Foliage Clumping Index Over China's Landmass Retrieved From the MODIS BRDF Parameters Product

Zhu, Guannan; Ju, Weimin; Chen, J. M.; Gong, Ping; Xing, Bailing; Zhu, Jieshun

doi:10.1109/tgrs.2011.2172213

Cited by 48 publications

(33 citation statements)

References 55 publications

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“…The slope of the zero-intercepted fitted line is comparable to the clumping index entered to the four-scale simulations (0.65, ΩE in Table 3). This result proves that with an increasing number of accurate clumping index maps [71,72], the LAI of a complex scenario (e.g., forests and savanna) can be retrieved using a 1D RT model and then post-processed with the clumping index to obtain the final result. To assess the influence of RT model selection more realistic, LAI maps, in our study area, retrieved by the SAIL and four-scale models, were compared with the corresponding 500-m reference maps.…”

Section: Impact Of Rt Model Selection On Retrieval Accuracysupporting

confidence: 53%

Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection

Yin

Liu

et al. 2015

Remote Sensing

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Physically-based approaches for estimating Leaf Area Index (LAI) using remote sensing data rely on radiative transfer (RT) models. Currently, many RT models are freely available, but determining the appropriate RT model for LAI retrieval is still problematic. This study aims to evaluate the necessity of RT model selection for LAI retrieval and to propose a retrieval methodology using different RT models for different vegetation types. Both actual experimental observations and RT model simulations were used to conduct the evaluation. Each of them includes needleleaf forests and croplands, which have contrasting structural attributes. The scattering from arbitrarily inclined leaves (SAIL) model and the four-scale model, which are 1D and 3D RT models, respectively, were used to simulate the synthetic test datasets. The experimental test dataset was established through two field campaigns conducted in the Heihe River Basin. The results show that the realistic representation of canopy structure in RT models is very important for LAI retrieval.If an unsuitable RT model is used, then the root mean squared error (RMSE) will increase from 0.43 to 0.60 in croplands and from 0.52 to 0.63 in forests. In addition, an RT model's potential to retrieve LAI is limited by the availability of a priori information on RT model OPEN ACCESSRemote Sens. 2015, 7 4605 parameters. 3D RT models require more a priori information, which makes them have poorer generalization capability than 1D models. Therefore, physically-based retrieval algorithms should embed more than one RT model to account for the availability of a priori information and variations in structural attributes among different vegetation types.

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Section: Impact Of Rt Model Selection On Retrieval Accuracysupporting

confidence: 53%

Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection

Yin

Liu

et al. 2015

Remote Sensing

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“…(2) With the MCD 43A1, MCD 43 A2 data and a modified Ross-Li model [19], the Hotspot (the zenith angles of sun and the sensor were set as 45°, and their relative azimuth angle was 0°) and Darkspot (the zenith angles of sun and the sensor were set as 45°, while their relative azimuth angle was 180°) NIR reflectances were simulated to calculate the Normalized Difference between Hotspot and Darkspot (NDHD). Then the 500 m resolution clumping index was calculated based on its relationship with NDHD.…”

Section: Lai Inversion Based On Modis Datamentioning

confidence: 99%

“…Then the 500 m resolution clumping index was calculated based on its relationship with NDHD. The topography effect on the retrieved clumping index was corrected using the 500 m DEM data [19] and the corrected clumping index was further smoothed using the LACC method [41].…”

Section: Lai Inversion Based On Modis Datamentioning

confidence: 99%

“…However, the spatial resolution (6 km) of clumping index retrieved from POLDER data is too low to meet the demands of generating global 1 km or 500 m LAI products using MODIS data. Recently, Zhu et al [19] developed a method to retrieve the clumping index over China by simulating the Hotspot and Darkspot reflectance using the MODIS BRDF product, which makes it possible to inverse true LAI using MODIS data at 1 km and 500 m resolutions.…”

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confidence: 99%

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Spatial and temporal variations of forest LAI in China during 2000–2010

Liu

Chen

et al. 2012

Chin. Sci. Bull.

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Forests are crucial terrestrial ecosystems. Their leaf area index (LAI) is a key parameter determining the exchange of matter and energy between the atmosphere and the ground surface. In this study, MOD 09A1 and MCD 43A1 data were input into an inversion model based on the 4-scale geometric optical model to retrieve 8-d 500 m LAI products in China during the period 2000 to 2010. The resulting LAI product was validated using LAI measured in 6 typical areas. The spatial and temporal variations of forest LAI and its relationships with temperature and precipitation were analyzed. The results show that the accuracy of the 500 m LAI product was above 70% in the 6 typical areas, indicating the reliability of this product.

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“…Methods for estimating LAI from remotely sensed data evolved from establishing empirical statistical relations with LAI [55] to inversion of radiative transfer models. By inverting a specific radiative transfer model -4-scale geometric optical model [56] and inverting leaf clumping from remotely sensed data [57], Liu et al [58] The atmospheric condition in China is deteriorating as the economic growth. Recently, more attention has been paid to remote sensing observation of atmospheric conditions as a new type of technology [59][60][61][62][63][64].…”

Section: Progress In Remote Sensing Of Environmental Change Over Chinamentioning

confidence: 99%

Remote sensing of environmental change over China: A review

Gong

2012

Chin. Sci. Bull.

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Foliage Clumping Index Over China's Landmass Retrieved From the MODIS BRDF Parameters Product

Cited by 48 publications

References 55 publications

Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection

Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection

Spatial and temporal variations of forest LAI in China during 2000–2010

Remote sensing of environmental change over China: A review

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