Evaluation and Improvement of SMOS and SMAP Soil Moisture Products for Soils with High Organic Matter over a Forested Area in Northeast China

Jin, Mengjie; Zheng, Xingming; Jiang, Tao; Li, Xiaofeng; Li, Xiaojie; Zhao, Kai

doi:10.3390/rs9040387

Cited by 27 publications

(28 citation statements)

References 38 publications

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“…To meet the science requirements, one of its post-launch objectives is to validate the accuracy of the science data products [14]. However, so far, only a few validations of the SMAP products have been conducted, and almost all of them have been carried out at the core validation sites (CVS) [12,[16][17][18][19][20][21][22]. Because observations from the CVS have been used to refine and validate retrieval algorithms, it is important to validate the SMAP products over other regions beyond the CVS [12,14,21,23].…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…4) using SMAP TB measurements (blue lines), in situ soil moisture (SM) content (black lines), and vegetation opacity (thick green dots) from MODIS; and dielectric mixing models (Park et al, 2017) with wilting point (Eq. [1]) functions and organic matter (OM) input from the SoilGrid1km database (Hengl et al, 2014) (P o r), with wilting point (Jin et al, 2017) and porosity (Wösten et al, 1999) functions and OM input from the Harmonized World Soil Database (FAO, 2012) (P o h), with wilting point and porosity classified by USDA soil classification (P), Wang and Schmugge (1980) (W), Dobson et al (1985) (D), and Mironov et al (2009) (M) with OM input from the Harmonized World Soil Database (FAO, 2012) (M o h) and the SoilGrid1km database (Hengl et al, 2014) (M o r).…”

Section: Resultsmentioning

confidence: 99%

“…Because of these microscopic and macroscopic mechanisms, an increase in OM may increase both the wilting point and porosity. This is supported by several pedotransfer functions, showing that the wilting point (Gupta and Larson, 1979; Jin et al, 2017; Rawls et al, 1982) and dry porosity (Saxton and Rawls, 2006; Tóth et al, 2015; Vereecken et al, 1989; Wösten et al, 1999) are higher for soils with higher OM content. If this impact of OM is incorporated into a DMM, the relationship between soil moisture and permittivity, and thus the relationship between soil moisture and brightness temperature (TB), will change accordingly.…”

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confidence: 83%

“…2). First, we apply a linear‐type model for the wilting point (wp), similar to Jin's model (Jin et al, 2017):

wp = 0.02982 + 0.089 v_{clay} + 0.00786 OM

where v clay is the volumetric mixing ratio of clay (cm 3 cm −3 ), and OM is the organic matter content (%). Following this, for the calculation of soil porosity ( p ), the following pedotransfer function (Tóth et al, 2015) was applied in this study:

\begin{array}{l} left & p & = & 0.6819 - \frac{0.06480}{OC+1} - 0.11900 {BD}^{2} - 0.02668 \\ left & + 0.1489 v_{clay} + 0.08031 v_{silt} + \frac{0.02321}{(OC+1) {BD}^{2}} \\ left & + 0.01908 {BD}^{2} - 0.11090 v_{clay} - 0.2315 v_{silt} v_{clay} \\ left & - 0.01197 v_{silt} {BD}^{2} - 0.01068 v_{clay} {BD}^{2} \end{array}

where v silt is the volumetric mixing ratio of soil silt (cm 3 cm −3 ), BD is the soil bulk density (g cm −3 ), and OC is the soil organic C, defined as the OM divided by 1.72 (Stenberg et al, 2010) (%).…”

Section: Methodsmentioning

confidence: 99%

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A Dielectric Mixing Model Accounting for Soil Organic Matter

Park

Montzka

Jagdhuber

et al. 2019

Vadose Zone Journal

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In the proposed model, the wilting point and porosity are a function of organic matter. In organic‐rich soil, the model improves accuracy of the microwave radiative transfer model. The model is applicable for both portable and satellite soil moisture sensors. Most dielectric mixing models have been developed for mineral soils without extensive consideration of organic matter (OM). In addition, when used for in situ measurement, most of these models focus only on the real part of the effective dielectric constant without the corresponding imaginary part. Organic matter fractions in soils are found globally (57%), with an especially significant amount in the boreal region (17%). Without proper consideration of OM in dielectric mixing models and subsequent microwave radiative transfer modeling, brightness temperature (TB) calculations may be erroneous. This would lead to uncertainties in the estimation of higher level products, such as soil moisture retrievals from portable soil moisture sensors (e.g., time‐domain reflectometers) or passive microwave sensors onboard the Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Advanced Microwave Scanning Radiometer (AMSR2) satellites. We incorporated OM into a dielectric mixing model by adjusting the wilting point and porosity according to the OM content, i.e., the effective soil dielectric constant decreases with higher OM due to a decrease in the fraction of free water and an increase in bound water. With the proposed soil parameters in the dielectric mixing model, high levels of OM increase the TB for a specific soil moisture by decreasing the microwave effective dielectric constant. The simulated TB better reproduced SMAP‐observed TB (11% in RMSE) through the improvement of the effective dielectric constant (40% reduction in RMSE). We anticipate that the application of our approach can improve microwave‐based surface soil moisture retrievals in areas with high OM.

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“…2018, 10, 304 3 of 18 as a higher potential for water infiltration because of the macropores created by the organic matter. For this reason, dielectric mixing models were recently also developed for organic soil layers [19][20][21][22]. The empirical model proposed by Bircher et al [19] is currently being implemented in the SMOS soil moisture retrieval algorithm to replace the Mironov et al [17] model wherever organic soil surface layers are present.…”

Section: Introductionmentioning

confidence: 99%

Passive L-Band Microwave Remote Sensing of Organic Soil Surface Layers: A Tower-Based Experiment

et al. 2018

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Organic soils play a key role in global warming because they store large amount of soil carbon which might be degraded with changing soil temperatures or soil water contents. There is thus a strong need to monitor these soils and, in particular, their hydrological characteristics using, for instance, space-borne L-band brightness temperature observations. However, there are still open issues with respect to soil moisture retrieval techniques over organic soils. In view of this, organic soil blocks with their vegetation cover were collected from a heathland in the Skjern River catchment in western Denmark and then transported to a remote sensing field laboratory in Germany where their structure was reconstituted. The controlled conditions at this field laboratory made it possible to perform tower-based L-band radiometer measurements of the soils over a period of two months. Brightness temperature data were inverted using a radiative transfer (RT) model for estimating the time variations in the soil dielectric permittivity and the vegetation optical depth. In addition, the effective vegetation scattering albedo parameter of the RT model was retrieved based on a two-step inversion approach. The remote estimations of the dielectric permittivity were compared to in situ measurements. The results indicated that the radiometer-derived dielectric permittivities were significantly correlated with the in situ measurements, but their values were systematically lower compared to the in situ ones. This could be explained by the difference between the operating frequency of the L-band radiometer (1.4 GHz) and that of the in situ sensors (70 MHz). The effective vegetation scattering albedo parameter was found to be polarization dependent. While the scattering effect within the vegetation could be neglected at horizontal polarization, it was found to be important at vertical polarization. The vegetation optical depth estimated values over time oscillated between 0.10 and 0.19 with a mean value of 0.13. This study provides further insights into the characterization of the L-band brightness temperature signatures of organic soil surface layers and, in particular, into the parametrization of the RT model for these specific soils. Therefore, the results of this study are expected to improve the performance of space-borne remote sensing soil moisture products over areas dominated by organic soils.

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Evaluation and Improvement of SMOS and SMAP Soil Moisture Products for Soils with High Organic Matter over a Forested Area in Northeast China

Cited by 27 publications

References 38 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

A Dielectric Mixing Model Accounting for Soil Organic Matter

Passive L-Band Microwave Remote Sensing of Organic Soil Surface Layers: A Tower-Based Experiment

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