FRM4SM WORK PACKAGE 5 TECHNICAL NOTE 2
SMOS VALIDATION IN ORGANIC SOIL
SO-TN-CB-GS-0112 REQ-16: R&D CASE STUDY 2

François Gibon, Arnaud Mialon, Philippe Richaume, Yann H. Kerr, Ali

Mahmoodi, Nemesio Rodríguez-Fernández, Roberto Sabia

► To cite this version:

François Gibon, Arnaud Mialon, Philippe Richaume, Yann H. Kerr, Ali Mahmoodi, et al.. FRM4SM WORK PACKAGE 5 TECHNICAL NOTE 2 SMOS VALIDATION IN ORGANIC SOIL SO-TN-CB-GS-0112 REQ-16: R&D CASE STUDY 2. SO-TN-CB-GS-0112, cesbio. 2023. hal-04840062

HAL Id: hal-04840062

https://hal.science/hal-04840062v1

Submitted on 16 Dec 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Abstract

Soil characteristics, as the texture or density, influence the emission at L-band and consequently the validation of satellite based soil moisture data. Among these parameters, the proportion of Soil Organic Carbon (SOC), is often used as a proxy for Soil Organic Matter (SOM) because it is easier to quantify, has different impacts on the whole validation chain, for example the dielectric constant sensitivity to the SOC content in the satellite retrieval or the needs for a specific calibration function for probes installed in soil with high SOC content. The aim of this document is to investigate the validation of the satellite SMOS soil moisture product in high organic soil context. The results show a slight worsening of the performances with the increase of the SOC, however further analyses are needed to determine the uncertainty magnitude between SMOS and the reference.

**Keywords: SMOS validation, organic soil, probes calibration**
## Document approval

<table><thead><tr><th></th><th>Role</th><th>Name</th><th>Date</th><th>Signature</th></tr></thead><tbody><tr><td rowspan="5">Written by</td><td rowspan="5">Project engineer</td><td>F. Gibon</td><td>/ /22</td><td rowspan="5"></td></tr><tr><td>A. Mialon</td><td>/ /22</td></tr><tr><td>Y. Kerr</td><td>/ /22</td></tr><tr><td>N. Rodriguez F.</td><td>/ /22</td></tr><tr><td>P. Richaume</td><td>/ /22</td></tr><tr><td>Approved by</td><td>Lead investigator</td><td>A. Mialon</td><td>/ /22</td><td></td></tr><tr><td>Approved by</td><td>Technical officer</td><td>R. Sabia</td><td>/ /22</td><td></td></tr></tbody></table>

## Distribution list

<table><thead><tr><th>Name</th><th>Affil.</th><th>Signature</th></tr></thead><tbody><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr></tbody></table>

<table><thead><tr><th>Name</th><th>Affil.</th><th>Signature</th></tr></thead><tbody><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr></tbody></table>

## Auxiliary documents related to this project

<table><tr><td>ATBD L2</td><td>Kerr, Y.H. and Richaume, P., and Waldteufel, P. and Ferrazzoli, P. and Wigneron, J.P. and Schwank, M. and Rautiainen, K., Algorithm theoretical basis document (ATBD) for the SMOS level 2 soil moisture processor, Technical Report TN-ESL-SM-GS-0001-4b SM-ESL (CBSA), 145p, 2020</td></tr><tr><td>FRM4SM detailed proposal</td><td>FRM4SM - Fiducial Reference Measurements for Soil Moisture Detailed Proposal, Vienna, Austria, AWST TUWIEN CESBIO, January 2021</td></tr><tr><td>MRD</td><td>EEOM-SMOS-MRD, Mission Objectives and Scientific Requirements of the Soil Moisture and Ocean Salinity (SMOS) Mission, Version 5, issue 1.0 040701, https://esamultimedia.esa.int/docs/SMOS_MRD_V5.pdf</td></tr></table>
# Purpose and scope of document

Fiducial Reference Measurement for Soil Moisture (FRM4SM) is a two-year ESA project (May 2021-April 2023) with the main goal to provide confidence information in satellite-based soil moisture retrievals all along the mission duration towards a maximum Return On Investment (ROI).

## ESA FRM4SM PROJECT CONTRACT N°4000135204/21//I-BG

The present document corresponds to the REQ-16 of the FRM4SM detailed proposal, related to the Case Study 2. It answers, in part, to the different objectives of the Task 5 led by the Working Group 5 which are:

* OBJ-11 – Three SMOS R&D case studies

* OBJ-12 – Sub-footprint variability and vertical stratification influence

* OBJ-13 – Temporal aliasing

* OBJ-14 – In-situ-driven strategy versus satellite-driven strategy

* OBJ-15 – Committed areas

# Document status

<table><thead><tr><td>Issue</td><td>Date</td><td>Details</td><td>Editor</td></tr></thead><tbody><tr><td>0.0</td><td>1 Sept 2022</td><td>Creation of draft and structure within document</td><td>François Gibon<br>Arnaud Mialon</td></tr><tr><td>0.1</td><td>XX</td><td>XX</td><td>XX</td></tr><tr><td>0.2</td><td>XX</td><td>XX</td><td>XX</td></tr><tr><td>0.3</td><td>XX</td><td>XX</td><td>XX</td></tr></tbody></table>

If further corrections are required, please contact François Gibon or Arnaud Mialon (francois.gibon@univ-tlse3.fr, arnaud.mialon@univ-tlse3.fr).
# Contents

## 1 Introduction 6

1.1 Why a focus on soil organic matter content ? 6

1.1.1 Some definitions of Soil Organic Matter (SOM) and Soil Organic Carbon (SOC) 6

1.1.2 The interactions between SOC and soil moisture in the context of global warming 6

1.2 How the SOC content is considered in SMOS soil moisture retrieval 7

1.3 Satellite based soil moisture validation issues in high SOC content 8

1.3.1 Which references for the validation ? 8

1.3.2 Probes reliability in this SOC context ? 8

1.4 Objectives 9

## 2 Data 9

2.1 Soil moisture 9

2.1.1 SMOS L2 Soil Moisture v700 9

2.1.2 in-situ measurements 9

2.2 SOC databases 9

## 3 Methods 10

3.1 Validation process 10

3.2 Modelling approach 10

## 4 Results & discussions 10

4.1 SOC content impact on the soil moisture value 10

4.2 SMOS performances sensitivity to SOC content 12

4.3 SMOS dielectric model choice sensitivity on the soil moisture retrieved 16

4.4 Example of in-situ probe in high SOC context of peatlands 16

## 5 Conclusions & perspectives 17

## References 18
Acronyms

<table><tr><td>- AGB</td><td>Above Ground Biomass</td></tr><tr><td>- BT</td><td>Brightness Temperature</td></tr><tr><td>- CATDS</td><td>Centre Aval de Traitement des Données SMOS</td></tr><tr><td>- ECMWF</td><td>European Center for Medium-Range Weather Forecasts</td></tr><tr><td>- ESA</td><td>European Space Agency</td></tr><tr><td>- ESL</td><td>Expert Support Laboratory</td></tr><tr><td>- FAO</td><td>Food Agricultural Organization</td></tr><tr><td>- FRM4SM</td><td>Fiducial References Measurements for Soil Moisture</td></tr><tr><td>- HWSD</td><td>Harmonized World Soil Database</td></tr><tr><td>- ISMN</td><td>International Soil Moisture Network</td></tr><tr><td>- MEANWEF</td><td>MEANWEF weighted average function of all the WEFs</td></tr><tr><td>- MRD</td><td>Mission Requirements Document</td></tr><tr><td>- QA4SM</td><td>Quality Assurance for Soil Moisture</td></tr><tr><td>- RFI</td><td>Radio Frequency Interference</td></tr><tr><td>- RSOM</td><td>Ratio of Soil Organic Matter</td></tr><tr><td>- RTM</td><td>Radiative Transfer Model</td></tr><tr><td>- SM</td><td>Soil Moisture</td></tr><tr><td>- SMOS</td><td>Soil Moisture and Ocean Salinity</td></tr><tr><td>- SOC</td><td>Soil Organic Carbon</td></tr><tr><td>- SOM</td><td>Soil Organic Matter</td></tr><tr><td>- VOD</td><td>Vegetation Optical Depth</td></tr><tr><td>- VWC</td><td>Vegetation Water Content</td></tr><tr><td>- WA</td><td>Working Area</td></tr><tr><td>- WEF</td><td>WEighted Function</td></tr></table>
# 1 Introduction

## 1.1 Why a focus on soil organic matter content ?

### 1.1.1 Some definitions of Soil Organic Matter (SOM) and Soil Organic Carbon (SOC)

Soil composition is often defined with a certain proportion of air, water, mineral and organic matter. These proportions are variable, and depends on soil characteristics, such as texture or its density, and the landcover. The organic part, called Soil Organic Matter (SOM), is a mixture of components of different forms and types of biotic origin, and can be broadly classified into living and non-living (8). This SOM proportion plays a significant role in the global water and carbon cycle (17). In fact, the potential of water retention is particularly high for rich SOM soil due to micro-physical phenomena.

In terms of definitions, total organic carbon, soil organic carbon and organic carbon represent the same information. Organic matter differs from the total organic as it not only includes the carbon but also all the organic components (hydrogen, oxygen, nitrogen...). However, as it is complex to estimate from a soil sample, SOC is usually used as a SOM proxy, considering the relation : $SOM = SOC \times 1.72$ (1).

### 1.1.2 The interactions between SOC and soil moisture in the context of global warming

The SOC content is involved in many land processes (biogeochemistry, climate change mitigation...) that gathered by the FAO through its implication in 5 of the 12 Sustainable Development Goals (SDG), see Figure 1 (adapted from (7)).

<figure><img src="image_2.png" /><figcaption>Figure 1: SOC involvment in the Sustainable Development Goals-SDG (7)</figcaption></figure>

Its role in the soil fertility and productivity is included in the SDG 2 "Zero hunger" and 3 "Good health and well-being", its role on the water storage on the SDG 3 and 6 "Clean water and sanitation". The SDG 15 "Life on land" considers the SOC as a factor which maintains the soil biodiversity. The SDG 13 "Climate action" considers the SOC as a factor for the soil carbon sequestration, however, particularly sensitive to climate change and is exposed to intense thawing (16). In (14), the authors estimate that, 1014 Pg (uncertainties of + 194 à -175 Pg) of carbon are stored in the permafrost soils. With the increase of temperature due to climate change, the perspective of thawing of permafrost will be associated with a significant release of green house gases. It is of importance to measure soil moisture at high latitudes, and to evaluate the performance of SMOS SM in the region of permafrost, and for rich organic soil.
## 1.2 How the SOC content is considered in SMOS soil moisture retrieval

SMOS level 2 retrieval uses two dielectric models to compute the dielectric constant of a soil depending on its composition. Indeed, a full mineral soil is characterized with the Mironov dielectric model, whereas a soil with a high organic content is modeled with the Bircher model (4). The conditions in-between are characterized by a mix of these two models, as follows:

$$ \epsilon_{total} = RSOM \times \epsilon_{Bircher} + (1 - RSOM) \times \epsilon_{Mironov} $$

with RSOM being the fraction of organic soil within the SMOS footprint. It is obtained using the following strategy: i/ an area of 35x35 of 4-km cell is defined around each SMOS node; ii/ for each 4 km cell, the number of 250x250m SoilGrid250m cell higher than 102 g/kg (*) is classified as organic (proportion from 0 to 1 see map Figure 2 from (3)) iii/ the antenna pattern is then applied on this 35x35 cell area (see Figure 3), to compute the RSOM. This is a summary of the process and a more detailed explanation is reported in the SMOS Algorithm Theoretical Basis Document (ATBD, (13)).

<figure><img src="image_3.png" /><figcaption>Figure 2: The blue mask correspond to the point with the SOC>102 g/kg map from (3).</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Figure 3: On the left, optic image of the example area DGG189732; on the right, the "organic soil" proportion, for each 4x4km DFFG pixels, corresponding to the fraction of 250x250m pixels with a SOC over 102 g/kg (SoilGridv2017 database)</figcaption></figure>

<footnote>(*) This threshold was established with SoilGridv2017 used in the SMOS v700, a new threshold is defined for SoilGridv2020 for the next release SMOS v720.</footnote>
Bircher et al. (5) noticed that the SMOS SM were biased at the HOBE site in Denmark, due to the high organic content of the site. This result motivated the ESA project HighLat with the focus on the aim to define a more suitable dielectric constant for such soils. During this project, Bircher et al. (4) also addressed the issue of the calibration function of the soil moisture probe. Indeed, most manufacturer provide two functions to transform the sensor output into soil moisture: one for mineral soil, one for organic soil. It has been shown that the calibration function for organic soil is not always optimal, and it is recommended for users to perform their own calibration. (4).

## 1.3 Satellite based soil moisture validation issues in high SOC content

### 1.3.1 Which references for the validation ?

In order to evaluate the SMOS performance in the context of such particular soil, we are using the ISMN network as the reference. However, as shown in Figure 4, there are not many probes in high SOC areas (the green part of the map). This is an issue for the validation of satellite derived soil moisture in such conditions.

There are different reasons to this scarcity. The map shows that the areas with high SOC are mainly in the high latitude, which is not easy to access (no road, no trails, complex landscape, snow risk) to install the instruments and to maintain it. The presence of snow can be another issue as no SMOS level 2 is retrieved when snow is detected on the ground (13).

<figure><img src="image_5.png" /><figcaption>Figure 4: The background map is the SOC content value (OpenLandMap USDA) and the black dots correspond to the ISMN stations.</figcaption></figure>

### 1.3.2 Probes reliability in this SOC context ?

The second potential issue, with the small amount of in-situ sites, is the quality of those references. In fact, the density of the soil decreases with increasing SOC, meaning that, to some extent, it could affect the contact between the probe pins and the soil. Moreover, the probes need a specific calibration function for organic soil. The manufacturer usually provides such information, but it is often recommended to process its own calibration function, as it depends strongly on the SOC content of the soil that is being monitored (4).
## 1.4 Objectives

Considering the importance of soil moisture in rich organic soil, the objectives of this document are i) to investigate the SMOS performance in high SOC content and ii) to discuss the caveats and potential improvements. The following structure is proposed to assess these objectives: i) investigate the relation between the SOC values and the soil moisture, ii) assess the SMOS performance on high SOC content, iii) evaluate the different dielectric models and their sensitivity and iv) discuss the caveats of in-situ experiment in high SOC content.

# 2 Data

## 2.1 Soil moisture

### 2.1.1 SMOS L2 Soil Moisture v700

The analysis is based on the SMOS L2SMv700 which is detailed in the ATBD (12) and in the technical note DT5-1 (9). This v700 version uses SoilGridv2017 (RSOM threshold >102 g/kg), however the v720 version is based on the SoilGridv2020 (RSOM threshold >68 g/kg). This is why the two thresholds are assessed in Figure 12.

### 2.1.2 in-situ measurements

To complete our analysis, the following soil moisture references are used in this study are the following:

* ISMN: The ISMN database is detailed in (9).

* Jura peatland experiment: Experiments described Section 4.4.

* SMAPVEX: The data measured during the SMAPVEX22 campaign that was hold in Massachusetts, focused on the soil moisture measurement in the context of forested areas, with high soil organic carbon content. The data are, however, not yet available at the time this document is written. We mentioned this dataset as it should be considered for further investigations. Contact: Michael Cosh

* Alaska Fairbank University database: Not available on time to be included in the present analysis, the dataset should be considered for further investigations. Contact: Helene Genet

## 2.2 SOC databases

The SOC data used here was extracted using the Google Earth Engine code available here: https://code.earthengine.google.com/?scriptPath=users%2Ffgneofg%2Fdefault%3Asoc_soilgris_ismn

* SoilGridv2020: SoilGrid is a system for global digital soil mapping that uses over 230 000 soil profiles observations from the WoSIS database and a series of environmental covariates. Covariates were selected from a pool of over 400 environmental layers from Earth observation derived products and other environmental information including climate, land cover and terrain morphology (15).

* HiHydrosoil: Based on SoilGridv2020, the field *orcm* of HiHydrosoil is computed using the soc times 1.721, assuming that organic matter has 58% organic carbon. See (10).

* OpenLandMapUSDA: Based on machine learning predictions from global compilation of soil profiles and samples (11).
# 3 Methods

## 3.1 Validation process

The method of validation is similar to the one described in the Technical Note 1 DT5-1 (9).

## 3.2 Modelling approach

One can consider the model used in the SMOS retrieval chain, referred to as the breadboards, to analyze the dielectric constant impact on the SM retrieval. This exercise just started by the time we wrote this technical note. No significant results can be presented, but we mention here this analysis for future studies. The principle is to define/configure a soil with various soil organic content, to model the corresponding TB using 1) the Mironov model, 2) the Bircher model, 3) a mix of the two, and to evaluate their impact in terms of TB by comparing the modeled TB to real SMOS TB.

# 4 Results & discussions

## 4.1 SOC content impact on the soil moisture value

The aim of this section is to investigate the relation between the SOC and the soil moisture. In fact, Yang et al. (17) showed a global soil moisture increase with the SOC. Based on the analyses of the ISMN probes (2389 stations), we: i) compute the mean and std of every probe, ii) extract the SoilGridv2020 SOC value for each probe and iii) look at the relationship between the SM and the SOC.

<figure><img src="image_6.png" /><figcaption>Figure 5: Average of soil moisture measured by the probes at the in situ sites</figcaption></figure>

On the map Figure 5 the average soil moisture of each of the stations were computed. The result is displayed on the histogram with: $\mu = 0.21 \text{ m}^3\cdot\text{m}^{-3}$ and $\sigma = 0.12 \text{ m}^3\cdot\text{m}^{-3}$. The points in red are dryer than the blue ones. We can identify the temperate climate with the blue points on the west of the Europe, East of the US and China. The regions as the West of the US, West Africa, and Australia have a more arid climate with the redder points. The points in Alaska probably suffer
from the snow presence, which reduces the soil moisture during the winter period but also hampers the retrieval of SM from SMOS observations.

The map Figure 6 shows the standard deviation of the stations. The result is also represented as the histogram with: $\mu$ = 0.087 m$^3$.m$^{-3}$ and $\sigma$ = 0.056 m$^3$.m$^{-3}$.

<figure><img src="image_7.png" /><figcaption>Figure 6: Standard deviation of soil moisture measured by the probes at the in situ sites</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Figure 7: SOC content at the ISMN probes (SoilGridv2020) locations, and its distribution represented by the histogram</figcaption></figure>
In the map Figure 7, the SOC value for each of the sites was extracted from the SoilGridv2020 database. The result is displayed on the histogram with: $\mu$ = 66.28 g/kg and $\sigma$ = 58.68 g/kg.

In Figure 8, the scatter plot presents the SOC as a function of the average soil moisture value (left panel) and the standard deviation (on the right panel).

There is a slight trend towards an increase of the SOC with the SM and with its standard deviation (std), even though we observe a large dispersion/scatter. One can also notice that some values of mean SM are really high, with an average of $\ge$ 0.8 m$^3$.m$^{-3}$.

<figure><img src="image_9.png" /><figcaption>Figure 8: Left: mean soil moisture as a function of SOC; Right: standard deviation of soil moisture as a function of SOC, the color of the dots correspond to the point density.</figcaption></figure>

## 4.2 SMOS performances sensitivity to SOC content

In this section, the scores performed in the TN1 are analyzed regarding: i) the RSOM computed in the SMOS retrieval ii) the SOC value at the probe scale for different SOC/SOM database.

In Figure 9, the correlation (top left), RMSD (top right), bias (bottom left) and ubRMSD (bottom right) are represented as a function of the RSOM computed for each SMOS nodes. The figures do not show clear trends. One potential reason for this is because the organic fraction RSOM is based on a binary mask (0, 1) set if the SOC is higher than 102 g/kg, consequently it does not allow to represent the variability of SOC. The density of points are very unequal and concentrated around the 0 RSOM. The small amount of point above RSOM 0 suggest that the model of Bircher is not strongly included in the $\epsilon_{total}$ for the DGGs chosen here for the validation.
<figure><img src="image_10.png" /><figcaption>Figure 9: Scores (R, Bias, RMSD, ubRMSD) as a function of the fraction of SMOS footprints that is identified as organic soil (RSOM).</figcaption></figure>

In Figure 10, the correlation, bias, RMSD and ubRMSD are represented as a function of SOC values extracted from the SoilGridv2020 dataset at each in-situ probes position. The correlation cloud spread is globally increasing with the SOC, whereas there is no clear trend for the bias. However, the scores for the RMSD and ubRMSD seem to be degraded with the increase of SOC.
<figure><img src="image_11.png" /><figcaption>Figure 10: Scores (R, Bias, RMSD, ubRMSD) as a function of the SOC content at each in situ sites, obtained from SoilGridv2020 SOC content for each probes</figcaption></figure>

In Figure 11, the correlation, bias, RMSD and ubRMSD are represented as a function of the SOC value extracted from the different database for each probe: SoilGridv2020 in the first column (from the left), HiHydrosoilv2.0 in the second one and OpenLandMapUSDA in the third one. Globally, there is a slight trend towards an increase of the SOC with the worsening of the validation scores, even though we observe an increase of the dispersion.

In the table Figure 12, the scores are analyzed for the points with high or low SOM and SOC. For the first case, the limit of 30% was chosen as a threshold to define a soil of "organic soil" (6). Consequently, the scores have been classified with the probes having less/more than 30% SOM. Probes in high SOM shows a validation score worsening of 0.27 in correlation, 0.05 m³.m⁻³ in RMSD, 0.023 m³.m⁻³ in ubRMSD and a difference of bias of 0.108 m³.m⁻³. Those differences show that the SMOS performances for the sites having less than 30% of SOM at the probe scales are better than in high organic soil.

For the second case, the limit of 102 g/kg was chosen to be consistent with the threshold used in the RSOM computing within the SMOS retrieval. It shows a worsening of 0.19 in correlation, 0.03 m³.m⁻³ in RMSD, 0.018 m³.m⁻³ in ubRMSD and a difference of bias of 0.030 m³.m⁻³. These differences indicate that SMOS performs better compared with probes installed in soil with low organic soil (<102 g/kg). The same trend is shown with the RSOM threshold of 68 g/kg considered in the L2v720.
<figure><img src="image_12.png" /><figcaption>Figure 11: SMOS scores (R, RMSD, bias, ubRMSD) as a function of different SOC/SOM database, from left to right: SoilGridv2020, HiHydrosoil and OpenLandMap USDA</figcaption></figure>

<table><thead><tr><th></th><th>ALL</th><th>SOM&lt;30%</th><th>SOM&gt;30%</th><th>SOC&lt;120g/kg</th><th>SOC&gt;120g/kg</th><th>SOC&lt;68g/kg*</th><th>SOC&gt;68g/kg*</th></tr></thead><tbody><tr><th>R</th><td>μ=0.60<br>σ=0.21</td><td>μ=0.61<br>σ=0.20</td><td>μ=0.34<br>σ=0.19</td><td>μ=0.62<br>σ=0.20</td><td>μ=0.41<br>σ=0.20</td><td>μ=0.63<br>σ=0.20</td><td>μ=0.51<br>σ=0.21</td></tr><tr><th>RMSD<br>m<sup>2</sup>.m<sup>-3</sup></th><td>μ=0.11<br>σ=0.052</td><td>μ=0.11<br>σ=0.050</td><td>μ=0.16<br>σ=0.063</td><td>μ=0.11<br>σ=0.050</td><td>μ=0.14<br>σ=0.060</td><td>μ=0.10<br>σ=0.046</td><td>μ=0.13<br>σ=0.062</td></tr><tr><th>ubRMSD<br>m<sup>2</sup>.m<sup>-3</sup></th><td>μ=0.073<br>σ=0.024</td><td>μ=0.072<br>σ=0.023</td><td>μ=0.095<br>σ=0.022</td><td>μ=0.071<br>σ=0.023</td><td>μ=0.089<br>σ=0.021</td><td>μ=0.069<br>σ=0.023</td><td>μ=0.082<br>σ=0.023</td></tr><tr><th>Bias<br>m<sup>2</sup>.m<sup>-3</sup></th><td>μ=-0.034<br>σ=0.090</td><td>μ=-0.040<br>σ=0.084</td><td>μ=0.068<br>σ=0.12</td><td>μ=-0.039<br>σ=0.083</td><td>μ=0.008<br>σ=0.12</td><td>μ=-0.039<br>σ=0.079</td><td>μ=-0.022<br>σ=0.11</td></tr></tbody></table>

* threshold used with SoilGrid 2020 in the L2v720

<caption>Figure 12: SMOS performances (R, bias, RMSD, ubRMSD) for various conditions of SOC and SOM content (low/high SOC conditions considering different thresholds).</caption>
## 4.3 SMOS dielectric model choice sensitivity on the soil moisture retrieved

In order to assess and improve the SOC consideration in the SMOS retrieval, two exercises can be suggested:

1. Relate the RSOM value (fraction of pixel having an organic content over a certain threshold) to the SOC value. To do so, a sensitivity analysis could be done by changing the SOC threshold on a SMOS Working Area and to compute the fraction of SMOS footprint classified as organic (RSOM).

2. Model the different soil moisture retrievals using varying RSOM values on a DGG point with a probe near to, and so to look at the sensitivity of the validation scores to the RSOM.

Finally, in both cases, the scores could be investigated and related to the absolute SOC value, to evaluate the sensitivity of SMOS retrieval model to the RSOM. It implies to update the auxiliary files (AUX_DFFG) used in the Level 2 processor.

Another easy-to-run test, to bypass the RSOM, and the Level 2 context, is to use the breadboards to derive the soil moisture with either the Mironov or the Bircher model and to compare the validation scores. The aim is to relate the performance of each models to the SOC content.

## 4.4 Example of in-situ probe in high SOC context of peatlands

The aim of this section is to illustrate the potential issues using in-situ probes in high SOC context. An experimental site was set up to monitor the soil moisture in a peatland context. The study area is located in Jura mountains, in France, see Figure 13.

<figure><img src="image_13.png" /><figcaption>Figure 13: Jura peatland experiment, installed in the peatland of Les Rousses lake in France.</figcaption></figure>

The probes used here are the TEROS12 from MeterGroup (2) and the ZL6 datalogger. These probes allow us to monitor the soil moisture as well as the soil temperatures and the dielectric constant. Four probes were installed, two at a 5 cm depth and two at a 10 cm depth.

In the Figure 13, one can see the potential uncertainty on the installation of the probes regarding the very low soil density (right panels). In fact, the contact between the probes pins and the soil is not optimal.

In the top Figure 14 the RAW values of the probes (what is directly measures by the probes) are displayed, and in the bottom figure, the soil moisture value after conversion of these raw values by the calibration function given by the manufacturer. The calibration function for the mineral is presented in equation 1 whereas the specific function for the "organic" soil is presented in equation 2, as suggested by the Teros Manual (2). The important differences between these 2 calibration laws are represented by the two time series in Figure 14, as the blue and the red lines.
The strong drop in July is the consequence of a severe drought that hit Western Europe during summer 2022. The bias between the two different calibration functions is 0.071 m³.m⁻³. This value is higher than the average bias of 0.068 m³.m⁻³ found between SMOS SM and the in-situ probes in "organic soils", see Figure 12. Moreover, the manufacturer is providing an accuracy of 5% in organic soil if no dedicated calibration is processed (2). This value had to be discussed in further studies as it is of the same order as the RMSD of SMOS over the whole ISMN, μ=0.11 m³.m⁻³ σ=0.052 m³.m⁻³ (see the Figure 12).

$$ \theta_{mineral} = 3.879 \times 10^{-4} \times RAW - 0.6956 \quad (1) $$

$$ \theta_{organic} = 6.771 \times 10^{-10} \times RAW^3 - 5.105 \times 10^{-6} \times RAW^2 + 1.302 \times 10^{-2} \times RAW - 10.848 \quad (2) $$

<figure><img src="image_14.png" /><figcaption>Figure 14: Top: RAW outputs from the probes, Bottom: RAW output converted in soil moisture by the two different calibration function</figcaption></figure>

# 5 Conclusions & perspectives

This technical note provides a first glance at SMOS SM performances for soils having a high organic content. The first part of our analysis focused on the soil moisture acquired by the probes. The soil moisture (average and standard deviation) from the in-situ probes does not show a clear trend with the SOC values. One explanation could be the quality of the calibrations and the empty gap within the time series (which can notably appear under snow presence).

The second part concerns the SMOS performances and their sensitivity to the SOC. No clear relationship is found using the SMOS RSOM, which does not represent the SOC absolute value, but is rather the proportion of soil classified as "organic", i.e., having a high SOC. However, the analysis regarding the SOC value from SoilGridv2020 at the probe scale show a slight trend of worsening of the validation scores with the SOC.
Further investigations are needed to better quantify the magnitude order of:

* The SMOS dielectric constant model uncertainty in high SOC:
  To better understand the impact of the SMOS SOC consideration on the performance, here is a suggestion for further studies: i) to relate the RSOM with the SOC value then ii) to modelling the score's sensitivity to the RSOM.

An easy-to-run modelling experiment (using the breadboards platform) could also be run by using only Bircher or Mironov model to evaluate the performance sensitivity.

* The in-situ reference uncertainty:
  The results show a strong uncertainty of the reference in this context. Here is a suggestion to better understand the uncertainty magnitude in the uncertainty budget: i) assess SMOS on the "organic" calibrated probes identified in the ISMN database and ii) discuss the manufacturer accuracy specification in the validation scores.

This study will be valorized as a complementary analysis in the PhD activities of Juliette Ortet focusing on the soil moisture retrieval in the high latitude Ortet et al. 2023.

## References

[1] Fact sheets total organic carbon. URL: https://www.soilquality.org.au/factsheets/organic-carbon.

[2] *Manual TEROS 11/12.* URL: http://publications.metergroup.com/Manuals/20587_TEROS11-12_Manual_Web.pdf.

[3] Technical note: Towards an improved sm retrieval for organic-rich soils - esa/esrin contract 4000113119/15/i-sb0. 2017.

[4] Simone Bircher, Mie Andreasen, Johanna Vuollet, Juho Vehviläinen, Kimmo Rautiainen, François Jonard, Lutz Weihermüller, Elena Zakharova, Jean-Pierre Wigneron, and Yann H Kerr. Soil moisture sensor calibration for organic soil surface layers. *Geoscientific instrumentation, methods and data systems*, 5(1):109-125, 2016. doi:10.5194/gi-5-109-2016.

[5] Simone Bircher, Niels Skou, KH Jensen, Jeffrey P Walker, and L Rasmussen. A soil moisture and temperature network for smos validation in western denmark. *Hydrology and Earth System Sciences*, 16(5):1445-1463, 2012.

[6] Alexandra Bot and José Benites. *The importance of soil organic matter: Key to drought-resistant soil and sustained food production*. Number 80. Food & Agriculture Org., 2005.

[7] Lefevre Clara, Rekik Fatma, Alcantara Viridiana, and Wiese Liesl. Fao report - soil organic carbon hidden potential. 2017. URL: https://www.fao.org/3/i6937e/i6937e.pdf.

[8] David C Coleman, J Malcolm Oades, Goro Uehara, et al. Dynamics of soil organic matter in tropical ecosystems. 1989.

[9] François Gibon, Arnaud Mialon, Philippe Richaume, Yann H. Kerr, Alireza Mahmoodi, Nemesio Jose Rodriguez-Fernandez, Roberto Sabia, and Alexander Boresch. So-tn-cb-gs-0104 - technical note 1: Smos validation and committed areas.

[10] Simons G.W.H., R. Koster, and P. Droogers. Shihydrosoil v2.0 - a high resolution soil map of global hydraulic properties. 2020.
[11] Tomislav Hengl and Ichsani Wheeler. Soil organic carbon content in x 5 g / kg at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution (version v02). 2018. doi:10.5281/zenodo.1475457.

[12] Y H Kerr, P. Waldteufel, P. Richaume, J-P Wigneron, P Ferrazzoli, and R Gurney. SMOS level 2 processor for soil moisture - Algorithm Theoretical Based Document (ATBD). Technical Report SO-TN-ESL-SM-GS-0001, Issue 3.e, 2011. 121 p. URL: http://www.cesbio.ups-tlse.fr/fr/indexsmos.html.

[13] Y.H. Kerr, P. Richaume, P. Waldteufel, P. Ferrazzoli, J.P. Wigneron, M. Schwank, and K. Rautiainen. Algorithm theoretical basis document (ATBD) for the smos level 2 soil moisture processor. Technical Report TN-ESL-SM-GS-0001-4b SM-ESL (CBSA), page 145p, 2020.

[14] Umakant Mishra, Gustaf Hugelius, Eitan Shelef, Yuanhe Yang, Jens Strauss, Alexey Lupachev, Jennifer W. Harden, Julie D. Jastrow, Chien-Lu Ping, William J. Riley, Edward A. G. Schuur, Roser Matamala, Matthias Siewert, Lucas E. Nave, Charles D. Koven, Matthias Fuchs, Juri Palmtag, Peter Kuhry, Claire C. Treat, Sebastian Zubrzycki, Forrest M. Hoffman, Bo Elberling, Philip Camill, Alexandra Veremeeva, and Andrew Orr. Spatial heterogeneity and environmental predictors of permafrost region soil organic carbon stocks. Science Advances, 7(9), 2021. URL: https://advances.sciencemag.org/content/7/9/eaaz5236, arXiv:https://advances.sciencemag.org/content/7/9/eaaz5236.full.pdf, doi:10.1126/sciadv.aaz5236.

[15] L. Poggio, L. M. de Sousa, N. H. Batjes, G. B. M. Heuvelink, B. Kempen, E. Ribeiro, and D. Rossiter. Soilgrids 2.0: producing soil information for the globe with quantified spatial uncertainty. SOIL, 7(1):217-240, 2021. URL: https://soil.copernicus.org/articles/7/217/2021/, doi:10.5194/soil-7-217-2021.

[16] E. A. G. Schuur, A. D. McGuire, C. Schädel, G. Grosse, J. W. Harden, D. J. Hayes, G. Hugelius, C. D. Koven, P. Kuhry, D. M. Lawrence, S. M. Natali, D. Olefeldt, V. E. Romanovsky, K. Schaefer, M. R. Turetsky, C. C. Treat, and J. E. Vonk. Climate change and the permafrost carbon feedback. Nature, 520(7546):171-179, 2015. doi:https://doi.org/10.1038/nature14338.

[17] Yuanhe Yang, Jingyun Fang, Yanhong Tang, Chengjun Ji, Chengyang Zheng, Jinsheng He, and Biao Zhu. Storage, patterns and controls of soil organic carbon in the tibetan grasslands. Global change biology, 14(7):1592-1599, 2008.