Published

2018-01-01

Derivation of Soil Moisture using Modified Dubois Model with field assisted surface roughness on RISAT-1 data

Medición de la húmedad del suelo a través del Modelo Modificado de Dubois con superficie desigual, basado en información del Radar Imaging Satellite-1

DOI:

https://doi.org/10.15446/esrj.v22n1.59972

Keywords:

Soil moisture, surface roughness, Modified Dubois Model, Topp’s model, RISAT 1- SAR. (en)
Húmedad del suelo, desigualdad de la superficie, Modelo Modificado de Dubois, Modelo de Topp, Radar de Apertura Sintética, (es)

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Authors

  • Thanabalan Palanisamy Institute of Remote Sensing Anna University Chennai, India
  • Vidhya R Professor Institute of Remote Sensing Department of Civil Engineering Anna University Chennai - 600 025

The study discusses the soil moisture estimation using dual polarimetric RISAT-1. The semi-empirical approach of Modified Dubois Model (MDM) derived by (SrinivasaRao 2013) is worked out using (σ ̊HH) and (σ ̊HV) for soil moisture estimation using dual polarimetric backscattering image. IRS LISS IV data have been used to analyze the site suitability of different land use/cover types. The retrieval of backscattering coefficient values (σ ̊) from SAR is the common principle factor for soil moisture estimation. The surface roughness was measured in the selected sample location, for which the same backscattering values derived from the SAR is linearly correlated showing r2 = 0.93. The estimated surface roughness is used for retrieval of dielectric constant using MDM. The dielectric constant derived from MDM in combination with the Topps model proposed by (Topp 1980), is used to derive satellite-based soil moisture estimation. Linear regression analysis was performed, and the soil moisture derived from SAR are well correlated with the volumetric soil moisture showing the value of r2 = 0.63. 

Este estudio discute la estimación de la húmedad del suelo medida con el Radar de Imágenes Satelitales (RISAT-1) doble polarimétrico. La aproximación semiempírica del Modelo Modificado de Dubois (MDM), derivada por Srinivasa Rao (2013), se resuelve a través de (σ ̊HH) y (σ ̊HV) para la estimación de la húmedad del suelo usando la imagen de retrodispersión doble polarimétrica. La información de las imágenes LISS IV se utilizó para analizar la pertinencia de diferentes tipos de uso del suelo y coberturas. La recuperación de los valores de coeficientes retrodispersos (σ ̊) del Radar de Apertura Sintética (SAR, del inglés Synthetic Aperture Radar) es el factor común principal para la estimación de la húmedad del suelo. La desigualdad de la superficie se midió en la ubicación del ejemplo seleccionado, por lo que los mismos valores retrodispersos derivados del SAR están correlacionados linealmente con r2 = 0.93. La rugosidad de la superficie estimada se utilizó para la recuperación de la constante dieléctrica a través del Modelo Modificado de Dubois. La constante dielétrica derivada del MDM, combinada con el modelo de Topps, se utilizó para obtener la estimación de la húmedad del suelo a partir de datos satelitales. Se realizó el análisis de regresión lineal y la húmedad del suelo derivada del SAR está bien correlacionada con el índice volumétrico de húmedad del suelo con un valor r2 = 0.63 

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How to Cite

APA

Palanisamy, T. and R, V. (2018). Derivation of Soil Moisture using Modified Dubois Model with field assisted surface roughness on RISAT-1 data. Earth Sciences Research Journal, 22(1), 13–18. https://doi.org/10.15446/esrj.v22n1.59972

ACM

[1]
Palanisamy, T. and R, V. 2018. Derivation of Soil Moisture using Modified Dubois Model with field assisted surface roughness on RISAT-1 data. Earth Sciences Research Journal. 22, 1 (Jan. 2018), 13–18. DOI:https://doi.org/10.15446/esrj.v22n1.59972.

ACS

(1)
Palanisamy, T.; R, V. Derivation of Soil Moisture using Modified Dubois Model with field assisted surface roughness on RISAT-1 data. Earth sci. res. j. 2018, 22, 13-18.

ABNT

PALANISAMY, T.; R, V. Derivation of Soil Moisture using Modified Dubois Model with field assisted surface roughness on RISAT-1 data. Earth Sciences Research Journal, [S. l.], v. 22, n. 1, p. 13–18, 2018. DOI: 10.15446/esrj.v22n1.59972. Disponível em: https://revistas.unal.edu.co/index.php/esrj/article/view/59972. Acesso em: 24 apr. 2024.

Chicago

Palanisamy, Thanabalan, and Vidhya R. 2018. “Derivation of Soil Moisture using Modified Dubois Model with field assisted surface roughness on RISAT-1 data”. Earth Sciences Research Journal 22 (1):13-18. https://doi.org/10.15446/esrj.v22n1.59972.

Harvard

Palanisamy, T. and R, V. (2018) “Derivation of Soil Moisture using Modified Dubois Model with field assisted surface roughness on RISAT-1 data”, Earth Sciences Research Journal, 22(1), pp. 13–18. doi: 10.15446/esrj.v22n1.59972.

IEEE

[1]
T. Palanisamy and V. R, “Derivation of Soil Moisture using Modified Dubois Model with field assisted surface roughness on RISAT-1 data”, Earth sci. res. j., vol. 22, no. 1, pp. 13–18, Jan. 2018.

MLA

Palanisamy, T., and V. R. “Derivation of Soil Moisture using Modified Dubois Model with field assisted surface roughness on RISAT-1 data”. Earth Sciences Research Journal, vol. 22, no. 1, Jan. 2018, pp. 13-18, doi:10.15446/esrj.v22n1.59972.

Turabian

Palanisamy, Thanabalan, and Vidhya R. “Derivation of Soil Moisture using Modified Dubois Model with field assisted surface roughness on RISAT-1 data”. Earth Sciences Research Journal 22, no. 1 (January 1, 2018): 13–18. Accessed April 24, 2024. https://revistas.unal.edu.co/index.php/esrj/article/view/59972.

Vancouver

1.
Palanisamy T, R V. Derivation of Soil Moisture using Modified Dubois Model with field assisted surface roughness on RISAT-1 data. Earth sci. res. j. [Internet]. 2018 Jan. 1 [cited 2024 Apr. 24];22(1):13-8. Available from: https://revistas.unal.edu.co/index.php/esrj/article/view/59972

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3. Rucha Dave, Gaurav Kumar, Dharmendra Kr. Pandey, Armugha Khan, Bimal Bhattacharya. (2021). Evaluation of modified Dubois model for estimating surface soil moisture using dual polarization RISAT-1 C-band SAR data. Geocarto International, 36(13), p.1459. https://doi.org/10.1080/10106049.2019.1655801.

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