Compared performances of SMOS-IC soil moisture and vegetation optical depth retrievals based on Tau-Omega and Two-Stream microwave emission models

Li, Xiaojun; Al-Yaari, Amen; Schwank, Mike; Fan, Lei; Frappart, Frédéric; Swenson, Jennifer J.; Wigneron, Jean‐Pierre

doi:10.1016/j.rse.2019.111502

Cited by 73 publications

(41 citation statements)

References 77 publications

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“…Both of the parameters b from the Kasten model and γ from the Hänel model indicate the strength of hygroscopicity. These parameters in Case I were less than the parameters for Case II (Case I: b = 0.33, γ = 0.307; Case II: b = 1.34, γ = 1.138) [57]. More intuitively, the color ratio in Case II (Figure 4l) was significantly higher than that in Case I (Figure 4f) within the same range of RH change.…”

Section: Lidar-estimated Hygroscopicitymentioning

confidence: 83%

Hygroscopicity of Different Types of Aerosol Particles: Case Studies Using Multi-Instrument Data in Megacity Beijing, China

Wang

et al. 2020

Remote Sensing

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Water uptake by aerosol particles alters its light-scattering characteristics significantly. However, the hygroscopicities of different aerosol particles are not the same due to their different chemical and physical properties. Such differences are explored by making use of extensive measurements concerning aerosol optical and microphysical properties made during a field experiment from December 2018 to March 2019 in Beijing. The aerosol hygroscopic growth was captured by the aerosol optical characteristics obtained from micropulse lidar, aerosol chemical composition, and aerosol particle size distribution information from ground monitoring, together with conventional meteorological measurements. Aerosol hygroscopicity behaves rather distinctly for mineral dust coarse-mode aerosol (Case I) and non-dust fine-mode aerosol (Case II) in terms of the hygroscopic enhancement factor, f β (RH, λ 532 ), calculated for the same humidity range. The two types of aerosols were identified by applying the polarization lidar photometer networking method (POLIPHON). The hygroscopicity for non-dust aerosol was much higher than that for dust conditions with the f β (RH, λ 532 ) being around 1.4 and 3.1, respectively, at the relative humidity of 86% for the two cases identified in this study. To study the effect of dust particles on the hygroscopicity of the overall atmospheric aerosol, the two types of aerosols were identified and separated by applying the polarization lidar photometer networking method in Case I. The hygroscopic enhancement factor of separated non-dust fine-mode particles in Case I had been significantly strengthened, getting closer to that of the total aerosol in Case II. These results were verified by the hygroscopicity parameter, κ (Case I non-dust particles: 0.357 ± 0.024; Case II total: 0.344 ± 0.026), based on the chemical components obtained by an aerosol chemical speciation instrument, both of which showed strong hygroscopicity. It was found that non-dust fine-mode aerosol contributes more during hygroscopic growth and that non-hygroscopic mineral dust aerosol may reduce the total hygroscopicity per unit volume in Beijing. , haze and dust aerosols were dominant at the site. Enhanced observations were thus conducted then to study the mixed state and morphological changes of different chemical components leading to different characteristics of atmospheric pollutant aerosol.

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Section: Lidar-estimated Hygroscopicitymentioning

confidence: 83%

Hygroscopicity of Different Types of Aerosol Particles: Case Studies Using Multi-Instrument Data in Megacity Beijing, China

Wang

et al. 2020

Remote Sensing

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“…S2). The importance of a variable can also be calculated in the scikit-learn module based on how much each variable decreases the weighted impurity, i.e., the sum of the number of splits across all trees (Louppe et al, 2013). Although the RF model is robust to correlated explanatory variables, the importance calculation could be biased if there is a strong collinearity between different variables.…”

Section: Random Forestmentioning

confidence: 99%

Mapping the yields of lignocellulosic bioenergy crops from observations at the global scale

Ciais

Stehfest

et al. 2020

Earth Syst. Sci. Data

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Abstract. Most scenarios from integrated assessment models (IAMs) that project greenhouse gas emissions include the use of bioenergy as a means to reduce CO2 emissions or even to achieve negative emissions (together with CCS – carbon capture and storage). The potential amount of CO2 that can be removed from the atmosphere depends, among others, on the yields of bioenergy crops, the land available to grow these crops and the efficiency with which CO2 produced by combustion is captured. While bioenergy crop yields can be simulated by models, estimates of the spatial distribution of bioenergy yields under current technology based on a large number of observations are currently lacking. In this study, a random-forest (RF) algorithm is used to upscale a bioenergy yield dataset of 3963 observations covering Miscanthus, switchgrass, eucalypt, poplar and willow using climatic and soil conditions as explanatory variables. The results are global yield maps of five important lignocellulosic bioenergy crops under current technology, climate and atmospheric CO2 conditions at a 0.5∘×0.5∘ spatial resolution. We also provide a combined “best bioenergy crop” yield map by selecting one of the five crop types with the highest yield in each of the grid cells, eucalypt and Miscanthus in most cases. The global median yield of the best crop is 16.3 t DM ha−1 yr−1 (DM – dry matter). High yields mainly occur in the Amazon region and southeastern Asia. We further compare our empirically derived maps with yield maps used in three IAMs and find that the median yields in our maps are > 50 % higher than those in the IAM maps. Our estimates of gridded bioenergy crop yields can be used to provide bioenergy yields for IAMs, to evaluate land surface models or to identify the most suitable lands for future bioenergy crop plantations. The 0.5∘×0.5∘ global maps for yields of different bioenergy crops and the best crop and for the best crop composition generated from this study can be download from https://doi.org/10.5281/zenodo.3274254 (Li, 2019).

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“…However, SPL3SMP_E SM retrievals show a dry bias over some regions of the Amazon and Congo basin, which is opposite for MT-DCA SM retrievals. This can be explained in part by the discrepancies between the sampling depth of the modeled ECMWF SM data (0-7 cm top soil layer) and the SMAP SM data (~0-5 cm top soil layer) [47,52]. With this in mind, the different soil moisture Bias patterns presented in Figure 3e,f should be interpreted carefully.…”

Section: Resultsmentioning

confidence: 98%

“…In our work we evaluated and inter-compared the performances of two SMAP SM retrievals against ECMWF modeled SM (global scale) and ground-based measurements (local scale) following three main rules: i) using a long period (i.e., April 2015-December 2017) of SM retrievals of each SMAP product as much as possible (until November 2019, MT-DCA was only updated to the end of 2017); ii) strictly using the same number of pixels (and thus of dates) between the two SMAP SM retrievals; iii) doing same data filtering, i.e., not recommended by the SMAP retrievals quality flag, SM values outside the range 0-0.6 m 3 /m 3 [46], and temporal series of SMAP data pairs (i.e., number of observations available for validation) lower than one month (~31) were filtered out [51]. Four metrics, which are widely used in the soil moisture community [45,47], were used to compare the SMAP SM retrievals with the reference data (i.e., modeled ECMWF and in situ measurements): (Pearson) correlation coefficient (R; Equation 1) to assess the performance of SMAP retrievals to capture the seasonal variations of the reference SMs, bias (m 3 /m 3 ; Equation 2) to measure the wetness or dryness of the SMAP SM compared to the reference SMs, root mean square difference (RMSD; m 3 /m 3 ; Equation 3), and the ubRMSD (m 3 /m 3 ; Equation 4) [18]. It should be noted that R and ubRMSD were considered as first-order criteria in comparison to Bias and RMSD, as the reference soil moisture datasets do not represent the value as "observed" by the SMAP measurement, considering the different "sampling" depths of simulated, retrieved and in-situ SM data.…”

Section: Methodsmentioning

confidence: 99%

“…Several additional datasets were used to interpret the results, including the International Geosphere-Biosphere Programme (IGBP) land cover classification scheme and MODIS leaf index area (LAI). Earlier studies have shown that the performance of the satellite-based SM products may vary as a function of vegetation density and land cover class [29,42,47]. Here, we used the IGBP land cover classification scheme and LAI data to account for the different land cover types and vegetation conditions when comparing the SM retrievals.…”

Section: Additional Datasetsmentioning

confidence: 99%

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Evaluation of Two SMAP Soil Moisture Retrievals Using Modeled- and Ground-Based Measurements

Bai

2019

Remote Sensing

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A comprehensive evaluation of the performance of satellite-based soil moisture (SM) retrievals is undoubtedly very important to improve its quality and evaluate its potential application in hydrology, climate, and natural disasters (drought, flood, etc.). Since the release of the SMAP (Soil Moisture Active Passive) mission data in April 2015, the associated SM retrieval algorithms have developed rapidly, and their improvement work is still in progress. However, some newly developed SM retrievals have not been fully assessed and inter-compared. One such product is the new multi-temporal dual-channel retrieval algorithm (MT-DCA) SM retrievals, which was recently retrieved using the so-called MT-DCA algorithm. To solve this, we aim to assess the MT-DCA SM retrievals along with the SMAP-enhanced level three SM products (SPL3SMP_E, version 2). More specifically, in this paper we evaluated and inter-compared the two SMAP SM retrievals with the ECMWF (European Centre for Medium-Range Weather Forecasts) modeled SM and ISMN (International Soil Moisture Network) in situ observations by applying four statistical scores: Pearson correlation coefficient (R), root mean square difference (RMSD), bias, and unbiased RMSD (ubRMSD). It was found that both SMAP SM retrievals can better capture the seasonal variations of ECMWF-modeled SM and ground-based measurements according to correlations, and MT-DCA SM was drier than SPL3SMP_E SM by ~0.018 m3/m3 on average on a global scale. With respect to the ISMN ground-based measurements, the performance of SPL3SMP_E SM compared better than the MT-DCA SM. The median ubRMSD of SPL3SMP_E SM and MT-DCA SM with ground measurements computed over all selected ISMN sites were 0.058 m3/m3 and 0.070 m3/m3, respectively.

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Compared performances of SMOS-IC soil moisture and vegetation optical depth retrievals based on Tau-Omega and Two-Stream microwave emission models

Cited by 73 publications

References 77 publications

Hygroscopicity of Different Types of Aerosol Particles: Case Studies Using Multi-Instrument Data in Megacity Beijing, China

Hygroscopicity of Different Types of Aerosol Particles: Case Studies Using Multi-Instrument Data in Megacity Beijing, China

Mapping the yields of lignocellulosic bioenergy crops from observations at the global scale

Evaluation of Two SMAP Soil Moisture Retrievals Using Modeled- and Ground-Based Measurements

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