volume | PIER Journals

Abstract

An outdoor crop-bed was prepared to observe scatterometer response in the angular range of 20^ο to 70^ο at VV- and HHpolarization. The soil moisture and crop variables like plant height, leaf area index and biomass of crop ladyfinger were measured at different growth stages of the crop ladyfinger. Temporal variation in scattering coefficient was found highly dependent on crop variables and observed to increase with the increase of leaf area index and biomass for both polarizations. In this paper, a novel approach is proposed for the retrieval of soil moisture and crop variables using ground truth microwave scatterometer data and artificial neural network (ANN). Two different variants of radial basis function neural network (RBFNN) algorithms were used to approximate the function described by the input output relationship between the scattering coefficient and corresponding measured values of the soil moisture and crop variables. The new model proposed in this paper gives near perfect approximation for all three target parameters namely soil moisture, biomass and leaf area index. The retrieval with minimal error obtained with the test data confirms the efficacy of the proposed model. The generalized regression network was observed to give minimal system error at a much lower spread constant.

1. Jackson, T. J., J. Schmugge, and E. T. Engman, "Remote sensing applications to hydrology: Soil moisture," Hydrological Sciences Journal, Vol. 41, 517-530, 1996.

2. Bonan, G. B., "Importance of leaf area index and forest type when estimating photosynthesis in boreal forests," Remote Sensing of Environment, Vol. 43, 303-314, 1993.
doi:10.1016/0034-4257(93)90072-6

3. Schmugge, T., "Remote sensing of soil moisture in hydrological orecasting,", 101-124, Wiley, Chichester, 1985.

4. Muukkonen, P. and J. Heiskanen, "Estimating biomass for boreal forest using ASTER satellite data combined with stand wise forest inventory data," Remote Sensing of Environment, Vol. 99, 434-447, 2005.
doi:10.1016/j.rse.2005.09.011

5. Schmugge, T. J., P. E. O'Neill, and J. R. Wang, "Passive microwave soil moisture research," IEEE Transaction on Geoscience and Remote Sensing, Vol. 24, 12-22, 1986.
doi:10.1109/TGRS.1986.289584

6. Schmugge, T. and T. J. Jackson, "Mapping soil moisture with microwave radiometers," Meteorology and Atmospheric Physics, Vol. 54, 213-223, 1994.
doi:10.1007/BF01030061

7. Wigneron, J. P., J. C. Calvet, T. Pellarin, A. A. Van de Griend, M. Berger, and P. Ferrazzoli, "Retrieving near-surface soil moisture from microwave radiometric observations: Current status and future plans," Remote Sensing of Environment, Vol. 85, 489-506, 2003.
doi:10.1016/S0034-4257(03)00051-8

8. Cookmartin, G., P. Saich, S. Quegan, R. Cordey, P. Burgess-Allen, and A. Sowter, "Modeling microwave interactions with crops and comparison with ERS-2 SAR observations," IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, 658-670, 2000.
doi:10.1109/36.841996

9. Picard, G., T. L. Toan, and F. Mattia, "Understanding C-band radar backscatter from wheat canopy using multiple scattering coherent model," IIEEE Transactions on Geoscience and Remote Sensing, Vol. 41, 1583-1591, 2003.
doi:10.1109/TGRS.2003.813353

10. Hykin, S., Neural Network a Comprehensive Foundation, 148-167, Prentice Hall, 1998.

11. Bishop, C. M., "Neural networks and their applications," Review of Scientific Instruments, Vol. 65, 1803-1832, June 1994.
doi:10.1063/1.1144830

12. Widrow, B. and M. A. Lehr, "Thirty years of adaptive neural networks: Perception, madaline and backpropagation," Proceedings of IEEE, Vol. 78, No. 9, 1415-1442, September 1990.
doi:10.1109/5.58323

13. Loyola, D. G., "Application of neural network methods to the processing of earth observation satellite data," Neural Networks, Vol. 19, 168-177, 2006.
doi:10.1016/j.neunet.2006.01.010

14. Rumelhart, D. E. and J. L. McClellend, Parallel Distributed Processing; Exploration in Microstructure of Cognition, MIT Press, Cambridge, Mass., 1986.

15. Gori, M. and A. Tesi, "On the problem of local minima in backpropagation," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 14, 76-85, 1992.
doi:10.1109/34.107014

16. Chen, F. C. and M. H. Lin, "On the learning and convergence of radial basis networks," Proceedings of IEEE International Conference on Neural Networks, 983-988, March 1993.
doi:10.1109/ICNN.1993.298691

17. Daqi, G., W. Shuyan, and J. Yan, "An Electronic nose and modular radial basis function network classifiers for recognizing multiple fragrant materials," Sensors and Actuators B, Vol. 97, 391-401, 2004.
doi:10.1016/j.snb.2003.09.018

18. Ulaby, F. T., R. K. Moore, and A. K. Fung, Microwave Remote Sensing --- Active and Passive, Vol. 2, 827-832, Addison Wesley, Reading, MA, 1982.

19. Karam, M. A., A. K. Fung, and Y. M. M. Antar, "Electromagnetic wave scattering from vegetation samples," IEEE Transactions on Geoscience and Remote Sensing, Vol. 26, No. 6, 799-808, 1988.
doi:10.1109/36.7711

20. Ulaby, F. T., T. E. Van Deventer, J. R. East, T. F. Haddock, and M. E. Colluzzi, "Millimeter wave bistatic scattering from ground and vegetation targets," IEEE Transactions on Geoscience and Remote Sensing, Vol. 26, No. 3, 229-243, 1988.
doi:10.1109/36.3026

21. Singh, D., P. K. Mukhurjee, S. K. Sharma, and K. P. Singh, "Effect of soil moisture and crop cover in remote sensing," Advances Space Research, Vol. 18, No. 7, 63-66, 1996.
doi:10.1016/0273-1177(95)00291-X

Dr. Rajendra Prasad

Dr. Ravi Kumar

Dr. Dharmendra Singh