Leaf area index retrieval using IRS LISS-III sensor data and validation of the MODIS LAI product over central India

Pandya, Mehul R.; Singh, Randhir; Chaudhari, K. N.; Bairagi, G. D.; Sharma, R. P.; Dadhwal, V. K.; Parihar, J. S.

doi:10.1109/tgrs.2006.876028

Cited by 28 publications

(13 citation statements)

References 22 publications

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“…Another large uncertainty factor within this extrapolation framework, which could possibly lead to lower extrapolation and model performances, certainly is the input of MODIS LAI/FPAR data. They have often been found to be inaccurate under certain conditions [ Wang et al , 2005; Pandya et al , 2006; Pisek and Chen , 2007; Horn and Schulz , 2010], especially for needleleaf forests [ Wang et al , 2004; Yang et al , 2006a]. Additionally, there is a distinct scale mismatch between EC measurements and MODIS data, and the linkage between these two data sources is complicated by the variability of the area for which EC measurements are representative [ Chen et al 2009, 2010].…”

Section: Discussionmentioning

confidence: 99%

Spatial extrapolation of light use efficiency model parameters to predict gross primary production

Horn

Schulz

2011

J. Adv. Model. Earth Syst.

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To capture the spatial and temporal variability of the gross primary production as a key component of the global carbon cycle, the light use efficiency modeling approach in combination with remote sensing data has shown to be well suited. Typically, the model parameters, such as the maximum light use efficiency, are either set to a universal constant or to land class dependent values stored in look-up tables. In this study, we employ the machine learning technique support vector regression to explicitly relate the model parameters of a light use efficiency model calibrated at several FLUXNET sites to site-specific characteristics obtained by meteorological measurements, ecological estimations and remote sensing data. A feature selection algorithm extracts the relevant site characteristics in a cross-validation, and leads to an individual set of characteristic attributes for each parameter. With this set of attributes, the model parameters can be estimated at sites where a parameter calibration is not possible due to the absence of eddy covariance flux measurement data. This will finally allow a spatially continuous model application. The performance of the spatial extrapolation scheme is evaluated with a cross-validation approach, which shows the methodology to be well suited to recapture the variability of gross primary production across the study sites.

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Section: Discussionmentioning

confidence: 99%

Spatial extrapolation of light use efficiency model parameters to predict gross primary production

Horn

Schulz

2011

J. Adv. Model. Earth Syst.

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“…Relatively few studies used remote sensing to evaluate/validate and retrieve LAI over terrestrial vegetation in India. These studies include agroecosystems (Pandya et al, 2006), or plantations (Datta, 2011) with limited spatial coverage. However, Indian forest vegetation with high structural heterogeneity has not been studied much and need to be understood in spatio-temporal domain.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Spatio-Temporal Patterns of Leaf Area Index in Different Forest Types of India Using High Temporal Remote Sensing Data

Chhabra¹,

Panigrahy²

2012

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

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ABSTRACT:Knowledge of temporal variations of Leaf Area Index (LAI) aids in understanding the climate-vegetation interaction of different vegetative systems. This information is amenable from high temporal remote sensing data. India has around 78.37 million hectare, accounting for 23.84% of the geographic area of the country under forest/tree cover. India has a diverse set of vegetation types ranging from tropical evergreen to dry deciduous. We present a detailed spatio-temporal and inter-seasonal analysis of LAI patterns in different forest types of India using MODIS 8-day composites global LAI/fPAR product for the year 2005 at 1-km spatial resolution. A forest cover mask was generated using SPOT 1-km landuse/landcover classification over the Indian region. The range of estimated LAI varied from 0.1-6.9 among the different forest types. Maximum LAI was observed in tropical evergreen forests in North-Eastern region and Western Ghats. Low LAI was observed in Central Indian region due to predominance of dry deciduous forests. The spatial patterns of seasonal variations detected that for most of the forest types, the peak LAI values were observed during September and October months of the autumn season in contrast to minimum LAI during summer season. The mean LAI and standard deviation for each 8-day LAI composite were also computed and mean monthly LAI profiles were derived for each forest type classified on the basis of their geographical locations. These results are useful indicators for detailed understanding of phenological sequence and may also serve as important inputs for deriving bioclimatic indices for different forest types of India.

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“…Upscaled LAI had better agreement with MODIS LAI (RMSE = 0.70) when compared with field measurements. Pandya et al (2006) also showed a significant positive correlation (r = 0.62 to 0.78) between MODIS LAI product and LISS-III-derived LAI indicating a good performance of the MODIS LAI product in central India.The main reason behind matching of NDVI-based upscaled LAI to MODIS LAI is mainly due to the averaging of LAI values occurring at coarser resolutions (Williams et al 2008). This is because aggregation leads to lowering of LAI and consequently overcomes the problem of NDVI saturation at high LAI.…”

Section: Comparison Of Upscaled Lai With Modis Lai Productmentioning

confidence: 79%

Upscaling of leaf area index in Terai forest plantations using fine- and moderate-resolution satellite data

Tripathi

Patel

Kushwaha

et al. 2014

International Journal of Remote Sensing

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Ecophysiological variables, such as leaf area index (LAI), play a key role in the functioning of ecosystem processes and are thus a useful determinant of primary production, evapotranspiration, and biogeochemical cycling. In the present study, upscaling of LAI was carried out by applying a transfer function from field LAI measurements to fine-resolution LISS III images and subsequently to coarse resolution Moderate Resolution Imaging Spectroradiometer (MODIS) data using a photosynthetically active radiation (PAR)/LAI ceptometer (AccuPAR model LP-80). Field data were collected from differently aged forest plantation types in the Terai Central Forest Division of Nainital district in Uttarakhand, India. The upscaling was done by establishing an empirical exponential relationship between normalized difference vegetation index (NDVI) and LAI. Results reveal a significant relationship (p < 0.01) between NDVI and LAI for each of the studied plantation types, i.e. teak, poplar, eucalyptus, and mixed plantation. The LAI was mapped at 23.5 m resolution by applying a plantation-specific LAI versus NDVI relationship derived from IRS Linear Imaging Self-Scanning Sensor images. The LAI maps were upscaled by using a simple linear averaging within nonoverlapping windows to match with Terra-MODIS 250 m resolution NDVI images. The upscaled time series of LAI was compared with representative field-measured LAI measurements and also with MODIS LAI at a resolution of 1000 m. Upscaled LAI was found to be significantly related to the field-measured LAI with a value of 0.69 for coefficient of determination and with a root mean square error of 0.77. On the other hand, upscaled LAI has less agreement with the MODIS LAI product (R 2 = 0.5 and root mean square error = 0.70).

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Leaf area index retrieval using IRS LISS-III sensor data and validation of the MODIS LAI product over central India

Cited by 28 publications

References 22 publications

Spatial extrapolation of light use efficiency model parameters to predict gross primary production

Spatial extrapolation of light use efficiency model parameters to predict gross primary production

Analysis of Spatio-Temporal Patterns of Leaf Area Index in Different Forest Types of India Using High Temporal Remote Sensing Data

Upscaling of leaf area index in Terai forest plantations using fine- and moderate-resolution satellite data

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