Estimation of radiative transfer parameters for soil moisture retrieval from SMOS brightness temperatures - a synthetic 1D experiment with the Particle Filter

Abstract. L-Band radiometry is considered to be one of the most suitable techniques to estimate surface soil moisture by means of remote sensing. Brightness temperatures are key in this process, as they are the main input in the retrieval algorithm. The work exposed compares brightness temperatures measured by the Soil Moisture and Ocean Salinity (SMOS) mission to two different sets of modelled ones, over the Iberian Peninsula from 2010 to 2012. The latter were estimated using a radiative transfer model and state variables from two land surface models: (i) ORganising Carbon and Hydrology In Dynamic EcosystEms (ORCHIDEE) and (ii) Hydrology – Tiled ECMWF Scheme for Surface Exchanges over Land (H-TESSEL). The radiative transfer model used is the Community Microwave Emission Model (CMEM). A good agreement in the temporal evolution of measured and modelled brightness temperatures is observed. However, their spatial structures are not consistent between them. An Empirical Orthogonal Function analysis of the brightness temperature's error identifies a dominant structure over the South-West of the Iberian Peninsula which evolves during the year and is maximum in Fall and Winter. Hypotheses concerning forcing induced biases and assumptions made in the radiative transfer model are analysed to explain this inconsistency, but no candidate is found to be responsible for it at the moment. Further hypotheses are proposed at the end of the paper.

Download Full-text

Estimation of Radiative Transfer Parameters from L-Band Passive Microwave Brightness Temperatures Using Advanced Data Assimilation

Vadose Zone Journal ◽

10.2136/vzj2012.0040 ◽

2013 ◽

Vol 12 (3) ◽

pp. vzj2012.0040 ◽

Cited By ~ 40

Author(s):

Carsten Montzka ◽

Jennifer P. Grant ◽

Hamid Moradkhani ◽

Harrie-Jan Hendricks Franssen ◽

Lutz Weihermüller ◽

...

Keyword(s):

Radiative Transfer ◽

Data Assimilation ◽

Passive Microwave ◽

Brightness Temperatures ◽

L Band ◽

Transfer Parameters

Download Full-text

Assimilation of microwave brightness temperatures for soil moisture estimation using particle filter

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/17/1/012126 ◽

2014 ◽

Vol 17 ◽

pp. 012126

Author(s):

H Y Bi ◽

J W Ma ◽

S X Qin ◽

J Y Zeng

Keyword(s):

Soil Moisture ◽

Particle Filter ◽

Brightness Temperatures ◽

Moisture Estimation

Download Full-text

Mapping barrier island soil moisture using a radiative transfer model of hyperspectral imagery from an unmanned aerial system

Scientific Reports ◽

10.1038/s41598-021-82783-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Rehman S. Eon ◽

Charles M. Bachmann

Keyword(s):

Soil Moisture ◽

Radiative Transfer ◽

Moisture Content ◽

Layer Thickness ◽

Soil Moisture Content ◽

Hyperspectral Imagery ◽

Barrier Island ◽

Water Layer ◽

Radiative Transfer Model ◽

Transfer Model

AbstractThe advent of remote sensing from unmanned aerial systems (UAS) has opened the door to more affordable and effective methods of imaging and mapping of surface geophysical properties with many important applications in areas such as coastal zone management, ecology, agriculture, and defense. We describe a study to validate and improve soil moisture content retrieval and mapping from hyperspectral imagery collected by a UAS system. Our approach uses a recently developed model known as the multilayer radiative transfer model of soil reflectance (MARMIT). MARMIT partitions contributions due to water and the sediment surface into equivalent but separate layers and describes these layers using an equivalent slab model formalism. The model water layer thickness along with the fraction of wet surface become parameters that must be optimized in a calibration step, with extinction due to water absorption being applied in the model based on equivalent water layer thickness, while transmission and reflection coefficients follow the Fresnel formalism. In this work, we evaluate the model in both field settings, using UAS hyperspectral imagery, and laboratory settings, using hyperspectral spectra obtained with a goniometer. Sediment samples obtained from four different field sites representing disparate environmental settings comprised the laboratory analysis while field validation used hyperspectral UAS imagery and coordinated ground truth obtained on a barrier island shore during field campaigns in 2018 and 2019. Analysis of the most significant wavelengths for retrieval indicate a number of different wavelengths in the short-wave infra-red (SWIR) that provide accurate fits to measured soil moisture content in the laboratory with normalized root mean square error (NRMSE)< 0.145, while independent evaluation from sequestered test data from the hyperspectral UAS imagery obtained during the field campaign obtained an average NRMSE = 0.169 and median NRMSE = 0.152 in a bootstrap analysis.

Download Full-text

Soil moisture and vegetation effects on the ASCAT backscatter-incidence angle dependence

10.5194/egusphere-egu21-10806 ◽

2021 ◽

Author(s):

Isabella Pfeil ◽

Wolfgang Wagner ◽

Sebastian Hahn ◽

Raphael Quast ◽

Susan Steele-Dunne ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Radiative Transfer ◽

Incidence Angle ◽

Transfer Model ◽

Study Region ◽

Vegetation Effects ◽

First Order ◽

Vegetation Parameters ◽

Low Incidence

<div> <p>Soil moisture (SM) datasets retrieved from the advanced&#160;scatterometer&#160;(ASCAT) sensor are well established and widely used for various hydro-meteorological, agricultural,&#160;and&#160;climate monitoring applications. Besides SM, ASCAT is sensitive to vegetation structure and vegetation water content, enabling the retrieval of vegetation optical depth (VOD;&#160;1). The challenge in the retrieval of SM and vegetation products&#160;from ASCAT observations&#160;is to separate the two effects. As described by Wagner et al. (2), SM and vegetation affect the&#160;relation between&#160;backscatter&#160;and incidence angle differently.&#160;&#160;At high incidence angles, the response from bare soil and thus the sensitivity to SM conditions is significantly weaker than at low incidence angles,&#160;leading to decreasing backscatter with increasing incidence angle.&#160;The presence of vegetation on the other hand&#160;decreases the backscatter dependence on&#160;the incidence angle. The dependence of backscatter on the incidence angle&#160;can be&#160;described by a second-order Taylor polynomial based on a slope and a curvature coefficient.&#160;It was found empirically that SM conditions have no significant effect on the steepness of the slope, and that therefore,&#160;SM and&#160;vegetation effects can be&#160;separated&#160;using&#160;the&#160;slope&#160;(2).&#160;&#160;This is a major assumption in the&#160;TU&#160;Wien&#160;soil moisture retrieval algorithm used in several operational soil moisture products. However,&#160;recent&#160;findings by&#160;Quast et al. (3)&#160;using&#160;a first-order radiative transfer model for the inversion of soil and vegetation parameters from&#160;scatterometer&#160;observations&#160;indicate that SM may influence the slope, as the SM-induced backscatter increase is more pronounced at low incidence angles.&#160;</p> </div><div> <div> <p>The aim of this analysis is to&#160;revisit&#160;the assumption that&#160;SM&#160;does not affect the&#160;slope of the backscatter incidence angle relations by&#160;investigating&#160;if&#160;short-term variability,&#160;observed&#160;in ASCAT slope&#160;timeseries&#160;on top of the seasonal vegetation cycle,&#160;is caused by SM.&#160;We therefore compare timeseries and anomalies of&#160;the&#160;ASCAT slope&#160;to&#160;air temperature,&#160;rainfall&#160;and SM&#160;from the ERA5-Land dataset. We carry out the analysis in a&#160;humid continental&#160;climate (Austria) and a&#160;Mediterranean&#160;climate&#160;study region (Portugal).&#160;First results show&#160;significant negative&#160;correlations between slope and SM anomalies. However,&#160;correlations between temperature and slope anomalies are&#160;of a similar magnitude,&#160;albeit&#160;positive,&#160;which may reflect temperature-induced vegetation dynamics. The fact that temperature and SM are&#160;strongly correlated&#160;with each&#160;other&#160;complicates the&#160;interpretation of the results.&#160;Thus, our second approach is to&#160;investigate&#160;daily slope values and their change between dry and wet days.&#160;The results of this study shall&#160;help to quantify&#160;the&#160;uncertainties&#160;in ASCAT SM products caused by&#160;the potentially&#160;inadequate&#160;assumption&#160;of a SM-independent&#160;slope.&#160;</p> </div> <div> <p>&#160;</p> </div> <div> <p>(1) Vreugdenhil, Mariette, et al. "Analyzing the vegetation parameterization in the TU-Wien ASCAT soil moisture retrieval." IEEE Transactions on Geoscience and Remote Sensing 54.6 (2016): 3513-3531.</p> <p><span>(2) Wagner, Wolfgang, et al. "Monitoring soil moisture over the Canadian Prairies with the ERS scatterometer." IEEE Transactions on Geoscience and Remote Sensing 37.1 (1999): 206-216.&#160;</span></p> </div> <div> <p>(3) Quast, Raphael, et al. "A Generic First-Order Radiative Transfer Modelling Approach for the Inversion of Soil and Vegetation Parameters from Scatterometer Observations." Remote Sensing 11.3 (2019): 285.</p> </div> </div>

Download Full-text

Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model

Hydrology and Earth System Sciences ◽

10.5194/hess-20-4895-2016 ◽

2016 ◽

Vol 20 (12) ◽

pp. 4895-4911 ◽

Cited By ~ 59

Author(s):

Gabriëlle J. M. De Lannoy ◽

Rolf H. Reichle

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Surface Soil ◽

Root Zone ◽

Incidence Angle ◽

Land Surface Model ◽

Surface Model ◽

Model Errors ◽

Surface Soil Moisture ◽

Brightness Temperatures

Abstract. Three different data products from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated separately into the Goddard Earth Observing System Model, version 5 (GEOS-5) to improve estimates of surface and root-zone soil moisture. The first product consists of multi-angle, dual-polarization brightness temperature (Tb) observations at the bottom of the atmosphere extracted from Level 1 data. The second product is a derived SMOS Tb product that mimics the data at a 40° incidence angle from the Soil Moisture Active Passive (SMAP) mission. The third product is the operational SMOS Level 2 surface soil moisture (SM) retrieval product. The assimilation system uses a spatially distributed ensemble Kalman filter (EnKF) with seasonally varying climatological bias mitigation for Tb assimilation, whereas a time-invariant cumulative density function matching is used for SM retrieval assimilation. All assimilation experiments improve the soil moisture estimates compared to model-only simulations in terms of unbiased root-mean-square differences and anomaly correlations during the period from 1 July 2010 to 1 May 2015 and for 187 sites across the US. Especially in areas where the satellite data are most sensitive to surface soil moisture, large skill improvements (e.g., an increase in the anomaly correlation by 0.1) are found in the surface soil moisture. The domain-average surface and root-zone skill metrics are similar among the various assimilation experiments, but large differences in skill are found locally. The observation-minus-forecast residuals and analysis increments reveal large differences in how the observations add value in the Tb and SM retrieval assimilation systems. The distinct patterns of these diagnostics in the two systems reflect observation and model errors patterns that are not well captured in the assigned EnKF error parameters. Consequently, a localized optimization of the EnKF error parameters is needed to further improve Tb or SM retrieval assimilation.

Download Full-text

Retrieving soil moisture from simulated brightness temperatures by a neural network

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/36.942544 ◽

2001 ◽

Vol 39 (8) ◽

pp. 1662-1672 ◽

Cited By ~ 33

Author(s):

Yuei-An Lieu ◽

Shou-Fang Liu ◽

Wen-June Wang

Keyword(s):

Neural Network ◽

Soil Moisture ◽

Brightness Temperatures

Download Full-text

Objective evaluation of mesoscale simulations of the Algiers 2001 flash flood by the model-to-satellite approach

Advances in Geosciences ◽

10.5194/adgeo-7-247-2006 ◽

2006 ◽

Vol 7 ◽

pp. 247-250 ◽

Cited By ~ 8

Author(s):

N. Söhne ◽

J.-P. Chaboureau ◽

S. Argence ◽

D. Lambert ◽

E. Richard

Keyword(s):

Radiative Transfer ◽

Flash Flood ◽

Objective Evaluation ◽

Brightness Temperatures ◽

Grid Nesting ◽

Mesoscale Simulations

Abstract. An objective evaluation of mesoscale simulations by the model-to-satellite approach is performed. The model-to-satellite approach consists in calculating brightness temperatures (BT) from model variables with a radiative transfer code. It allows to compare directly and quantitatively simulations and observations by calculating statistical scores. This method is detailed and used herein to objectively evaluate an ensemble of Meso-NH simulations of the Algiers 2001 flash flood. In particular, the improvement due to the grid-nesting is shown.

Download Full-text