Submitted to: Proceedings of the American Society of Agricultural and Biological Engineers International (ASABE)
Publication Type: Proceedings
Publication Acceptance Date: 3/26/2007
Publication Date: 6/17/2007
Citation: Gowda, P., Chavez Eguez, J.L., Colaizzi, P.D., Howell, T.A., Schwartz, R.C., Marek, T.H. 2007. Relationship between LAI and Landsat TM spectral vegetation indices in the Texas Panhandle. [abstract]. In: Proceedings of the American Society of Agricultural and Biological Engineers International (ASABE). Paper No. 072013. June 17-20, 2007, Minneapolis, Minnesota.
Interpretive Summary: Leaf area index is a measure of crop leaf density. It is important in photosynthesis, crop water use, and nutrient uptake and yield of crops. It is one of the more sensitive input parameters in plant growth, energy balance, and water quality simulation models. Manual collection of these data over a large area is costly and time intensive. Remote sensing based leaf area index models are useful tools for rapid collection of these data over larger areas. However, leaf area index models developed for one region may not represent another region. Few such remote sensing models are available to estimate leaf area index for the major summer crops in the Texas Panhandle. In this study, a set of models using Landsat 5 satellite data to estimate leaf area index were developed for the Texas Panhandle. Results indicate that satellite data using the red and near infrared part of the electromagnetic spectrum were sensitive to the crop leaf area index. Remote sensing tools were promising for the rapid collection of leaf area index data on fields over large areas in the Texas Panhandle.
Technical Abstract: Mapping and monitoring leaf area index (LAI) is important for spatially distributed modeling of surface energy balance, evapotranspiration and vegetation productivity. Remote sensing can facilitate the rapid collection of LAI information on individual fields over large areas in a time and cost-effective manner. However, there are no LAI models available for the major summer crops in the Texas Panhandle. The main objective of this study was to develop statistical relationship between LAI and Landsat Thematic Mapper (TM) based spectral vegetation indices (SVI) for major crops in the Texas Panhandle. LAI was measured in 48 randomly selected commercial fields in Moore and Ochiltree counties. Data collection was made to coincide with Landsat 5 satellite overpasses on the study area. Numerous derivations of SVIs were examined for estimating LAI using ordinary least square regression models such as linear, quadratic, power and exponential models. The R2 values for the selected models varied from 0.76 to 0.84 with the power function model based on the normalized difference between TM bands 4 and 3 (NDVI) producing the best results. Analysis of the results indicated that the SVI-LAI models based on the simple ratio i.e. ratio of TM bands 4 and 3, and NDVI are most sensitive to LAI.