Comparison and evaluation of Medium Resolution Imaging Spectrometer leaf area index products across a range of land use

Canisius, Francis; Fernandes, Richard; Chen, Jing

doi:10.1016/j.rse.2009.12.010

Cited by 35 publications

(19 citation statements)

References 42 publications

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“…Thirty-three MERIS Reduced Resolution (RR) Level 2 (1200 m) images were used in this study, spanning the growing season from 28th March to 30th November, 2004. The L2 products contain geolocated geophysical parameters in addition to surface reflectance, including terrain height, geometric information, solar and viewing geometry, meteorological data and several flags addressing image quality (Canisius et al, 2010). MERIS L2 products were radiometrically and atmospherically corrected to account for Rayleigh scattering, ozone, water vapor absorption and aerosol content.…”

Section: Satellite Data Acquisition and Processingmentioning

confidence: 99%

Temporal disparity in leaf chlorophyll content and leaf area index across a growing season in a temperate deciduous forest

Croft

Chen

Zhang

2014

International Journal of Applied Earth Observation and Geoinfor

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Section: Satellite Data Acquisition and Processingmentioning

confidence: 99%

Temporal disparity in leaf chlorophyll content and leaf area index across a growing season in a temperate deciduous forest

Croft

Chen

Zhang

2014

International Journal of Applied Earth Observation and Geoinfor

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“…Neural network is also developed to generate canopy biophysical products from top-of-canopy reflectance measurements [4], [5]. Although these techniques have been widely used to estimate LAI at large scales, studies also show that the accuracy of these models may not meet specific application requirements in certain cases [6].…”

Section: Introductionmentioning

confidence: 99%

Leaf Area Index Estimation Using Vegetation Indices Derived From Airborne Hyperspectral Images in Winter Wheat

Xie

Huang

Liang

et al. 2014

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Continuous monitoring leaf area index (LAI) of field crops in a growing season has a great challenge. The development of remote sensing technology provides a good tool for timely mapping LAI regionally. In this study, hyperspectral reflectance data (405-835 nm) obtained from an airborne hyperspectral imager (Pushbroom Hyperspectral Imager) were used to model LAI of winter wheat canopy in the 2002 crop growing season. LAI was modeled based on its semi-empirical relationships with six vegetation indices (VIs), including ratio vegetation index (RVI), modified simple ratio index (MSR), normalized difference vegetation index (NDVI), a newly proposed index NDVI-like (which resembles NDVI), modified triangular vegetation index (MTVI2), and modified soil adjusted vegetation index (MSAVI).To assess the performance of these VIs, root mean square errors (RMSEs) and determination coefficient (R 2 ) between estimated LAI and measured LAI were reported. Our result showed that NDVI-like was the most accurate predictor of LAI. The inclusion of a green band in MTVI2 trended to give a rise to a much quicker saturation with increase of LAI (e.g., over 3.5). MSAVI and MTVI2 showed comparable but lower potential than NDVIlike in estimating LAI. RVI and MSR demonstrated their lowest prediction accuracy, implying that they are more likely to be affected by environmental conditions such as atmosphere and cloud, thus cannot properly reflect the properties of winter wheat canopy. Our results support the use of VIs for a quick assessment of seasonal variations in winter wheat LAI. Among the indices Manuscript we tested in this study, the newly developed NDVI-like model created the most accurate and reliable results.Index Terms-Hyperspectral remote sensing, leaf area index (LAI), vegetation index (VI), winter wheat.

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“…Fuchs et al (2009) used data from the ASTER sensor (15-m spatial resolution) to estimate tree carbon stocks in the Siberian tundra and found that the best predictor variables included spectral bands in the shortwave infrared portion with correlation values ranging from 0.52 to 0.57. Canisius et al (2010) also recognized that avoiding shortwave infrared bands in the analysis could lead to a decrease in desired correlations. Cruz-Leyva et al 2010modeled forest density variables such as basal area and wood volume in a forest of Pinus patula and P. teocote in Hidalgo, Mexico using both spectral variables from SPOT5 HRG and auxiliary information (climatic and topographic data).…”

Section: Discussionmentioning

confidence: 99%

“…(1) This spectral index is sensitive to moisture stress of vegetation and inversely correlated with vegetation biomass. We put emphasis on using this vegetation index because there was some evidence that it worked better when modeling forest biomass with near and middle-shortwave infrared spectral bands (Rock et al, 1986;Gjertsen, 2007;Fuchs et al, 2009;Canisius et al, 2010). A vegetation index that is equivalent to NDVI 62 is the normalized difference water index (NDWI), which is constructed similarly to NDVI 62 but with the reverse order for numerator elements (ρ 2 -ρ 6 ).…”

Section: Satellite Datamentioning

confidence: 99%

Mapping aboveground biomass by integrating geospatial and forest inventory data through a k-nearest neighbor strategy in North Central Mexico

et al. 2013

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As climate change negotiations progress, monitoring biomass and carbon stocks is becoming an important part of the current forest research. Therefore, national governments are interested in developing forest-monitoring strategies using geospatial technology. Among statistical methods for mapping biomass, there is a nonparametric approach called k-nearest neighbor (kNN). We compared four variations of distance metrics of the kNN for the spatially-explicit estimation of aboveground biomass in a portion of the Mexican north border of the intertropical zone. Satellite derived, climatic, and topographic predictor variables were combined with the Mexican National Forest Inventory (NFI) data to accomplish the purpose. Performance of distance metrics applied into the kNN algorithm was evaluated using a cross validation leave-one-out technique. The results indicate that the Most Similar Neighbor (MSN) approach maximizes the correlation between predictor and response variables (r=0.9). Our results are in agreement with those reported in the literature. These findings confirm the predictive potential of the MSN approach for mapping forest variables at pixel level under the policy of Reducing Emission from Deforestation and Forest Degradation (REDD+).

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Comparison and evaluation of Medium Resolution Imaging Spectrometer leaf area index products across a range of land use

Cited by 35 publications

References 42 publications

Temporal disparity in leaf chlorophyll content and leaf area index across a growing season in a temperate deciduous forest

Temporal disparity in leaf chlorophyll content and leaf area index across a growing season in a temperate deciduous forest

Leaf Area Index Estimation Using Vegetation Indices Derived From Airborne Hyperspectral Images in Winter Wheat

Mapping aboveground biomass by integrating geospatial and forest inventory data through a k-nearest neighbor strategy in North Central Mexico

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