|Hymer, Daniel - NASA GODDARD SPACE FLT CT|
|Qi, Jiaguo - MICHIGAN STATE UNIVERSITY|
|Kerr, Yann - CESBIO TOULOUSE FRANCE|
Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: July 1, 2000
Publication Date: N/A
Interpretive Summary: To make day-to-day farm management decisions, it is necessary to have accurate information about soil and plant conditions within each field. It may be possible to use images obtained from orbiting sensors to acquire such information. In this study, we investigated the sensitivity of images from an orbiting radar sensor to variations in field roughness, surface soil moisture, vegetation density, and plant litter. Preliminary results showed that the radar data were related directly to within-field differences in tillage, irrigation, and crop vigor. Furthermore, we demonstrated an operational approach to map field roughness conditions that could be used to assess surface subsidence and erosion. With frequent radar images of an entire farm, someday in the future it may be possible to manage farm resources with high precision at low expense. This will offer farm managers another tool to monitor and manage their crops and improve farm efficiency.
Technical Abstract: Studies over the past 25 years have shown that measurements of surface reflectance and temperature (termed optical remote sensing) are useful for monitoring crop and soil conditions. Far less attention has been given to the use of radar imagery, even though Synthetic Aperture Radar (SAR) systems have the advantages of cloud penetration, all-weather coverage, high spatial resolution, day/night acquisitions, and signal independence of the solar illumination angle. In this study, we obtained coincidental optical and SAR images of an agricultural area to investigate the use of SAR imagery for farm management. The optical and SAR data were normalized to indices ranging from 0 to 1 based on the meteorological conditions and sun/sensor geometry for each date to allow temporal analysis. These normalized indices were used in a synergistic approach to retrieve soil surface roughness from measured SAR backscatter through a series of steps in which the SAR data were filtered based on concurrent measurements in optical wavelengths. Results showed that SAR imagery was sensitive to variations in field tillage, surface soil moisture, vegetation density, and plant litter. These results should be useful for regional studies of agricultural tillage, subsidence and erosion. Recognizing the limitations of optical remote sensing data due to cloud interference and atmospheric attenuation, the findings of this study should encourage further studies of SAR imagery for crop and soil assessment.