Influence of Vegetation Cover on the Oh Soil Moisture Retrieval Model: A Case Study of the Malinda Wetland, Tanzania

Kirimi, Fridah; Kuria, David Ndegwa; Thonfeld, Frank; Amler, Esther; Mubea, Kenneth; Misana, Salome; Menz, Gunter

doi:10.4236/ars.2016.51003

Cited by 9 publications

(5 citation statements)

References 35 publications

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“…Detailed descriptions of each texture measure can be obtained in Haralick (1979). We searched for local precipitation data near our study site, given that humidity has an influence on backscatter values (Kirimi et al 2016). The meteorological stations closest to our study site were Porto Velho (code A925 113 Kilometers away) and Ariquemes (code A940, 80 km away).…”

Section: Sentinel Data Acquisition and Processingmentioning

confidence: 99%

Evaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest

et al. 2019

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Section: Sentinel Data Acquisition and Processingmentioning

confidence: 99%

Evaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest

et al. 2019

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“…As for semi-empirical or empirical models, they are often difficult to apply to sites other than those on which they were developed and are generally valid only for specific soil conditions. The empirical models are often favored by users because the models are easier to implement and invert [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A New Empirical Model for Radar Scattering from Bare Soil Surfaces

Baghdadi¹,

Choker²,

Zribi

et al. 2016

Remote Sensing

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Abstract:The objective of this paper is to propose a new semi-empirical radar backscattering model for bare soil surfaces based on the Dubois model. A wide dataset of backscattering coefficients extracted from synthetic aperture radar (SAR) images and in situ soil surface parameter measurements (moisture content and roughness) is used. The retrieval of soil parameters from SAR images remains challenging because the available backscattering models have limited performances. Existing models, physical, semi-empirical, or empirical, do not allow for a reliable estimate of soil surface geophysical parameters for all surface conditions. The proposed model, developed in HH, HV, and VV polarizations, uses a formulation of radar signals based on physical principles that are validated in numerous studies. Never before has a backscattering model been built and validated on such an important dataset as the one proposed in this study. It contains a wide range of incidence angles (18 • -57 • ) and radar wavelengths (L, C, X), well distributed, geographically, for regions with different climate conditions (humid, semi-arid, and arid sites), and involving many SAR sensors. The results show that the new model shows a very good performance for different radar wavelengths (L, C, X), incidence angles, and polarizations (RMSE of about 2 dB). This model is easy to invert and could provide a way to improve the retrieval of soil parameters.

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“…Earlier studies have demonstrated the necessity to eliminate the effects of vegetation on soil moisture retrieval [71]. It was discovered that soil moisture retrieval is much more accurate when vegetation cover is considered [72]. To further explore the backscatter coefficients derived in Section 4.3 with different vegetation descriptors, the soil moisture was retrieved from the backscatter to allow comparison of observed soil moisture in the oil palm study area.…”

Section: Vegetation Effects On Soil Moisture Retrieval Based On Polarizationmentioning

confidence: 99%

Vegetation Effects on Soil Moisture Retrieval from Water Cloud Model Using PALSAR-2 for Oil Palm Trees

et al. 2021

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In oil palm crop, soil fertility is less important than the physical soil characteristics. It is important to have a balance and sufficient soil moisture to sustain high yields in oil palm plantations. However, conventional methods of soil moisture determination are laborious and time-consuming with limited coverage and accuracy. In this research, we evaluated synthetic aperture radar (SAR) and in-situ observations at an oil palm plantation to determine SAR signal sensitivity to oil palm crop by means of water cloud model (WCM) inversion for retrieving soil moisture from L-band HH and HV polarized data. The effects of vegetation on backscattering coefficients were evaluated by comparing Leaf Area Index (LAI), Leaf Water Area Index (LWAI) and Normalized Plant Water Content (NPWC). The results showed that HV polarization effectively simulated backscatter coefficient as compared to HH polarization where the best fit was obtained by taking the LAI as a vegetation descriptor. The HV polarization with the LAI indicator was able to retrieve soil moisture content with an accuracy of at least 80%.

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Influence of Vegetation Cover on the Oh Soil Moisture Retrieval Model: A Case Study of the Malinda Wetland, Tanzania

Cited by 9 publications

References 35 publications

Evaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest

Evaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest

A New Empirical Model for Radar Scattering from Bare Soil Surfaces

Vegetation Effects on Soil Moisture Retrieval from Water Cloud Model Using PALSAR-2 for Oil Palm Trees

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