Coupling two RADAR backscattering models to assess soil roughness and surface water content at farm scale

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14 Citazioni (Scopus)

Abstract

Remote sensing techniques are useful for agro-hydrological monitoring at the farm scale because the availability of spatially and temporally distributed data improves agricultural models for irrigation and crop yield optimization under water scarcity conditions. This research focuses on the surface water content retrieval using active microwave data. Two semi-empirical models were chosen as these showed the best performances in simulating cross and co-polarized backscatter. Thus, these models were coupled to obtain reliable assessments of both soil water content and soil roughness. The use of the coupled model enables one to avoid using roughness measured in situ. Remote sensing images and in situ data were collected between April and July 2006 within the European Space Agency-funded project AgriSAR 2006. The images data set includes L-band in HH, VV and VH polarizations acquired from the airborne E-SAR sensor, operated by the German Aerospace Centre. Results were validated using in situ soil water content and roughness measurements. The results show that reliable assessment of both soil roughness (r2 up to ˜0.8) and soil water content (r2 ˜ 0.9) can be retrieved in fields characterized by low fractional coverage.
Lingua originaleEnglish
pagine (da-a)1677-1689
Numero di pagine13
RivistaHydrological Sciences Journal
Volume58
Stato di pubblicazionePublished - 2013

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roughness
water content
farm
surface water
soil water
soil
remote sensing
crop yield
backscatter
synthetic aperture radar
polarization
irrigation
sensor
monitoring
in situ
water

All Science Journal Classification (ASJC) codes

  • Water Science and Technology

Cita questo

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title = "Coupling two RADAR backscattering models to assess soil roughness and surface water content at farm scale",
abstract = "Remote sensing techniques are useful for agro-hydrological monitoring at the farm scale because the availability of spatially and temporally distributed data improves agricultural models for irrigation and crop yield optimization under water scarcity conditions. This research focuses on the surface water content retrieval using active microwave data. Two semi-empirical models were chosen as these showed the best performances in simulating cross and co-polarized backscatter. Thus, these models were coupled to obtain reliable assessments of both soil water content and soil roughness. The use of the coupled model enables one to avoid using roughness measured in situ. Remote sensing images and in situ data were collected between April and July 2006 within the European Space Agency-funded project AgriSAR 2006. The images data set includes L-band in HH, VV and VH polarizations acquired from the airborne E-SAR sensor, operated by the German Aerospace Centre. Results were validated using in situ soil water content and roughness measurements. The results show that reliable assessment of both soil roughness (r2 up to ˜0.8) and soil water content (r2 ˜ 0.9) can be retrieved in fields characterized by low fractional coverage.",
author = "{La Loggia}, Goffredo and Fulvio Capodici and Giuseppe Ciraolo and Antonino Maltese and D'Urso",
year = "2013",
language = "English",
volume = "58",
pages = "1677--1689",
journal = "Hydrological Sciences Journal",
issn = "0262-6667",
publisher = "Taylor and Francis Ltd.",

}

TY - JOUR

T1 - Coupling two RADAR backscattering models to assess soil roughness and surface water content at farm scale

AU - La Loggia, Goffredo

AU - Capodici, Fulvio

AU - Ciraolo, Giuseppe

AU - Maltese, Antonino

AU - D'Urso, null

PY - 2013

Y1 - 2013

N2 - Remote sensing techniques are useful for agro-hydrological monitoring at the farm scale because the availability of spatially and temporally distributed data improves agricultural models for irrigation and crop yield optimization under water scarcity conditions. This research focuses on the surface water content retrieval using active microwave data. Two semi-empirical models were chosen as these showed the best performances in simulating cross and co-polarized backscatter. Thus, these models were coupled to obtain reliable assessments of both soil water content and soil roughness. The use of the coupled model enables one to avoid using roughness measured in situ. Remote sensing images and in situ data were collected between April and July 2006 within the European Space Agency-funded project AgriSAR 2006. The images data set includes L-band in HH, VV and VH polarizations acquired from the airborne E-SAR sensor, operated by the German Aerospace Centre. Results were validated using in situ soil water content and roughness measurements. The results show that reliable assessment of both soil roughness (r2 up to ˜0.8) and soil water content (r2 ˜ 0.9) can be retrieved in fields characterized by low fractional coverage.

AB - Remote sensing techniques are useful for agro-hydrological monitoring at the farm scale because the availability of spatially and temporally distributed data improves agricultural models for irrigation and crop yield optimization under water scarcity conditions. This research focuses on the surface water content retrieval using active microwave data. Two semi-empirical models were chosen as these showed the best performances in simulating cross and co-polarized backscatter. Thus, these models were coupled to obtain reliable assessments of both soil water content and soil roughness. The use of the coupled model enables one to avoid using roughness measured in situ. Remote sensing images and in situ data were collected between April and July 2006 within the European Space Agency-funded project AgriSAR 2006. The images data set includes L-band in HH, VV and VH polarizations acquired from the airborne E-SAR sensor, operated by the German Aerospace Centre. Results were validated using in situ soil water content and roughness measurements. The results show that reliable assessment of both soil roughness (r2 up to ˜0.8) and soil water content (r2 ˜ 0.9) can be retrieved in fields characterized by low fractional coverage.

UR - http://hdl.handle.net/10447/78855

UR - http://www.tandfonline.com/doi/abs/10.1080/02626667.2013.797578#.VOXGUfnF98E

M3 - Article

VL - 58

SP - 1677

EP - 1689

JO - Hydrological Sciences Journal

JF - Hydrological Sciences Journal

SN - 0262-6667

ER -