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

Ram Mohan Narayanan, Mahabaleshwara S. Hegde

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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)
Title of host publicationInternational Geoscience and Remote Sensing Symposium (IGARSS)
Pages504-506
Number of pages3
Volume1
StatePublished - 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

Fingerprint

Soil moisture
synthetic aperture radar
soil moisture
sensor
Sensors
Radar
radar
Satellites
Neural networks
Monitoring
speckle
Backscattering
monitoring
Speckle
Moisture
polarization
Microwaves
moisture
inversion
ERS

All Science Journal Classification (ASJC) codes

  • Software
  • Geology

Cite this

Narayanan, R. M., & Hegde, M. S. (1995). Soil moisture inversion algorithms using ERS-1, JERS-1, and ALMAX SAR data. In International Geoscience and Remote Sensing Symposium (IGARSS) (Vol. 1, pp. 504-506)
Narayanan, Ram Mohan ; Hegde, Mahabaleshwara S. / Soil moisture inversion algorithms using ERS-1, JERS-1, and ALMAX SAR data. International Geoscience and Remote Sensing Symposium (IGARSS). Vol. 1 1995. pp. 504-506
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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.",
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Narayanan, RM & Hegde, MS 1995, Soil moisture inversion algorithms using ERS-1, JERS-1, and ALMAX SAR data. in International Geoscience and Remote Sensing Symposium (IGARSS). vol. 1, pp. 504-506, Proceedings of the 1995 International Geoscience and Remote Sensing Symposium. Part 1 (of 3), Firenze, Italy, 7/10/95.

Soil moisture inversion algorithms using ERS-1, JERS-1, and ALMAX SAR data. / Narayanan, Ram Mohan; Hegde, Mahabaleshwara S.

International Geoscience and Remote Sensing Symposium (IGARSS). Vol. 1 1995. p. 504-506.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Hegde, Mahabaleshwara S.

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N2 - 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.

AB - 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.

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Narayanan RM, Hegde MS. Soil moisture inversion algorithms using ERS-1, JERS-1, and ALMAX SAR data. In International Geoscience and Remote Sensing Symposium (IGARSS). Vol. 1. 1995. p. 504-506