Vegetation Characterization through the Use of Precipitation-Affected SAR Signals

Ramses A. Molijn; Lorenzo Iannini; Paco López Dekker; Paulo S.G. Magalhães; Ramon F. Hanssen

doi:10.3390/rs10101647

Vegetation Characterization through the Use of Precipitation-Affected SAR Signals

Ramses A. Molijn, Lorenzo Iannini, Paco López Dekker, Paulo S.G. Magalhães, Ramon F. Hanssen

Research output: Contribution to journal › Article › Scientific › peer-review

12 Citations (Scopus)

62 Downloads (Pure)

Abstract

Current space-based SAR offers unique opportunities to classify vegetation types and to monitor vegetation growth due to its frequent acquisitions and its sensitivity to vegetation geometry. However, SAR signals also experience frequent temporal fluctuations caused by precipitation events, complicating the mapping and monitoring of vegetation. In this paper, we show that the influence of a priori known precipitation events on the signals can be used advantageously for the classification of vegetation conditions. For this, we exploit the change in Sentinel-1 backscatter response between consecutive acquisitions under varying wetness conditions, which we show is dependent on the state of vegetation. The performance further improves when a priori information on the soil type is taken into account.

Original language	English
Pages (from-to)	1-17
Number of pages	17
Journal	Remote Sensing
Volume	10
Issue number	10
DOIs	https://doi.org/10.3390/rs10101647
Publication status	Published - 2018

Bibliographical note

After publication of the research paper [1], the authors wish to make the following correction.
The link to the affiliation of Ramon F. Hanssen should have been (1). Hence, the affiliation of Ramon
F. Hanssen is Geoscience and Remote Sensing at Delft University of Technology. The authors would like to apologize for any inconvenience caused. The change does not affect the scientific results.
The manuscript will be updated and the original will remain online on the article webpage, with a reference to this correction.
Reference
1. Molijn, R.A.; Iannini, L.; López Dekker, P.; Magalhães, P.S.; Hanssen, R.F. Vegetation Characterization
through the Use of Precipitation-Affected SAR Signals. Remote Sens. 2018, 10, 1647. [CrossRef]

Keywords

Incidence angle
Precipitation
SAR signals
Soil type
Vegetation classification

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47368853 remotesensing-10-01647Final published version, 2.13 MBLicence: CC BY

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title = "Vegetation Characterization through the Use of Precipitation-Affected SAR Signals",

abstract = "Current space-based SAR offers unique opportunities to classify vegetation types and to monitor vegetation growth due to its frequent acquisitions and its sensitivity to vegetation geometry. However, SAR signals also experience frequent temporal fluctuations caused by precipitation events, complicating the mapping and monitoring of vegetation. In this paper, we show that the influence of a priori known precipitation events on the signals can be used advantageously for the classification of vegetation conditions. For this, we exploit the change in Sentinel-1 backscatter response between consecutive acquisitions under varying wetness conditions, which we show is dependent on the state of vegetation. The performance further improves when a priori information on the soil type is taken into account.",

keywords = "Incidence angle, Precipitation, SAR signals, Soil type, Vegetation classification",

author = "Molijn, {Ramses A.} and Lorenzo Iannini and Dekker, {Paco L{\'o}pez} and Magalh{\~a}es, {Paulo S.G.} and Hanssen, {Ramon F.}",

note = "After publication of the research paper [1], the authors wish to make the following correction. The link to the affiliation of Ramon F. Hanssen should have been (1). Hence, the affiliation of Ramon F. Hanssen is Geoscience and Remote Sensing at Delft University of Technology. The authors would like to apologize for any inconvenience caused. The change does not affect the scientific results. The manuscript will be updated and the original will remain online on the article webpage, with a reference to this correction. Reference 1. Molijn, R.A.; Iannini, L.; L{\'o}pez Dekker, P.; Magalh{\~a}es, P.S.; Hanssen, R.F. Vegetation Characterization through the Use of Precipitation-Affected SAR Signals. Remote Sens. 2018, 10, 1647. [CrossRef]",

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AU - Molijn, Ramses A.

AU - Iannini, Lorenzo

AU - Dekker, Paco López

AU - Magalhães, Paulo S.G.

AU - Hanssen, Ramon F.

N1 - After publication of the research paper [1], the authors wish to make the following correction. The link to the affiliation of Ramon F. Hanssen should have been (1). Hence, the affiliation of Ramon F. Hanssen is Geoscience and Remote Sensing at Delft University of Technology. The authors would like to apologize for any inconvenience caused. The change does not affect the scientific results. The manuscript will be updated and the original will remain online on the article webpage, with a reference to this correction. Reference 1. Molijn, R.A.; Iannini, L.; López Dekker, P.; Magalhães, P.S.; Hanssen, R.F. Vegetation Characterization through the Use of Precipitation-Affected SAR Signals. Remote Sens. 2018, 10, 1647. [CrossRef]

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N2 - Current space-based SAR offers unique opportunities to classify vegetation types and to monitor vegetation growth due to its frequent acquisitions and its sensitivity to vegetation geometry. However, SAR signals also experience frequent temporal fluctuations caused by precipitation events, complicating the mapping and monitoring of vegetation. In this paper, we show that the influence of a priori known precipitation events on the signals can be used advantageously for the classification of vegetation conditions. For this, we exploit the change in Sentinel-1 backscatter response between consecutive acquisitions under varying wetness conditions, which we show is dependent on the state of vegetation. The performance further improves when a priori information on the soil type is taken into account.

AB - Current space-based SAR offers unique opportunities to classify vegetation types and to monitor vegetation growth due to its frequent acquisitions and its sensitivity to vegetation geometry. However, SAR signals also experience frequent temporal fluctuations caused by precipitation events, complicating the mapping and monitoring of vegetation. In this paper, we show that the influence of a priori known precipitation events on the signals can be used advantageously for the classification of vegetation conditions. For this, we exploit the change in Sentinel-1 backscatter response between consecutive acquisitions under varying wetness conditions, which we show is dependent on the state of vegetation. The performance further improves when a priori information on the soil type is taken into account.

KW - Incidence angle

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KW - SAR signals

KW - Soil type

KW - Vegetation classification

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Vegetation Characterization through the Use of Precipitation-Affected SAR Signals

Abstract

Bibliographical note

Keywords

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