Synergy of optical and radar remote sensing in agricultural applications

Ability to estimate crop information from remotely sensed imagery is fundamental in precision agriculture. Traditional approach using optical remote sensing is often limited by cloud-free quality imagery while microwave radar has not been fully explored to infer crop conditions. There is a need to develop an alternative to infer crop information that overcomes these limitations. In this study, an optical/radar synergy was developed and used to examine its potential for extracting soil and plant information. The synergy uses a microwave scattering model developed by Karam and his colleagues but modified to (1) take into account underneath soil backscattering properties and (2) use optical remote sensing as direct input variables to the model. The synergistic method was applied to two data sets from Maricopa Agricultural Center, Maricopa, Arizona, and the experimental fields of the National Institute for Agro-Environmental Sciences, Tsukuba, Japan. The data sets included images from Landsat and ERS satellites as well as some ground based soil and plant measurements. The preliminary results indicate that radar imagery can be effectively integrated with optical imagery for extracting both soil and plant information. There exist potentials to use such synergy for site-specific agricultural management practices.

[1]  P. J. Pinter,et al.  Remote sensing for crop protection , 1993 .

[2]  M. S. Moran,et al.  Opportunities and limitations for image-based remote sensing in precision crop management , 1997 .

[3]  F. Ulaby,et al.  Microwave Backscatter Dependence on Surface Roughness, Soil Moisture, and Soil Texture: Part I-Bare Soil , 1978, IEEE Transactions on Geoscience Electronics.

[4]  E. Engman,et al.  Status of microwave soil moisture measurements with remote sensing , 1995 .

[5]  F. Baret,et al.  Potentials and limits of vegetation indices for LAI and APAR assessment , 1991 .

[6]  Fawwaz Ulaby,et al.  Preliminaly Evaluation of the SIR-B Response to Soil Moisture, Surface Roughness, and Crop Canopy Cover , 1986, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Yann Kerr,et al.  Leaf area index estimates using remotely sensed data and BRDF models in a semiarid region. , 2000 .

[8]  Jiaguo Qi,et al.  Soil moisture estimation in a semiarid rangeland using ERS-2 and TM imagery , 2004 .

[9]  J. C. Price,et al.  Leaf area index estimation from visible and near-infrared reflectance data , 1995 .

[10]  Ghassem R. Asrar,et al.  Estimates of leaf area index from spectral reflectance of wheat under different cultural practices and solar angle , 1985 .

[11]  S. Running,et al.  Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active , 1998 .

[12]  Adrian K. Fung,et al.  A microwave scattering model for layered vegetation , 1992, IEEE Trans. Geosci. Remote. Sens..