Calculations of radar backscattering coefficient of vegetation-covered soils

Mo, Tsan; Schmugge, Thomas J.; Jackson, Thomas J.

doi:10.1016/0034-4257(84)90041-5

Cited by 49 publications

(4 citation statements)

References 10 publications

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“…Satellite remote sensing of soil moisture is generally characterized by sensor types (i.e., active and passive) and microwave bands such as X-, C-, and L-bands used for the retrievals. Whereas, active microwave sensors transmit pulses of microwave energy to the land surface and measure the backscatter coefficient over vegetated regions (Mo et al, 1984;Wagner et al, 1999), the passive sensors observe microwave emission at various frequencies from the Earth's surface (Njoku and Kong, 1977;Jackson et al, 1982;Mo et al, 1982). The latter (i.e., passive) largely occupies the history of SM remote sensing compared to the active sensors (Kim et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A Triple Collocation-Based Comparison of Three L-Band Soil Moisture Datasets, SMAP, SMOS-IC, and SMOS, Over Varied Climates and Land Covers

2021

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Soil moisture plays an important role in the hydrologic water cycle. Relative to in-situ soil moisture measurements, remote sensing has been the only means of monitoring global scale soil moisture in near real-time over the past 40 years. Among these, soil moisture products from radiometry sensors operating at L-band, e.g., SMAP, SMOS, and SMOS-IC, are theoretically established to be more advantageous than previous C/X-band products. However, little effort has been made to investigate the inter-product differences of L-band soil moisture retrievals and provide insights into the optimal use of these products. In this regard, this study aims to identify the relative strengths and weaknesses of three L-band soil moisture products across diverse climate zones and land covers at the global scale using triple collocation analysis. Results show that SMOS-IC exhibits significantly improved soil moisture estimation skills, relative to the original SMOS product. This demonstrates the paramount importance of retrieval algorithm development in improving global soil moisture estimates—given both SMOS-IC and SMOS are using the same L-band brightness temperature information. Relative to SMOS-IC, SMAP is superior across 69% of global land surface in terms of error variances. However, SMOS-IC tends to outperform SMAP over temperate/arid regions including in the east of North America, South America, western Africa, northern China, and central Australia. Additionally, considerable performance degradation of all the L-band data products is observed over unvegetated areas. This may suggest that improving soil moisture retrieval accuracy over arid and semi-arid regions should be a key priority for future L-band soil moisture development, and model-based (e.g., GLDAS) soil moisture products appear to provide more accurate soil moisture estimates over these regions.

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Section: Introductionmentioning

confidence: 99%

A Triple Collocation-Based Comparison of Three L-Band Soil Moisture Datasets, SMAP, SMOS-IC, and SMOS, Over Varied Climates and Land Covers

2021

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“…For exposed soil surface or surface with thin vegetation, σ s pp plays a dominant role in the received signal and is influenced mainly by soil moisture and surface roughness. For areas where the vegetation layer is thick, the scattering is determined to be largely influenced by scattering from the vegetation canopy (Mo et al, 1984).…”

Section: Active Radar Remote Sensing Methodsmentioning

confidence: 99%

Evaluation of soil surface moisture in Dak Nong province, Vietnam by Sentinel-1 radar image

Vu,

Nguyen,

2023

JMES

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This study focuses on monitoring the variation of surface soil moisture (0-1 cm) and ground soil moisture (0÷1 m) in the soil in Dak Nong province using Sentinel-1 radar images received in December 2017. Active radar signals utilized in this method were sent and received using short wave pulses. The intensity of the received signal is compared to determine the scattering coefficient of the surface. The most common imaging constructs of active shortwave are Synthetic Aperture Radar (SAR), which transmits a series of pulses that pass through a radar antenna. Soil moisture is calculated by measuring electromagnetic radiation in the wavelength range from 0,5÷100 cm based on the contrast between the dielectric properties of water (~80) and soil particles (<4). With increasing humidity, the dielectric constant of the water mixture increases, this change can be detected by radar sensors. The results of moisture estimation by satellite images are checked by the results of field sampling and laboratory analysis. Radar remote sensing technique has demonstrated the ability to estimate soil moisture on the principle of correlating with actual measured data. The return scattering value of the soil consists of two components: the former is the feedback scattering value of the raw soil; while the latter is the feedback scattering value of the vegetation cover. It is recommended that this technology should be applied to territories with complex, difficult-to-access terrains and few meteorological monitoring stations.

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“…The difference in energy between emitted and reflected microwave radiations is the backscatter coefficient ( σ °). The backscatter coefficient is in general expressed in decibels (dB) ( Mo et al, 1984 ). The retrieval of soil moisture from radar sensors was obtained in the 1970s ( Jackson et al, 1981;MacDonald and Waite 1971;Ulaby 1974;Ulaby et al, 1974Ulaby et al, , 1978Ulaby and Batlivala 1976 ), simultaneously with the development of passive microwave sensor retrievals.…”

Section: Retrieval Of Soil Moisture From Active Microwave Sensorsmentioning

confidence: 99%

Four decades of microwave satellite soil moisture observations: Part 1. A review of retrieval algorithms

Karthikeyan

Pan

Wanders

et al. 2017

Advances in Water Resources

132

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Calculations of radar backscattering coefficient of vegetation-covered soils

Cited by 49 publications

References 10 publications

A Triple Collocation-Based Comparison of Three L-Band Soil Moisture Datasets, SMAP, SMOS-IC, and SMOS, Over Varied Climates and Land Covers

A Triple Collocation-Based Comparison of Three L-Band Soil Moisture Datasets, SMAP, SMOS-IC, and SMOS, Over Varied Climates and Land Covers

Evaluation of soil surface moisture in Dak Nong province, Vietnam by Sentinel-1 radar image

Four decades of microwave satellite soil moisture observations: Part 1. A review of retrieval algorithms

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