Monitoring of rice crop using ENVISAT ASAR data

Because of the cloudy conditions during the rice growth period, rice is one of the agricultural crops most suited to monitoring with the SAR instruments. Backscatter response measured by SAR is correlated with rice conditions, including height, density, biomass and structure, which are variable at different growing stages. In this paper, multi-date ENVISAT ASAR Alternating Polarization Mode (APMode) imageries were acquired during the rice crop growing cycle. At the same time, the rice parameters were measured in field. A continuous canopy model was used to compute the backscattering from rice fields during the growth cycle, and the relationship between rice parameters and radar backscattering coefficients from both ASAR and modeling was analyzed. The effects of polarization, incidence angle and polarization on radar backscattering coefficients were analyzed. It was found that simulated radar backscatter has similar trends as ASAR data. This will be meaningful for the further research of rice parameters estimation from ASAR data. Different features show significantly different signatures in ASAR images and they follow some certain laws, so rice area can be accurately mapped by using multi-temporal SAR images, then rice yield can be estimated.

[1]  Guoqing Sun,et al.  Simulation of L-band and HH microwave backscattering from coniferous forest stands: a comparison with SIR-B data , 1988 .

[2]  Thuy Le Toan,et al.  Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results , 1997, IEEE Trans. Geosci. Remote. Sens..

[3]  Kamal Sarabandi,et al.  Michigan microwave canopy scattering model , 1990 .

[4]  Jingjuan Liao,et al.  Analysis of temporal radar backscatter of rice: A comparison of SAR observations with modeling results , 2002 .

[5]  Edward A. Cloutis,et al.  Agricultural crop monitoring using airborne multi-spectral imagery and C-band synthetic aperture radar , 1999 .

[6]  Seiho Uratsuka,et al.  Season-long daily measurements of multifrequency (Ka, Ku, X, C, and L) and full-polarization backscatter signatures over paddy rice field and their relationship with biological variables , 2002 .

[7]  F. Ulaby,et al.  Microwave radar response to canopy moisture, leaf-area index, and dry weight of wheat, corn, and sorghum☆ , 1981 .

[8]  A. Fung,et al.  Microwave Remote Sensing Active and Passive-Volume III: From Theory to Applications , 1986 .

[9]  Richard K. Moore,et al.  Microwave Remote Sensing, Active and Passive , 1982 .

[10]  David S. Simonett,et al.  A composite L-band HH radar backscattering model for coniferous forest stands , 1988 .

[11]  Fawwaz Ulaby,et al.  Microwave Dielectric Spectrum of Vegetation-Part I: Experimental Observations , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[12]  F. Holecz,et al.  AN OPERATIONAL RICE FIELD MAPPING TOOL USING SPACEBORNE SAR DATA , 2000 .

[13]  Masaharu Fujita,et al.  Monitoring of rice crop growth from space using the ERS-1 C-band SAR , 1995, IEEE Trans. Geosci. Remote. Sens..

[14]  A. Fung Microwave Scattering and Emission Models and their Applications , 1994 .

[15]  Fawwaz Ulaby,et al.  Microwave Dielectric Spectrum of Vegetation - Part II: Dual-Dispersion Model , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[16]  B. Brisco,et al.  Rice monitoring and production estimation using multitemporal RADARSAT , 2001 .

[17]  A. Lopes,et al.  Multitemporal and dual-polarization observations of agricultural vegetation covers by X-band SAR images , 1989, IEEE Transactions on Geoscience and Remote Sensing.

[18]  R. J. Brown,et al.  Providing crop information using RADARSAT-1 and satellite optical imagery , 2002 .

[19]  Leila Guerriero,et al.  A fully polarimetric multiple scattering model for crops , 1995 .

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