrey L.; Shabanov, Nikolay V.; Zhou, Liming; Zhang, Yu; Buermann, Wolfgang; Dong, Jing; Veikkanen, Brita; Häme, Tuomas; Andersson, Kristin; Özdoğan, Mutlu; Knyazikhin, Yuri; Myneni, Ranga B.

doi:10.1016/s0034-4257(02)00047-0

Cited by 176 publications

(118 citation statements)

References 28 publications

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“…. on between these two biomes does not cause large errors (<0.30), which is consistent with deterministic findings from earlier LAI collections [21,30,31].…”

Section: Misclassification Induced Lai Errors (Mies)supporting

confidence: 90%

“…Confidence in our results will be enhanced when data for the proportion of the secondary biome becomes available. Information about the fractional vegetation cover (FVC) can be explored to study the impact on LAI retrievals [31]. The effects of FVC will be quantified when the full MODIS vegetation continuous field (VCF) products (MOD44B), including the percentage of trees, grasses and bare ground, are refined (MODIS Land.…”

Section: Future Prospectsmentioning

confidence: 99%

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The Impact of Potential Land Cover Misclassification on MODIS Leaf Area Index (LAI) Estimation: A Statistical Perspective

Fang

Myneni

2013

Remote Sensing

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Understanding the impact of vegetation mixture and misclassification on leaf area index (LAI) estimation is crucial for algorithm development and the application community. Using the MODIS standard land cover and LAI products, global LAI climatologies and statistics were obtained for both pure and mixed pixels to evaluate the effects of biome mixture on LAI estimation. Misclassification between crops and shrubs does not generally translate into large LAI errors (<0.37 or 27.0%), partly due to their relatively lower LAI values. Biome misclassification generally leads to an LAI overestimation for savanna, but an underestimation for forests. The largest errors caused by misclassification are also found for savanna (0.51), followed by evergreen needleleaf forests (0.44) and broadleaf forests (~0.31).Comparison with MODIS uncertainty indicators show that biome misclassification is a major factor contributing to LAI uncertainties for savanna, while for forests, the main uncertainties may be introduced by algorithm deficits, especially in summer. The LAI climatologies for pure pixels are recommended for land surface modeling studies. Future studies should focus on improving the biome classification for savanna systems and refinement of the retrieval algorithms for forest biomes.

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“…. on between these two biomes does not cause large errors (<0.30), which is consistent with deterministic findings from earlier LAI collections [21,30,31].…”

Section: Misclassification Induced Lai Errors (Mies)supporting

confidence: 90%

Section: Future Prospectsmentioning

confidence: 99%

The Impact of Potential Land Cover Misclassification on MODIS Leaf Area Index (LAI) Estimation: A Statistical Perspective

Fang

Myneni

2013

Remote Sensing

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“…This is particularly complex to achieve at the scale of medium and coarse resolution satellite sensor data due to land cover heterogeneity and poor spatial correspondence between ESU and image pixel as a result of geolocation errors [23][24][25]. Direct comparisons between estimates of a variable in the field and products derived from a satellite sensor data are feasible for large homogenous areas [26][27][28]. However, where the land surface landscape is heterogeneous, alternative validation approaches are required.…”

Section: Validation Of Eo-based Medium Spatial Resolution CCC Productsmentioning

confidence: 99%

“…However, where the land surface landscape is heterogeneous, alternative validation approaches are required. Several regional and global validation studies [26,29] have used fine spatial resolution remotely sensed data (10-30 m) to map biophysical variables as an intermediate step in scale between field biophysical estimates and medium or coarser spatial resolution satellite sensor imagery. For instance, validation of MERIS biophysical variables by the VALERI group (http://www.avignon.inra.fr/valeri/) involved first, establishing statistical 'transfer functions' between fine spatial resolution (e.g., 20 m) satellite sensor images and field biophysical estimates.…”

Section: Validation Of Eo-based Medium Spatial Resolution CCC Productsmentioning

confidence: 99%

“…The first are based on empirical equations, such as regressions between vegetation indices and field data. The second are based on physical models (i.e., inverse canopy reflectance modeling, [26]). Empirical methods are site-and sensor-specific that require accurate field data [65] and most employ vegetation indices using data in only two wavebands, usually red and near infrared [23,66].…”

Section: Deriving Lai and CCC Maps From Fine Spatial Resolution Datamentioning

confidence: 99%

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Methodologies and Uncertainties in the Use of the Terrestrial Chlorophyll Index for the Sentinel-3 Mission

et al. 2012

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A methodology is described for the validation of Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) data over heterogeneous land surfaces in an agricultural region in Southern Italy. The approach involves the use inverse canopy reflectance modeling techniques to derive maps of canopy chlorophyll content (CCC) and leaf area index (LAI) at fine spatial resolution. Indirect field measurements are used for validation of the fine spatial resolution data. Subsequently, these maps are aggregated based on a regular grid at 1 km spatial resolution to validate MERIS Level 2 MTCI (300 m). RapidEye satellite sensor data with a pixel size of 6.5 m are used for this purpose. Based on a set of independent ground measurements, fine spatial resolution maps achieved an R 2 = 0.78 and RMSE = 0.39 for CCC and R 2 = 0.76 and RMSE = 0.64 for LAI. The relationship between MERIS L2 MTCI and CCC [g·machieved a coefficient of determination of 0.74 and it resulted to be extremely statistically significant (p-value < 0.001). Additionally, a relative validation of two other satellite products at medium resolution spatial scale, namely MERIS leaf area index (LAI) and

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The role of local‐scale heterogeneities in terrestrial ecosystem modeling

et al. 2015

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The coarse‐grained spatial representation of many terrestrial ecosystem models hampers the importance of local‐scale heterogeneities. To address this issue, we combine a range of observations (forest inventories, eddy flux tower data, and remote sensing products) and modeling approaches with contrasting degrees of abstraction. The following models are selected: (i) Lund‐Potsdam‐Jena (LPJ), a well‐established, area‐based, dynamic global vegetation model (DGVM); (ii) LPJ‐General Ecosystem Simulator, a hybrid, individual‐based approach that additionally considers plant population dynamics in greater detail; and (iii) distributed in space‐LPJ, a spatially explicit version of LPJ, operating at a fine spatial resolution (100 m × 100 m), which uses an enhanced hydrological representation accounting for lateral connectivity of surface and subsurface water fluxes. By comparing model simulations with a multivariate data set available at the catchment scale, we argue that (i) local environmental and topographic attributes that are often ignored or crudely represented in DGVM applications exert a strong control on terrestrial ecosystem response; (ii) the assumption of steady state vegetation and soil carbon pools at the beginning of simulation studies (e.g., under “current conditions”), as embedded in many DGVM applications, is in contradiction with the current state of many forests that are often out of equilibrium; and (iii) model evaluation against vegetation carbon fluxes does not imply an accurate simulation of vegetation carbon stocks. Having gained insights about the magnitude of aggregation‐induced biases due to smoothing of spatial variability at the catchment scale, we discuss the implications of our findings with respect to the global‐scale modeling studies of carbon cycle and we illustrate alternative ways forward.

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Multiscale analysis and validation of the MODIS LAI productI. Uncertainty assessment

Cited by 176 publications

References 28 publications

The Impact of Potential Land Cover Misclassification on MODIS Leaf Area Index (LAI) Estimation: A Statistical Perspective

The Impact of Potential Land Cover Misclassification on MODIS Leaf Area Index (LAI) Estimation: A Statistical Perspective

Methodologies and Uncertainties in the Use of the Terrestrial Chlorophyll Index for the Sentinel-3 Mission

The role of local‐scale heterogeneities in terrestrial ecosystem modeling

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