Potential of L-Band Radar for Retrieval of Canopy and Subcanopy Parameters of Boreal Forests

Tabatabaeenejad, Alireza; Burgin, Mariko; Moghaddam, Mahta

doi:10.1109/tgrs.2011.2173349

Cited by 23 publications

(13 citation statements)

References 30 publications

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“…In the study area, the main crop is corn in cropland, which has been considered in the SMAP mission [14,59], thus leading to best performance in cropland in the study area far beyond the accuracy of 0.04 m 3 /m 3 . For coniferous forest, the parameters of evergreen needle leaf have been calibrated and validated by the CanExSM10 (The Canadian Experiment for Soil Moisture in 2010) measurements [14,60,61]. The main tree types are pine and spruce over the BERMS (Boreal Ecosystem Research and Monitoring Sites) forested sites in CanExSM10 [61], both of them are coniferae as well as picea crassifolia in our study area.…”

Section: Impacts Of Parameters B P and H Under Different Vegetation Tmentioning

confidence: 99%

Multi-Scale Evaluation of the SMAP Product Using Sparse In-Situ Network over a High Mountainous Watershed, Northwest China

Zhang

2017

Remote Sensing

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Abstract:As the latest L-band mission to date, evaluation of the Soil Moisture Active Passive (SMAP) products is one of its post-launch objectives. However, almost all previous studies have been conducted at the core validation sites (CVS) of the SMAP mission. This paper presents an evaluation of the SMAP soil moisture Level 3 (L3) and Level 4 (L4) products under different vegetation types at multiple tempo-spatial scales over the upper reach of the Heihe River Watershed, a topographically complex mountainous area in Northwest China. This was done through comparisons of the L3 and L4 products with ground-based observations from a sparse in situ network of permanent and temporary stations from 1 April 2015 to 22 June 2017. Results show that, compared with in situ observations at point scale, both the L3 and L4 products represent the temporal trends of the in situ observations in the study area well, with R values of 0.601 and 0.538 for the L3 ascending and descending products, respectively, and ranging from 0.353 to 0.410 for the L4 product at eight overpassing moments. However, because of the uncertainties of brightness temperature T Bp and effective temperature T eff as well as their propagations in the inversion algorithm, both products did not achieve the accuracy of 0.04 m 3 /m 3 in mountainous area. These uncertainties also result in the "dry bias" of the SMAP products in almost all the evaluations to date. Compared with areal average values at the watershed scale, the L3 product is far beyond the accuracy of 0.04 m 3 /m 3 and the L4 product basically achieves the accuracy. In vegetation-covered land, the suitability and the variability of the coefficient b p result in both products performing best in cropland, then coniferous forest, sparse grassland, dense grassland, and alpine meadow, and worst in shrub. In barren land, the errors in estimating surface roughness h caused by the complex topography lead to poor performance of the SMAP products. With the relative errors of the SMAP brightness temperature observations and the corresponding land model forecast in the assimilation; the L3 and L4 products show different performance at both temporal and spatial scales; and the L3 product provides more reliable soil moisture estimates in the study area. Based on the results of this study, we propose: quantifying the uncertainties in estimating brightness temperature T Bp and effective temperature T eff ; determine coefficient b p and surface roughness h factor under various conditions; improving Goddard Earth Observing Model System Version 5 (GEOS-5) model; and deriving the SMAP-only climatology to improve the SMAP soil moisture estimates in the future.

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Section: Impacts Of Parameters B P and H Under Different Vegetation Tmentioning

confidence: 99%

Multi-Scale Evaluation of the SMAP Product Using Sparse In-Situ Network over a High Mountainous Watershed, Northwest China

Zhang

2017

Remote Sensing

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“…The forest vegetation and soil parameters measured during SMAPVEX12 are currently being used to parameterize radar scattering models that are the bases for retrieving soil moisture under the forest canopies. This will follow a similar approach to that taken in the CanEX-10 field campaign [51].…”

Section: ) Forest Vegetation and Soilmentioning

confidence: 99%

The Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12): Prelaunch Calibration and Validation of the SMAP Soil Moisture Algorithms

McNairn

Jackson

Wiseman

et al. 2015

IEEE Trans. Geosci. Remote Sensing

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222

117

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The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite is scheduled for launch in January 2015. In order to develop robust soil moisture retrieval algorithms that fully exploit the unique capabilities of SMAP, algorithm developers had identified a need for long-duration combined active and passive L-band microwave observations. In response to this need, a joint Canada-U.S. field experiment (SMAPVEX12) was conducted in Manitoba (Canada) over a six-week period in 2012. Several times per week, NASA flew two aircraft carrying instruments that could simulate the observations the SMAP satellite would provide. Ground crews collected soil moisture data, crop measurements, and biomass samples in support of this campaign. The objective of SMAPVEX12 was to support the development, enhancement, and testing of SMAP soil moisture retrieval algorithms. This paper details the airborne and field data collection as well as data calibration and analysis. Early results from the SMAP active radar retrieval methods are presented and demonstrate that relative and absolute soil moisture can be delivered by this approach. Passive active L-band sensor Manuscript (PALS) antenna temperatures and reflectivity, as well as backscatter, closely follow dry down and wetting events observed during SMAPVEX12. The SMAPVEX12 experiment was highly successful in achieving its objectives and provides a unique and valuable data set that will advance algorithm development.Index Terms-Passive microwave, soil moisture, Soil Moisture Active Passive (SMAP), synthetic aperture radar.

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“…The inversion of the model enables successful correction of the vegetation effect, to the extent that the vegetation representation in the model is reliable. Iterative inversion of a physical model was performed for forest, corn, and soybean fields using airborne and ground-based snapshot observations [26], [27]. However, with spaceborne σ 0 observations the speckle noise can be large (0.5-0.7 dB or 13%-17% of the signal, in the case of Soil Moisture Active Passive mission (SMAP) [28]).…”

mentioning

confidence: 99%

Surface Soil Moisture Retrieval Using the L-Band Synthetic Aperture Radar Onboard the Soil Moisture Active–Passive Satellite and Evaluation at Core Validation Sites

Kim

Zyl

Johnson

et al. 2017

IEEE Trans. Geosci. Remote Sensing

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This paper evaluates the retrieval of soil moisture in the top 5-cm layer at 3-km spatial resolution using L-band dual-copolarized Soil Moisture Active-Passive (SMAP) synthetic aperture radar (SAR) data that mapped the globe every three days from mid-April to early July, 2015. Surface soil moisture retrievals using radar observations have been challenging in the past due to complicating factors of surface roughness and vegetation scattering. Here, physically based forward models of radar scattering for individual vegetation types are inverted using a time-series approach to retrieve soil moisture while correcting for the effects of static roughness and dynamic vegetation. Compared with the past studies in homogeneous field scales, this paper performs a stringent test with the satellite data in the presence of terrain slope, subpixel heterogeneity, and vegetation growth. The retrieval process also addresses any deficiencies in the forward model by removing any time-averaged bias between model and observations and by adjusting the strength of vegetation contributions. The retrievals are assessed at 14 core validation sites representing a wide range of global soil and vegetation conditions over grass, pasture, shrub, woody savanna, corn, wheat, and soybean fields. The predictions of the forward models used agree with SMAP measurements to within 0.5 dB unbiased-root-mean-square error (ubRMSE) and −0.05 dB (bias) for both copolarizations. Soil moisture retrievals have an accuracy of 0.052 m 3 /m 3 ubRMSE, −0.015 m 3 /m 3 bias, and a correlation of 0.50, compared to in situ measurements, thus meeting the accuracy target of 0.06 m 3 /m 3 ubRMSE. The successful retrieval demonstrates the feasibility of a physically based time series retrieval with L-band SAR data for characterizing soil moisture over diverse conditions of soil moisture, surface roughness, and vegetation. Kim et al.

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Potential of L-Band Radar for Retrieval of Canopy and Subcanopy Parameters of Boreal Forests

Cited by 23 publications

References 30 publications

Multi-Scale Evaluation of the SMAP Product Using Sparse In-Situ Network over a High Mountainous Watershed, Northwest China

Multi-Scale Evaluation of the SMAP Product Using Sparse In-Situ Network over a High Mountainous Watershed, Northwest China

The Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12): Prelaunch Calibration and Validation of the SMAP Soil Moisture Algorithms

Surface Soil Moisture Retrieval Using the L-Band Synthetic Aperture Radar Onboard the Soil Moisture Active–Passive Satellite and Evaluation at Core Validation Sites

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