Field‐scale Leaf Area Index estimation using IRS‐1D LISS‐III data

A study was carried out to estimate field‐scale Leaf Area Index (LAI) using fine resolution polar orbiting IRS‐1D LISS‐III sensor data at 23 m spatial resolution. Three cloud‐free scenes on 8 January, 2002 (D1), 30 January, 2002 (D2) and 15 January, 2003 (D3) over two sites in Gujarat, India, were acquired. The sub‐scenes encompassing the study area were extracted and geo‐registered. Surface reflectances in the red (0.62–0.68 µm) and near‐infrared (NIR) (0.77–0.86 µm) bands were generated using 6S atmospheric correction code and coincident ground measurements on aerosol and water vapour. Normalized difference vegetation index (NDVI), simple ratio (SR) and soil‐adjusted vegetation index (SAVI) were computed from the reflectances in the red and NIR. A total of 70 mean measured LAI datasets on wheat and tobacco were used for regression analysis and empirical models were developed between LAI and three vegetation indices (VI). Both exponential and power models gave R 2 between 0.53 and 0.61 except for D2 (R 2 between 0.04 and 0.11) when wheat was mostly at the post‐anthesis stage and the VI–LAI relation seems to be influenced by canopy geometry and angular distribution of leaves. The analysis indicated that with the soil type of the study sites being different, the SAVI‐based model had a smaller rms. error (R 2 = 0.496 and rms. error = 0.685) in estimation of LAI when compared with the SR‐ (R 2 = 0.478, rms. error = 0.698) and NDVI‐ (R 2 = 0.491, rms. error = 0.689) based models. The LAIs for the study region were estimated by inversion of empirical models and validated against ground‐measured data.

[1]  D. Diner,et al.  Estimation of vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from atmosphere‐corrected MISR data , 1998 .

[2]  C. Wiegand,et al.  Leaf Area, Light Interception, and Yield Estimates from Spectral Components Analysis1 , 1984 .

[3]  J. Norman,et al.  Instrument for Indirect Measurement of Canopy Architecture , 1991 .

[4]  E. Kanemasu,et al.  Leaf Area Index Estimates for Wheat from LANDSAT and Their Implications for Evapotranspiration and Crop Modeling1 , 1979 .

[5]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[6]  Hongliang Fang,et al.  Retrieving leaf area index with a neural network method: simulation and validation , 2003, IEEE Trans. Geosci. Remote. Sens..

[7]  P. Warnant,et al.  The seasonality of the CO2 exchange between the atmosphere and the land biosphere: A study with a global mechanistic vegetation model , 1996 .

[8]  V. K. Dadhwal,et al.  Comparison of principal component inversion with VI-empirical approach for LAI estimation using simulated reflectance data , 2004 .

[9]  B. Bouman,et al.  Accuracy of estimating the leaf area index from vegetation indices derived from crop reflectance characteristics, a simulation study , 1992 .

[10]  L. Kergoat.  A model for hydrological equilibrium of leaf area index on a global scale , 1998 .

[11]  A. Bondeau,et al.  Combining agricultural crop models and satellite observations: from field to regional scales , 1998 .

[12]  S. Leblanc,et al.  A Shortwave Infrared Modification to the Simple Ratio for LAI Retrieval in Boreal Forests: An Image and Model Analysis , 2000 .

[13]  R. Jackson,et al.  Multisite Analyses of Spectral-Biophysical Data for Wheat , 1992 .

[14]  J. Chen,et al.  GeoComp-n, an advanced system for the processing of coarse and medium resolution satellite data. Part 2: Biophysical products for Northern ecosystems , 2002 .