Monitoring Sugarcane Growth Using ENVISAT ASAR Data

The objective of this paper is to investigate potential of satellite C-band synthetic aperture radar (SAR) radar in monitoring sugarcane growth in southern China. This paper proposes a method to map sugarcane growing area and retrieve sugarcane leaf area index (LAI) in different growth stages using ENVISAT Advanced SAR (ASAR) alternating polarization HH/HV data. The temporal response of ASAR alternating polarization HH/HV data to sugarcane fields and sugarcane LAI was first analyzed in the study area. The analysis shows that sugarcane fields have increasing temporal radar response trend with sugarcane growth and ratio of ASAR HV to HH data has a better correlation with the increase of sugarcane LAI. A theoretical radiative transfer model was adopted to interpret the trend. Based on the temporal variation of the radar response of sugarcane fields, a method for mapping sugarcane planting area was developed using ASAR HH and HV polarization data at two acquisition dates with a certain classification accuracy. The empirical models were also established to estimate LAI of sugarcane using the HV/HH polarization ratio. The results suggest that C-band ASAR data appear promising in the development of an operational system for monitoring sugarcane growth in southern China.

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

[2]  Simonetta Paloscia,et al.  The relationship between the backscattering coefficient and the biomass of narrow and broad leaf crops , 2001, IEEE Trans. Geosci. Remote. Sens..

[3]  Sushma Panigrahy,et al.  Analysis of temporal backscattering of cotton crops using a semiempirical model , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[4]  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.

[5]  J. Clevers The use of imaging spectrometry for agricultural applications , 1999 .

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

[7]  Jean-Pierre Wigneron,et al.  A simple approach to monitor crop biomass from C-band radar data , 1999 .

[8]  F. Ulaby,et al.  Effects of Vegetation Cover on the Microwave Radiometric Sensitivity to Soil Moisture , 1983, IEEE Transactions on Geoscience and Remote Sensing.

[9]  C. R. de Souza Filho,et al.  ASTER and Landsat ETM+ images applied to sugarcane yield forecast , 2006 .

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

[11]  Kamal Sarabandi,et al.  Semi-empirical model of the ensemble-averaged differential Mueller matrix for microwave backscattering from bare soil surfaces , 2002, IEEE Trans. Geosci. Remote. Sens..

[12]  Donald B. Percival,et al.  Probability density functions for multilook polarimetric signatures , 1994, IEEE Trans. Geosci. Remote. Sens..

[13]  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..

[14]  J. Clevers Application of a weighted infrared-red vegetation index for estimating leaf Area Index by Correcting for Soil Moisture , 1989 .

[15]  Hean-Teik Chuah,et al.  A preliminary study of phenological growth stages of wetland rice using ERS1/2 SAR data , 1997, IGARSS'97. 1997 IEEE International Geoscience and Remote Sensing Symposium Proceedings. Remote Sensing - A Scientific Vision for Sustainable Development.

[16]  Keith P. B. Thomson,et al.  Adaptation of the MIMICS backscattering model to the agricultural context-wheat and canola at L and C bands , 1994, IEEE Trans. Geosci. Remote. Sens..

[17]  Roger D. De Roo,et al.  A semi-empirical backscattering model at L-band and C-band for a soybean canopy with soil moisture inversion , 2001, IEEE Trans. Geosci. Remote. Sens..

[18]  Miina Rautiainen,et al.  Leaf area index estimation of boreal forest using ENVISAT ASAR , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Jakob J. van Zyl,et al.  Change detection techniques for ERS-1 SAR data , 1993, IEEE Trans. Geosci. Remote. Sens..

[20]  José Alexandre Melo Demattê,et al.  Discrimination of sugarcane varieties using Landsat 7 ETM+ spectral data , 2006 .

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

[22]  Bernardo Friedrich Theodor Rudorff,et al.  Multi‐temporal analysis of MODIS data to classify sugarcane crop , 2006 .

[23]  D. A. Teruel,et al.  Sugarcane leaf area index modeling under different soil water conditions , 1997 .