Evaluation of MODIS LAI/FPAR Product Collection 6. Part 2: Validation and Intercomparison

Yan, Kai; Park, Taejin; Yan, Guangjian; Liu, Zhao; Yang, Bin; Chen, Chi; Nemani, Ramakrishna R.; Knyazikhin, Yuri; Myneni, Ranga B.

doi:10.3390/rs8060460

Cited by 249 publications

(201 citation statements)

References 37 publications

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“…For example, Xu et al [64] assessed MODIS FAPAR through comparisons to ground measurements available from 2012-2016, obtaining a reasonable agreement (R 2 = 0.83, RMSE = 0.10) but with an overall overestimation tendency (bias = 0.08, scatters distributed within 0-0.2 difference). Similar results (R 2 = 0.74, RMSE = 0.15) were reported by Yan et al [59] using globally distributed FAPAR measurements. The study evidenced a clear overestimation of FAPAR over sparsely-vegetated areas, as noted previously in other studies [60].…”

Section: Discussionsupporting

confidence: 78%

“…Similar underestimation behaviour was found by other studies when comparing MODIS LAI products and LAI retrievals from RTM inversion [59,63]. Yan et al [59] found RMSE = 0.66 m 2 /m 2 and RMSE = 0.77 m 2 /m 2 when comparing MODIS C6 LAI estimates with ground LAI actual and LAI e f f measurements respectively, as well as larger uncertainties in high LAI values. Regarding FAPAR, an overall negative bias is found for all biomes.…”

Section: Discussionsupporting

confidence: 67%

“…Regarding FAPAR, an overall negative bias is found for all biomes. However, this bias could not be regarded as an issue in the estimations, since different studies have pointed out a systematic overestimation of MODIS retrievals in both C5 and C6 at low FAPAR values as a main drawback of the product [59][60][61]. For example, Xu et al [64] assessed MODIS FAPAR through comparisons to ground measurements available from 2012-2016, obtaining a reasonable agreement (R 2 = 0.83, RMSE = 0.10) but with an overall overestimation tendency (bias = 0.08, scatters distributed within 0-0.2 difference).…”

Section: Discussionmentioning

confidence: 99%

“…In the case of FAPAR, there is a constant negative bias of ≈0.05 which is also noticeable in the scatter plots shown in Figure 8. This is related with a documented systematic overestimation of MODIS FAPAR retrievals [59][60][61], which is partly corrected by the proposed retrieval approach. The spatial consistency of LAI/FAPAR estimates was also compared over the African continent (Figure 10).…”

Section: Validationmentioning

confidence: 99%

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Global Estimation of Biophysical Variables from Google Earth Engine Platform

et al. 2018

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This paper proposes a processing chain for the derivation of global Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction Vegetation Cover (FVC), and Canopy water content (CWC) maps from 15-years of MODIS data exploiting the capabilities of the Google Earth Engine (GEE) cloud platform. The retrieval chain is based on a hybrid method inverting the PROSAIL radiative transfer model (RTM) with Random forests (RF) regression. A major feature of this work is the implementation of a retrieval chain exploiting the GEE capabilities using global and climate data records (CDR) of both MODIS surface reflectance and LAI/FAPAR datasets allowing the global estimation of biophysical variables at unprecedented timeliness. We combine a massive global compilation of leaf trait measurements (TRY), which is the baseline for more realistic leaf parametrization for the considered RTM, with large amounts of remote sensing data ingested by GEE. Moreover, the proposed retrieval chain includes the estimation of both FVC and CWC, which are not operationally produced for the MODIS sensor. The derived global estimates are validated over the BELMANIP2.1 sites network by means of an inter-comparison with the MODIS LAI/FAPAR product available in GEE. Overall, the retrieval chain exhibits great consistency with the reference MODIS product (R 2 = 0.87, RMSE = 0.54 m 2 /m 2 and ME = 0.03 m 2 /m 2 in the case of LAI, and R 2 = 0.92, RMSE = 0.09 and ME = 0.05 in the case of FAPAR). The analysis of the results by land cover type shows the lowest correlations between our retrievals and the MODIS reference estimates (R 2 = 0.42 and R 2 = 0.41 for LAI and FAPAR, respectively) for evergreen broadleaf forests. These discrepancies could be attributed mainly to different product definitions according to the literature. The provided results proof that GEE is a suitable high performance processing tool for global biophysical variable retrieval for a wide range of applications.

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Section: Discussionsupporting

confidence: 78%

Section: Discussionsupporting

confidence: 67%

Section: Discussionmentioning

confidence: 99%

Section: Validationmentioning

confidence: 99%

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Global Estimation of Biophysical Variables from Google Earth Engine Platform

et al. 2018

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“…Two LAI data sets were used to calculate the growing season average LAI, namely the Boston University reprocessed MODIS Collection 6 LAI (hereafter LAI modis ) and the latest GIMMS-3g LAI. LAI quantifies the total area of green elements of the canopy per unit horizontal ground area 38 . The applied data sets are thus proxies for the total green LAI of all canopy layers, and can be used as a proxy for the green vegetation cover.…”

Section: Methodsmentioning

confidence: 99%

Increased vegetation growth and carbon stock in China karst via ecological engineering

et al. 2018

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T he impacts of climate change and anthropogenic activities on Earth's vegetation and ecosystems have been in the spotlight of science in the past decades [1][2][3][4][5][6] . With increasing climate variability and more frequent occurrences of extreme events expected in the future 7 , research has targeted the sensitivity of ecosystems 8,9 . At the same time, recent studies have shown globally increasing leaf area index (LAI; a proxy for green vegetation cover) 10 , and aboveground biomass carbon (ABC) 11 , also known as the greening Earth 10,12 . Dynamic vegetation models and Earth observation studies reveal climatic and atmospheric changes as the main drivers of large scale increases in LAI 10,13 . On the contrary, the anthropogenic footprint is usually found to cause land degradation and deforestation 13,14 , and only a few studies find a direct positive effect of management on vegetation cover and biomass trends 2,11,15 . Although ecological conservation projects aim at increasing biodiversity, carbon sequestration and vegetation cover 16,17 , the success of such conservation efforts is not easily quantifiable, and the spatial footprint of projects is not always commensurable with contemporary satellite-and modelling-based monitoring methods. Adaptation and mitigation strategies to climate change should be anchored in knowledge on how ecosystems respond to climatic and anthropogenic disturbances, but at present it is not known whether conservation projects impact on the ability of vegetation to alleviate the effects of climate change at large scales.China's ecological restoration projects (for example, the Natural Forest Protection Project, the Grain to Green Project, and the Karst Rocky Desertification Restoration Project) are considered 'megaengineering' activities and the most ambitious afforestation and conservation projects in human history [16][17][18][19] . The highly sensitive and vulnerable karst ecosystem in southwest China is one of the largest exposed carbonate rock areas (more than 0.54 million km 2 ) in the world. This area hosts 220 million people 20,21 and has been selected as a major target of restoration projects. Descriptions as early as the seventeenth century reported the rocky karst mountains as an area of sparse forest or vegetation cover 22 , and accelerating desertification has been reported during the past half century, caused by the increasing intensity of human exploitation of natural resources [21][22][23][24] . As a result, approximately 0.13 million km 2 of karst areas previously covered by vegetation and soil were turned into a rocky landscape. To combat this severe form of land degradation and to relieve poverty, more than 130 billion yuan (~19 billion USD) have been invested in mitigation initiatives since the end of the 1990s 24 . The largest programme implemented, the Grain to Green Project, offers grain, cash and free seedlings as compensation for rural households to re-establish forests, shrub and/or grassland 24 . The costs of ecological engineering projects as a clima...

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Estimation of Woody and Herbaceous Leaf Area Index in Sub‐Saharan Africa Using MODIS Data

Kahiu

Hanan

2018

JGR Biogeosciences

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Savannas are widespread global biomes covering ~20% of terrestrial ecosystems on all continents except Antarctica. These ecosystems play a critical role in regulating terrestrial carbon cycle, ecosystem productivity, and the hydrological cycle and contribute to human livelihoods and biodiversity conservation. Despite the importance of savannas in ecosystem processes and human well‐being, the presence of mixed woody and herbaceous components at scales much finer than most medium‐ and coarse‐resolution satellite imagery poses significant challenges to their effective representation in remote sensing and modeling of vegetation dynamics. Although previous studies have attempted to separate woody and herbaceous components, the focus on greenness indices and fractional cover provides little insight into spatiotemporal variability in woody and herbaceous vegetation structure, in particular, leaf area index (LAI). This paper presents a method to partition 1 km spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) aggregate green leaf area index (LAIA) from 2003 to 2015, into separate woody (LAIW) and herbaceous (LAIH) constituents in both drought seasonal savannas and moist tropical forests of Sub‐Saharan Africa (SSA). In our analysis, we use an allometric relationship describing the variation in peak within‐canopy woody LAI of dominant tree species (LAIWpinc) across gradients in mean annual precipitation, coupled with independent estimates of woody canopy cover (τw), to constrain seasonally changing LAIW. We present the LAI partitioning approach and highlight the broad spatial and temporal patterns of woody and herbaceous LAI across SSA. The long‐term average 8 day phenologies of woody and herbaceous LAI (averaged across 2003–2015) are available for evaluation, research, and application purposes.

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Evaluation of MODIS LAI/FPAR Product Collection 6. Part 2: Validation and Intercomparison

Cited by 249 publications

References 37 publications

Global Estimation of Biophysical Variables from Google Earth Engine Platform

Global Estimation of Biophysical Variables from Google Earth Engine Platform

Increased vegetation growth and carbon stock in China karst via ecological engineering

Estimation of Woody and Herbaceous Leaf Area Index in Sub‐Saharan Africa Using MODIS Data

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