Status of microwave soil moisture measurements with remote sensing

Engman, Edwin T.; Chauhan, N.S.

doi:10.1016/0034-4257(94)00074-w

Cited by 299 publications

(126 citation statements)

References 22 publications

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“…The very nature of SAR imaging (and all imaging) is that the surface or target under study can be described only at that one particular instance when the image was acquired. While many change detection techniques have been proposed and utilised for the analysis of images acquired by optical sensors, less attention has been devoted to change detection using SAR data [134] due to its inherent complexity in terms of processing and in the development of effective data analysis techniques to minimise speckle [135,136].…”

Section: Soil Moisture Retrieval Using a Change Detection Approachmentioning

confidence: 99%

Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques

2009

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Abstract:The importance of land surface-atmosphere interactions, principally the effects of soil moisture, on hydrological, meteorological, and ecological processes has gained widespread recognition over recent decades. Its high spatial and temporal variability however, makes soil moisture a difficult parameter to measure and monitor effectively using traditional methods. Microwave remote sensing technology has demonstrated the potential to map and monitor relative soil moisture changes over large areas at regular intervals in time and also the opportunity of measuring, through inverse modelling, absolute soil moisture values. This ability has been demonstrated under a variety of topographic and land cover conditions using both active and passive microwave instruments. The purpose of this paper is to review the current status of soil moisture determination from active microwave remote sensing systems and to highlight the key areas of research that will have to be addressed to achieve routine use of the proposed retrieval approaches.

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Section: Soil Moisture Retrieval Using a Change Detection Approachmentioning

confidence: 99%

Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques

2009

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“…Conventional field measurement techniques have serious limitations in their ability to appropriately estimate the spatial distribution of soil moisture, particularly over large areas that are characterized by soil surface heterogeneity [3]. Most hydrological models require soil moisture information and use point measurements or spatial distributed soil moisture values derived from physically-based land surface models [1,2,[4][5][6]. Presently, however, spatial distributions of high resolution soil moisture estimates are being used as input to hydrological models to predict the runoff [2,7,8].…”

Section: Introductionmentioning

confidence: 99%

Non-parametric Methods for Soil Moisture Retrieval from Satellite Remote Sensing Data

et al. 2009

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Satellite remote sensing observations have the potential for efficient and reliable mapping of spatial soil moisture distributions. However, soil moisture retrievals from active microwave remote sensing data are typically complex due to inherent difficulty in characterizing interactions among land surface parameters that contribute to the retrieval process. Therefore, adequate physical mathematical descriptions of microwave backscatter interaction with parameters such as land cover, vegetation density, and soil characteristics are not readily available. In such condition, non-parametric models could be used as possible alternative for better understanding the impact of variables in the retrieval process and relating it in the absence of exact formulation. In this study, non-parametric methods such as neural networks, fuzzy logic are used to retrieve soil moisture from active microwave remote sensing data. The inclusion of soil characteristics and Normalized Difference Vegetation Index (NDVI) derived from infrared and visible measurement, have significantly improved soil moisture retrievals and reduced root mean square error (RMSE) by around 30% in the retrievals. Soil moisture derived from these methods was compared with ESTAR soil moisture (RMSE ~4.0%) and field soil moisture measurements (RMSE OPEN ACCESSRemote Sensing 2009, 1 4 6.5%). Additionally, the study showed that soil moisture retrievals from highly vegetated areas are less accurate than bare soil areas.

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“…Thus, the radar observation depth is a function of the soil moisture and it is greater for dryer soil than for moist soil. As well as the soil moisture, the remote sensing observation depth is also depending on free space wavelength, incidence angle, wave polarization, surface roughness and vegetation cover [36][37][38]. The radar penetration depth δ p , which has been introduced by Ulaby et al [39], is a function of the radar frequency system and soil moisture (soil dielectric constant).…”

Section: Theorymentioning

confidence: 99%

Surface Parameter Estimation Using Bistatic Polarimetric X-Band Measurements

Khadhra

2012

PIER B

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Abstract-The main purpose of this paper is to separately estimate the important surface parameters (soil moisture and roughness) by using full polarimetric bistatic measurements. The results provide a basis for new satellite application of future bistatic measurement systems such as the TanDEM-X satellite mission.Initially, bistatic X-band measurements, which have been recorded in the Bistatic Measurement Facility (BMF) at the DLR Oberpfaffenhofen, Microwaves and Radar Institute, will be presented. The bistatic measurement sets are composed of soils with different well-known statistical roughness scales and different moistures. The BMF has been calibrated using the Isolated Antenna Calibration Technique (IACT). The validation of the calibration was achieved by measuring the reflectivity of fresh water. In the second part, the assessment of the surface parameters (soil moisture and surface roughness) using the well calibrated data introduced in the former related part, will be detailed. The validation of the specular algorithm by estimating the soil moisture of two surfaces with different roughness scales will be reported. Additionally, a new technique using the coherent term of the Integral Equation Method (IEM) to estimate the soil roughness will be presented, as well as the sensitivity of phase and reflectivity with regard to moisture variation and therefore the penetration depth was evaluated. Current results demonstrate a non-linear relationship

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Status of microwave soil moisture measurements with remote sensing

Cited by 299 publications

References 22 publications

Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques

Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques

Non-parametric Methods for Soil Moisture Retrieval from Satellite Remote Sensing Data

Surface Parameter Estimation Using Bistatic Polarimetric X-Band Measurements

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