An Airborne Microwave Radiometer System for Soil Moisture Mapping

1990 ◽  
Author(s):  
G. Ijjas
Keyword(s):  
Soil Moisture ◽  
Microwave Radiometer

Microwave radiometer observations of soil moisture in HAPEX-SAHEL

1995 ◽  
Author(s):  
Thomas J. Schmugge ◽  
Andre Chanzy ◽  
Yann H. Kerr ◽  
Peter van Oevelen
Keyword(s):  
Soil Moisture ◽  
Microwave Radiometer

Large area mapping of soil moisture using the ESTAR passive microwave radiometer in Washita'92

1995 ◽  
Vol 54 (1) ◽  
pp. 27-37 ◽  
Author(s):  
Thomas J. Jackson ◽  
David M. Le Vine ◽  
Calvin T. Swift ◽  
Thomas J. Schmugge ◽  
Frank R. Schiebe
Keyword(s):  
Soil Moisture ◽  
Microwave Radiometer ◽  
Passive Microwave ◽  
Large Area

The CIMR mission and its unique capabilities for soil moisture sensing

2021 ◽  
Author(s):  
Maria Piles ◽  
Roberto Fernandez-Moran ◽  
Luis Gómez-Chova ◽  
Gustau Camps-Valls ◽  
Dara Entekhabi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Spatial Scales ◽  
Microwave Radiometer ◽  
Critical Time ◽  
Global Scale ◽  
The Arctic ◽  
Temporal Perspective ◽  
Microwave Radiometers ◽  
Moisture Sensing

<p>The Copernicus Imaging Microwave Radiometer (CIMR) mission is currently being developed as a High Priority Copernicus Mission to support the Integrated European Policy for the Arctic. Due to its measurement characteristics, CIMR has exciting capabilities to enable a unique set of land surface products and science applications at a global scale. These characteristics go beyond what previous microwave radiometers (e.g. AMSR series, SMAP and SMOS) provide, and therefore allow for entirely new approaches to the estimation of bio-geophysical products from brightness temperature observations. Most notably, CIMR channels (L-,C-,X-,Ka-,Ku-bands) are very well fit for the simultaneous retrieval of soil moisture and vegetation properties, like biomass and moisture of different plant components such as leaves, stems or trunks. Also, the distinct spatial resolution of each frequency band allows for the development of approaches to cascade information and obtain these properties at multiple spatial scales. From a temporal perspective, CIMR has a higher revisit time than previous L-band missions dedicated to soil moisture monitoring (about 1 day global, sub-daily at the poles). This improved temporal resolution could allow resolving critical time scales of water processes, which is relevant to better model and understand land-atmosphere exchanges and feedbacks. In this presentation, new opportunities for soil moisture remote sensing made possible by the CIMR mission, as well as synergies and cross-sensor opportunities will be discussed.  </p>

scholarly journals Copernicus Imaging Microwave Radiometer (CIMR) Benefits for the Copernicus Level 4 Sea-Surface Salinity Processing Chain

2019 ◽  
Vol 11 (15) ◽  
pp. 1818 ◽  
Author(s):  
Daniele Ciani ◽  
Rosalia Santoleri ◽  
Gian Luigi Liberti ◽  
Catherine Prigent ◽  
Craig Donlon ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Microwave Radiometer ◽  
Ocean Surface ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Optimal Interpolation ◽  
Surface Salinity ◽  
Processing Chain

We present a study on the potential of the Copernicus Imaging Microwave Radiometer (CIMR) mission for the global monitoring of Sea-Surface Salinity (SSS) using Level-4 (gap-free) analysis processing. Space-based SSS are currently provided by the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites. However, there are no planned missions to guarantee continuity in the remote SSS measurements for the near future. The CIMR mission is in a preparatory phase with an expected launch in 2026. CIMR is focused on the provision of global coverage, high resolution sea-surface temperature (SST), SSS and sea-ice concentration observations. In this paper, we evaluate the mission impact within the Copernicus Marine Environment Monitoring Service (CMEMS) SSS processing chain. The CMEMS SSS operational products are based on a combination of in situ and satellite (SMOS) SSS and high-resolution SST information through a multivariate optimal interpolation. We demonstrate the potential of CIMR within the CMEMS SSS operational production after the SMOS era. For this purpose, we implemented an Observing System Simulation Experiment (OSSE) based on the CMEMS MERCATOR global operational model. The MERCATOR SSSs were used to generate synthetic in situ and CIMR SSS and, at the same time, they provided a reference gap-free SSS field. Using the optimal interpolation algorithm, we demonstrated that the combined use of in situ and CIMR observations improves the global SSS retrieval compared to a processing where only in situ observations are ingested. The improvements are observed in the 60% and 70% of the global ocean surface for the reconstruction of the SSS and of the SSS spatial gradients, respectively. Moreover, the study highlights the CIMR-based salinity patterns are more accurate both in the open ocean and in coastal areas. We conclude that CIMR can guarantee continuity for accurate monitoring of the ocean surface salinity from space.

Comparing ERA-40-Based L-Band Brightness Temperatures with Skylab Observations: A Calibration/Validation Study Using the Community Microwave Emission Model

2009 ◽  
Vol 10 (1) ◽  
pp. 213-226 ◽  
Author(s):  
Matthias Drusch ◽  
Thomas Holmes ◽  
Patricia de Rosnay ◽  
Gianpaolo Balsamo
Keyword(s):  
Soil Moisture ◽  
Dynamic Range ◽  
Microwave Radiometer ◽  
Space Station ◽  
Microwave Emission ◽  
Emission Model ◽  
Soil Database ◽  
Brightness Temperatures ◽  
L Band ◽  
Rms Errors

Abstract The Community Microwave Emission Model (CMEM) has been used to compute global L-band brightness temperatures at the top of the atmosphere. The input data comprise surface fields from the 40-yr ECMWF Re-Analysis (ERA-40), vegetation data from the ECOCLIMAP dataset, and the Food and Agriculture Organization’s (FAO) soil database. Modeled brightness temperatures have been compared against (historic) observations from the S-194 passive microwave radiometer onboard the Skylab space station. Different parameterizations for surface roughness and the vegetation optical depth have been used to calibrate the model. The best results have been obtained for rather simple approaches proposed by Wigneron et al. and Kirdyashev et al. The rms errors after calibration are 10.7 and 9.8 K for North and South America, respectively. Comparing the ERA-40 soil moisture product against the corresponding in situ observations suggests that the uncertainty in the modeled soil moisture is the predominant contributor to these rms errors. Although the bias between model and observed brightness temperatures are reduced after the calibration, systematic differences in the dynamic range remain. For NWP analysis applications, bias correction schemes should be applied prior to data assimilation. The calibrated model has been used to compute a 10-yr brightness temperature climatology based on ERA-40 data.

scholarly journals Effect of Rainfall Events on SMAP Radiometer-Based Soil Moisture Accuracy Using Core Validation Sites

2020 ◽  
Vol 21 (2) ◽  
pp. 255-264 ◽  
Author(s):  
Andreas Colliander ◽  
Thomas J. Jackson ◽  
Aaron Berg ◽  
D. D. Bosch ◽  
Todd Caldwell ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Pearson Correlation ◽  
Microwave Radiometer ◽  
Clay Content ◽  
Transitional Period ◽  
Moisture Distribution ◽  
Rain Event ◽  
Static State ◽  
In Situ Sensors ◽  
Rain Events

AbstractSoil moisture retrieval is particularly challenging during and immediately after precipitation events because of the transient movement of water in the shallow subsurface. Conventional L-band microwave radiometer–based soil moisture products use algorithms that assume a static state and a constant vertical soil moisture distribution. This study assessed the retrieval performance of a SMAP radiometer-based soil moisture product during and immediately after rain events. The removal of the rain event samples systematically improved the unbiased root-mean-square error (ubRMSE) from 0.037 (all measurements) to 0.028 m3 m−3 (transitory measurements screened out), while the magnitude of the bias became larger (from −0.005 to −0.014 m3 m−3); RMSE improved from 0.047 to 0.042 m3 m−3, and the Pearson correlation saw a minor positive change from 0.813 to 0.824. The results indicate that removing samples during the transitional period causes the comparison to improve, but also suggests that the true bias may be larger than the one estimated using all the samples. Furthermore, the results revealed that the effect was stronger for areas with high clay content. An assessment of the performance of the product during the rain events (overpass within 3 h from the start of the rain) showed that the ubRMSE degraded from the benchmarked 0.036 m3 m−3 (during no rain events at all) to 0.043 m3 m−3 (during rain). The results also showed that the bias became wetter, which is expected because SMAP sensed the water on the surface before propagating to the in situ sensors. SMAP maintains its soil moisture sensitivity even during rain events and screening of rain events may not be necessary to ensure sufficient soil moisture retrieval quality.

scholarly journals Assessment of Multi-Scale SMOS and SMAP Soil Moisture Products across the Iberian Peninsula

2020 ◽  
Vol 12 (3) ◽  
pp. 570 ◽  
Author(s):  
Gerard Portal ◽  
Thomas Jagdhuber ◽  
Mercè Vall-llossera ◽  
Adriano Camps ◽  
Miriam Pablos ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Iberian Peninsula ◽  
Spatial Scale ◽  
Temporal Dynamics ◽  
Microwave Radiometer ◽  
Added Value ◽  
Optical Data ◽  
Enhanced Resolution ◽  
The Impact

In the last decade, technological advances led to the launch of two satellite missions dedicated to measure the Earth’s surface soil moisture (SSM): the ESA’s Soil Moisture and Ocean Salinity (SMOS) launched in 2009, and the NASA’s Soil Moisture Active Passive (SMAP) launched in 2015. The two satellites have an L-band microwave radiometer on-board to measure the Earth’s surface emission. These measurements (brightness temperatures TB) are then used to generate global maps of SSM every three days with a spatial resolution of about 30–40 km and a target accuracy of 0.04 m3/m3. To meet local applications needs, different approaches have been proposed to spatially disaggregate SMOS and SMAP TB or their SSM products. They rely on synergies between multi-sensor observations and are built upon different physical assumptions. In this study, temporal and spatial characteristics of six operational SSM products derived from SMOS and SMAP are assessed in order to diagnose their distinct features, and the rationale behind them. The study is focused on the Iberian Peninsula and covers the period from April 2015 to December 2017. A temporal inter-comparison analysis is carried out using in situ SSM data from the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) to evaluate the impact of the spatial scale of the different products (1, 3, 9, 25, and 36 km), and their correspondence in terms of temporal dynamics. A spatial analysis is conducted for the whole Iberian Peninsula with emphasis on the added-value that the enhanced resolution products provide based on the microwave-optical (SMOS/ERA5/MODIS) or the active–passive microwave (SMAP/Sentinel-1) sensor fusion. Our results show overall agreement among time series of the products regardless their spatial scale when compared to in situ measurements. Still, higher spatial resolutions would be needed to capture local features such as small irrigated areas that are not dominant at the 1-km pixel scale. The degree to which spatial features are resolved by the enhanced resolution products depend on the multi-sensor synergies employed (at TB or soil moisture level), and on the nature of the fine-scale information used. The largest disparities between these products occur in forested areas, which may be related to the reduced sensitivity of high-resolution active microwave and optical data to soil properties under dense vegetation.

scholarly journals Assimilation of Disaggregated Microwave Soil Moisture into a Hydrologic Model Using Coarse-Scale Meteorological Data

2006 ◽  
Vol 7 (6) ◽  
pp. 1308-1322 ◽  
Author(s):  
O. Merlin ◽  
A. Chehbouni ◽  
G. Boulet ◽  
Y. Kerr
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Meteorological Data ◽  
Microwave Radiometer ◽  
Hydrologic Model ◽  
Observation Time ◽  
Coupling Scheme ◽  
Surface Soil Moisture ◽  
Coarse Scale ◽  
Near Surface

Abstract Near-surface soil moisture retrieved from Soil Moisture and Ocean Salinity (SMOS)-type data is downscaled and assimilated into a distributed soil–vegetation–atmosphere transfer (SVAT) model with the ensemble Kalman filter. Because satellite-based meteorological data (notably rainfall) are not currently available at finescale, coarse-scale data are used as forcing in both the disaggregation and the assimilation. Synthetic coarse-scale observations are generated from the Monsoon ‘90 data by aggregating the Push Broom Microwave Radiometer (PBMR) pixels covering the eight meteorological and flux (METFLUX) stations and by averaging the meteorological measurements. The performance of the disaggregation/assimilation coupling scheme is then assessed in terms of surface soil moisture and latent heat flux predictions over the 19-day period of METFLUX measurements. It is found that the disaggregation improves the assimilation results, and vice versa, the assimilation of the disaggregated microwave soil moisture improves the spatial distribution of surface soil moisture at the observation time. These results are obtainable regardless of the spatial scale at which solar radiation, air temperature, wind speed, and air humidity are available within the microwave pixel and for an assimilation frequency varying from 1/1 day to 1/5 days.

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