Evaluation of the MODIS LAI algorithm at a coniferous forest site in Finland

Wang, Yujie; Woodcock, Curtis E.; Buermann, Wolfgang; Stenberg, Pauline; Voipio, Pekka; Smolander, Heikki; Häme, Tuomas; Tian, Yuhong; Hu, Jiannan; Knyazikhin, Yuri; Myneni, Ranga B.

doi:10.1016/j.rse.2004.02.007

Cited by 212 publications

(144 citation statements)

References 43 publications

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“…Although the reflectance data can be used in high-resolution LAI mapping, vegetation indexes are more often used in validation activities. Commonly used vegetation indexes include normalized difference vegetation index (NDVI) [36], simple ratio (SR) [37], and reduced simple ratio (RSR) [38]. In mountainous areas, slope extracted from digital elevation model (DEM) data is often used to account for the terrain effects [31,39].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

A Cost-Constrained Sampling Strategy in Support of LAI Product Validation in Mountainous Areas

Yin

Zeng

et al. 2016

Remote Sensing

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Abstract:Increasing attention is being paid on leaf area index (LAI) retrieval in mountainous areas. Mountainous areas present extreme topographic variability, and are characterized by more spatial heterogeneity and inaccessibility compared with flat terrain. It is difficult to collect representative ground-truth measurements, and the validation of LAI in mountainous areas is still problematic. A cost-constrained sampling strategy (CSS) in support of LAI validation was presented in this study. To account for the influence of rugged terrain on implementation cost, a cost-objective function was incorporated to traditional conditioned Latin hypercube (CLH) sampling strategy. A case study in Hailuogou, Sichuan province, China was used to assess the efficiency of CSS. Normalized difference vegetation index (NDVI), land cover type, and slope were selected as auxiliary variables to present the variability of LAI in the study area. Results show that CSS can satisfactorily capture the variability across the site extent, while minimizing field efforts. One appealing feature of CSS is that the compromise between representativeness and implementation cost can be regulated according to actual surface heterogeneity and budget constraints, and this makes CSS flexible. Although the proposed method was only validated for the auxiliary variables rather than the LAI measurements, it serves as a starting point for establishing the locations of field plots and facilitates the preparation of field campaigns in mountainous areas.

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Section: Theoretical Backgroundmentioning

confidence: 99%

A Cost-Constrained Sampling Strategy in Support of LAI Product Validation in Mountainous Areas

Yin

Zeng

et al. 2016

Remote Sensing

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“…Fig. 9 shows temporal variation in the RI for 2°by 2°areas centered on the five sites described in Table 1 and a needle leaf forest site near Ruokolahti, Finland (61.32N/28.43E, Wang et al, 2004). The low number of algorithm retrievals during the winter time is due to snow conditions and/or the absence of green leaves.…”

Section: Lai and Fpar Retrievalsmentioning

confidence: 99%

“…Also, satellite derived products themselves have a certain accuracy and precision, for example, due to incomplete atmospheric correction. Therefore, the LAI value assigned to a single pixel may be unreliable, but the mean LAI of multiple pixels may be valid (Wang et al, 2004). Thus, it is preferable to perform comparisons at multipixel (patch) scale, where the LAI product is statistically stable.…”

Section: Validation Of Misr Lai Product At Other Sitesmentioning

confidence: 99%

Analysis of the MISR LAI/FPAR product for spatial and temporal coverage, accuracy and consistency

Tan

et al. 2007

Remote Sensing of Environment

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The Multi-angle Imaging SpectroRadiometer (MISR) instrument provides global imagery at nine discrete viewing angles and four visible/nearinfrared spectral bands. MISR standard products include green leaf area index (LAI) of vegetation and fraction of photosynthetically active radiation absorbed by vegetation (FPAR). This paper describes the research basis for transitioning the MISR LAI/FPAR products from provisional to validation status. The efforts included not only comparisons to field data but also analyses of relationships, consistency and complementarity between various MISR products derived by independent algorithms. For example, we show how the energy absorbed by the ground below vegetation can be estimated from two independent MISR products, FPAR and BHRPAR (bi-hemispheric reflectance at PAR wavelengths). Further, we show that this information can be used to derive at least three measures of canopy structure -Beer's law extinction coefficient, mean leaf inclination and the gap fraction or vegetation ground cover. The spatial and temporal coverage of the LAI/FPAR product is mainly limited by cloud contamination. However, when a successful aerosol retrieval is performed, typically 95% of pixels have surface reflectance retrievals suitable as input to the LAI/FPAR algorithm. The algorithm provides LAI/FPAR retrievals in 50-80% of these pixels with suitable input. The early versions of the algorithm overestimated LAI values in grasses and broadleaf crops. The MISR LAI product from the recalibrated algorithm (version 3.3) is assessed by comparison with field data collected in a 3 × 3 km agricultural area (grasses and cereal crops) near Avignon, France. LAI retrievals in other biomes are compared to MODIS LAI product of known accuracy. The MISR LAI product shows structural and phenological variability in agreement with data. Our results suggest that the product is accurate to within 0.66 LAI in herbaceous vegetation and savannas and is an overestimate by about 1 LAI in broadleaf forests. LAI retrievals over needle leaf forests remain at provisional quality level.

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“…Previous studies have shown the importance of LAI in ecological and remote sensing studies. LAI is a key input for climate and large-scale ecosystem models and also is a key structural characteristic of forest ecosystems (Chen et al 1997;Myneni et al 1997;Wang et al 2004;Zheng and Moskal 2009). In the last decades, LAI has been successfully retrieved using hyperspectral data in the visible/near-infrared (0.35-1.0 µm, VNIR) and short-wave infrared (1.0-2.5 µm, SWIR) regions (Zheng and Moskal 2009).…”

Section: Introductionmentioning

confidence: 99%

Leaf Area Index Retrieved From Thermal Hyperspectral Data

Neinavaz¹,

Skidmore²,

Darvishzadeh³

et al. 2016

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

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ABSTRACT:Leaf area index (LAI) is an important essential biodiversity variable due to its role in many terrestrial ecosystem processes such as evapotranspiration, energy balance, and gas exchanges as well as plant growth potential. A novel approach presented here is the retrieval of LAI using thermal infrared (8-14 μm, TIR) measurements. Here, we evaluate LAI retrieval using TIR hyperspectral data. Canopy emissivity spectral measurements were recorded under controlled laboratory conditions using a MIDAC (M4401-F) illuminator Fourier Transform Infrared spectrometer for two plant species during which LAI was destructively measured. The accuracy of retrieval for LAI was then assessed using partial least square regression (PLSR) and narrow band index calculated in the form of normalized difference index from all possible combinations of wavebands. The obtained accuracy from the PLSR for LAI retrieval was relatively higher than narrow-band vegetation index (0.54

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Evaluation of the MODIS LAI algorithm at a coniferous forest site in Finland

Cited by 212 publications

References 43 publications

A Cost-Constrained Sampling Strategy in Support of LAI Product Validation in Mountainous Areas

A Cost-Constrained Sampling Strategy in Support of LAI Product Validation in Mountainous Areas

Analysis of the MISR LAI/FPAR product for spatial and temporal coverage, accuracy and consistency

Leaf Area Index Retrieved From Thermal Hyperspectral Data

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