Application of a new leaf area index algorithm to China's landmass using MODIS data for carbon cycle research

Liu, R.; Chen, J.M.; Liu, J.; Deng, Feng; Sun, Rui

doi:10.1016/j.jenvman.2006.04.023

Cited by 47 publications

(30 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The algorithm uses GIMMS NDVI and UMD land cover (Hansen et al, 2000) as input. Specific description of the algorithm is given by Deng et al (2006) and Liu et al (2007).…”

Section: Methodsmentioning

confidence: 99%

Spatial and temporal variation of global LAI during 1981–2006

Liu

Huo

2010

J. Geogr. Sci.

View full text Add to dashboard Cite

Abstract:Earth is always changing. Knowledge about where changes happened is the first step for us to understand how these changes affect our lives. In this paper, we use a long-term leaf area index data (LAI) to identify where changes happened and where has experienced the strongest change around the globe during . Results show that, over the past 26 years, LAI has generally increased at a rate of 0.0013 per year around the globe. The strongest increasing trend is around 0.0032 per year in the middle and northern high latitudes (north of 30°N). LAI has prominently increased in Europe, Siberia, Indian Peninsula, America and south Canada, South region of Sahara, southwest corner of Australia and Kgalagadi Basin; while noticeably decreased in Southeast Asia, southeastern China, central Africa, central and southern South America and arctic areas in North America.

show abstract

“…The algorithm uses GIMMS NDVI and UMD land cover (Hansen et al, 2000) as input. Specific description of the algorithm is given by Deng et al (2006) and Liu et al (2007).…”

Section: Methodsmentioning

confidence: 99%

Spatial and temporal variation of global LAI during 1981–2006

Liu

Huo

2010

J. Geogr. Sci.

View full text Add to dashboard Cite

show abstract

“…Therefore, AGB is considered one of the most important biochemical crop parameters [4][5][6], and correct estimation of AGB can help improve crop monitoring and yield prediction [7]. Estimation of AGB is also important for ecological research at regional and global scales [8] and has been employed in global carbon cycle [9][10][11] and climate change [12][13][14] studies.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models

et al. 2017

View full text Add to dashboard Cite

Correct estimation of above-ground biomass (AGB) is necessary for accurate crop growth monitoring and yield prediction. We estimated AGB based on images obtained with a snapshot hyperspectral sensor (UHD 185 firefly, Cubert GmbH, Ulm, Baden-Württemberg, Germany) mounted on an unmanned aerial vehicle (UAV). The UHD 185 images were used to calculate the crop height and hyperspectral reflectance of winter wheat canopies from hyperspectral and panchromatic images. We constructed several single-parameter models for AGB estimation based on spectral parameters, such as specific bands, spectral indices (e.g., Ratio Vegetation Index (RVI), NDVI, Greenness Index (GI) and Wide Dynamic Range VI (WDRVI)) and crop height and several models combined with spectral parameters and crop height. Comparison with experimental results indicated that incorporating crop height into the models improved the accuracy of AGB estimations (the average AGB is 6.45 t/ha). The estimation accuracy of single-parameter models was low (crop height only: R 2 = 0.50, RMSE = 1.62 t/ha, MAE = 1.24 t/ha; R 670 only: R 2 = 0.54, RMSE = 1.55 t/ha, MAE = 1.23 t/ha; NDVI only: R 2 = 0.37, RMSE = 1.81 t/ha, MAE = 1.47 t/ha; partial least squares regression R 2 = 0.53, RMSE = 1.69, MAE = 1.20), but accuracy increased when crop height and spectral parameters were combined (partial least squares regression modeling: R 2 = 0.78, RMSE = 1.08 t/ha, MAE = 0.83 t/ha; verification: R 2 = 0.74, RMSE = 1.20 t/ha, MAE = 0.96 t/ha). Our results suggest that crop height determined from the new UAV-based snapshot hyperspectral sensor can improve AGB estimation and is advantageous for mapping applications. This new method can be used to guide agricultural management.

show abstract

“…The GLOBCA-RBON LAI product provided by the European Space Agency is generated using a new LAI retrieval algorithm based on the 4-scale geometrical optical model [8]. This algorithm has been shown to be superior to the MODIS LAI algorithm in studies conducted in Canada, China and other regions [9][10][11].…”

mentioning

confidence: 99%

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

Liu

Chen

et al. 2012

Chin. Sci. Bull.

View full text Add to dashboard Cite

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.

show abstract

Application of a new leaf area index algorithm to China's landmass using MODIS data for carbon cycle research

Cited by 47 publications

References 25 publications

Spatial and temporal variation of global LAI during 1981–2006

Spatial and temporal variation of global LAI during 1981–2006

Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models

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

Contact Info

Product

Resources

About