Soil moisture inversion algorithms using ERS-1, JERS-1, and ALMAX SAR data

Ram Mohan Narayanan, Mahabaleshwara S. Hegde

Research output: Contribution to conferencePaper

3 Scopus citations

Abstract

Although the potential of microwave radar sensors to map soil moisture has been well-understood, continuous temporal and spatial monitoring of this important hydrological parameter has been limited due to the non-availability of operational satellite-borne sensor systems. However, the recent launch of ERS-1, JERS-1 and ALMAZ satellites carrying on-board SAR instruments has made possible synoptic soil moisture monitoring a reality. These systems operate over a wide range of frequencies, look angles and transmit-receive polarizations, and thus show synergistic advantages when combined for estimating soil moisture. We have developed a neural-network based soil moisture inversion algorithm that uses as inputs radar backscattering data solely from the above sensor systems, and tested the same using simulated data with speckle added. It appears that the neural-network approach yields superior results in mapping moisture patterns compared to the linear statistical inversion technique, although both show comparable errors in volumetric soil moisture estimation.

Original languageEnglish (US)
Pages504-506
Number of pages3
StatePublished - Jan 1 1995
EventProceedings of the 1995 International Geoscience and Remote Sensing Symposium. Part 1 (of 3) - Firenze, Italy
Duration: Jul 10 1995Jul 14 1995

Other

OtherProceedings of the 1995 International Geoscience and Remote Sensing Symposium. Part 1 (of 3)
CityFirenze, Italy
Period7/10/957/14/95

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All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

Cite this

Narayanan, R. M., & Hegde, M. S. (1995). Soil moisture inversion algorithms using ERS-1, JERS-1, and ALMAX SAR data. 504-506. Paper presented at Proceedings of the 1995 International Geoscience and Remote Sensing Symposium. Part 1 (of 3), Firenze, Italy, .