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Steven Reising
Electrical and Computer Engineering Department, Colorado State University, 1373 Campus Delivery, Fort Collins, CO 80523-1373, USA

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Short Biography

Professor and Director/Dr. Steven C. Reising received B.S. and M.S. degrees in electrical engineering from Washington University in St. Louis, and a Ph.D. degree in electrical engineering from Stanford University in 1998. He served as Assistant Professor in Electrical and Computer Engineering at the University of Massachusetts Amherst (1998-2004), where he received tenure. He served as Associate Professor at Colorado State University (2004-2011) and is currently a Full Professor in Electrical and Computer Engineering (2011-present). Dr. Reising has been the Principal Investigator of 18 grants from NASA, NOAA, NSF, DoD, ONR, NPOESS, ESA, Ball Aerospace, and FIRST RF Corporation. Dr. Reising’s research interests span a broad range of remote-sensing disciplines, including microwave remote sensing of the atmosphere and oceans from aircraft, SmallSats, and CubeSats; as well as design and development of radiometer systems, from microwave to THz frequencies. Dr. Reising has served as Associate Editor for Atmospheric Remote Sensing of MDPI's Remote Sensing since 2018. He was a founding Associate Editor of IEEE GRSL (2004-2013). He currently serves as Secretary of the IEEE Geoscience and Remote Sensing Society (GRSS), and previously served in many leadership positions, including as an elected AdCom Member of both IEEE GRSS (2003-2020) and IEEE MTT-S (2014-2019).

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Editorial
Published: 22 July 2021 in Remote Sensing
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Evidence of the rapid degradation of the Earth’s natural environment has grown in recent years

ACS Style

Yuei-An Liou; Yuriy Kuleshov; Chung-Ru Ho; Kim-Anh Nguyen; Steven Reising. Preface: Earth Observations for Environmental Sustainability for the Next Decade. Remote Sensing 2021, 13, 2871 .

AMA Style

Yuei-An Liou, Yuriy Kuleshov, Chung-Ru Ho, Kim-Anh Nguyen, Steven Reising. Preface: Earth Observations for Environmental Sustainability for the Next Decade. Remote Sensing. 2021; 13 (15):2871.

Chicago/Turabian Style

Yuei-An Liou; Yuriy Kuleshov; Chung-Ru Ho; Kim-Anh Nguyen; Steven Reising. 2021. "Preface: Earth Observations for Environmental Sustainability for the Next Decade." Remote Sensing 13, no. 15: 2871.

Journal article
Published: 11 December 2020 in IEEE Transactions on Geoscience and Remote Sensing
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The Temporal Experiment for Storms and Tropical Systems Technology Demonstration (TEMPEST-D) instrument is a five-frequency millimeter-wave radiometer operating from 87 to 181 GHz. The cross-track scanning radiometer has been operating on a 6U CubeSat in low Earth orbit since September 5, 2018. The direct-detection architecture of the radiometer reduces its mass and power consumption by eliminating the need for a local oscillator and mixer, also reducing system complexity. The instrument includes a scanning reflector and ambient calibration target. The reflector rotates continuously to scan the antenna beams in the cross-track direction, first across the blackbody calibration target, then toward the Earth over the full range of incidence angles, and finally to cosmic microwave background radiation at 2.73 K. This enables precision end-to-end calibration of the millimeter-wave receivers during every 2-s scan period. The TEMPEST-D millimeter-wave radiometers are based on 35-nm indium phosphide (InP) high-electron-mobility transistor (HEMT) low-noise amplifiers. This article describes the instrument and its characterization prior to launch.

ACS Style

Sharmila Padmanabhan; Todd C. Gaier; Alan B. Tanner; Shannon T. Brown; Boon H. Lim; Steven C. Reising; Robert Stachnik; Rudi Bendig; Richard Cofield. TEMPEST-D Radiometer: Instrument Description and Prelaunch Calibration. IEEE Transactions on Geoscience and Remote Sensing 2020, PP, 1 -14.

AMA Style

Sharmila Padmanabhan, Todd C. Gaier, Alan B. Tanner, Shannon T. Brown, Boon H. Lim, Steven C. Reising, Robert Stachnik, Rudi Bendig, Richard Cofield. TEMPEST-D Radiometer: Instrument Description and Prelaunch Calibration. IEEE Transactions on Geoscience and Remote Sensing. 2020; PP (99):1-14.

Chicago/Turabian Style

Sharmila Padmanabhan; Todd C. Gaier; Alan B. Tanner; Shannon T. Brown; Boon H. Lim; Steven C. Reising; Robert Stachnik; Rudi Bendig; Richard Cofield. 2020. "TEMPEST-D Radiometer: Instrument Description and Prelaunch Calibration." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-14.

Journal article
Published: 06 November 2020 in IEEE Transactions on Terahertz Science and Technology
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In this letter, a novel 1/f noise mitigation technique is presented to improve the receiver 1/f noise performance of a 670 GHz receiver. Time domain 1/f noise corrected samples are compared with samples obtained without the correction. Spectral domain analysis shows that the 1/f noise mitigation method improves the receiver noise performance by 19 dB in the receiver under test. The presented 1/f noise mitigation technique can be applied to any direct-detection receiver in the THz frequency range.

ACS Style

Mehmet Ogut; Caitlyn M. Cooke; William R. Deal; Pekka Kangaslahti; Alan B. Tanner; Steven C. Reising. A Novel 1/f Noise Mitigation Technique Applied to a 670 GHz Receiver. IEEE Transactions on Terahertz Science and Technology 2020, 11, 109 -112.

AMA Style

Mehmet Ogut, Caitlyn M. Cooke, William R. Deal, Pekka Kangaslahti, Alan B. Tanner, Steven C. Reising. A Novel 1/f Noise Mitigation Technique Applied to a 670 GHz Receiver. IEEE Transactions on Terahertz Science and Technology. 2020; 11 (1):109-112.

Chicago/Turabian Style

Mehmet Ogut; Caitlyn M. Cooke; William R. Deal; Pekka Kangaslahti; Alan B. Tanner; Steven C. Reising. 2020. "A Novel 1/f Noise Mitigation Technique Applied to a 670 GHz Receiver." IEEE Transactions on Terahertz Science and Technology 11, no. 1: 109-112.

Journal article
Published: 09 September 2020 in IEEE Transactions on Geoscience and Remote Sensing
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Temporal Experiment for Storms and Tropical Systems--Demonstration (TEMPEST-D) is a 6U CubeSat satellite with a cross-track scanning millimeter-wave radiometer measuring at five frequencies from 87 to 181 GHz. It employs a direct-detection architecture with InP HEMT monolithic microwave integrated circuit (MMIC) low-noise amplifiers and related new technologies. An end-to-end two-point external calibration is performed every 2-s rotation of the scanning mirror, based on observations of the cosmic microwave background and an internal blackbody calibration target, with three thermistors to monitor the target physical temperature. Corrections for antenna pattern effects and cross-scan biases based on prelaunch measured values were updated using data from an on-orbit calibration pitch maneuver. Validation of the observed brightness temperatures (TB) is performed by comparing to coincident nonprecipitating ocean observations from five well-calibrated on-orbit instruments, including Global Precipitation Measurement (GPM) mission Microwave Imager (GMI) and four Microwave Humidity Sounder (MHS) sensors on board NOAA-19, MetOp-A, MetOp-B, and MetOp-C satellites. Absolute calibration accuracy is within 1 K for all channels, well within the 4-K requirement. Calibration precision, or stability over time, is within 0.6 K for all channels, also well within the 2-K requirement. The intrinsic noise of TEMPEST-D is lower than MHS, resulting in similar on-orbit noise equivalent differential temperatures (NEDTs), even though TEMPEST-D has a much shorter integration time of 5 ms as compared to 18 ms for MHS. As a result, although the TEMPEST-D radiometer is substantially smaller, lower power, and lower cost than similar current operational radiometers, it has comparable or better performance in terms of instrument noise, calibration accuracy, and calibration stability or precision.

ACS Style

Wesley Berg; Shannon T. Brown; Boon H. Lim; Steven C. Reising; Yuriy Goncharenko; Christian D. Kummerow; Todd C. Gaier; Sharmila Padmanabhan. Calibration and Validation of the TEMPEST-D CubeSat Radiometer. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 4904 -4914.

AMA Style

Wesley Berg, Shannon T. Brown, Boon H. Lim, Steven C. Reising, Yuriy Goncharenko, Christian D. Kummerow, Todd C. Gaier, Sharmila Padmanabhan. Calibration and Validation of the TEMPEST-D CubeSat Radiometer. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (6):4904-4914.

Chicago/Turabian Style

Wesley Berg; Shannon T. Brown; Boon H. Lim; Steven C. Reising; Yuriy Goncharenko; Christian D. Kummerow; Todd C. Gaier; Sharmila Padmanabhan. 2020. "Calibration and Validation of the TEMPEST-D CubeSat Radiometer." IEEE Transactions on Geoscience and Remote Sensing 59, no. 6: 4904-4914.

Preprint content
Published: 23 March 2020
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Passive microwave radiometer systems have provided both temperature and water vapor sounding of the Earth’s atmosphere for several decades, including MSU, AMSU, MHS, ATMS, etc.  Due to its ability to penetrate clouds, dust, and aerosols, among global datasets, microwave atmospheric sounding provides the most valuable quantitative contribution to weather prediction.  Long-term, well-calibrated sounding records can be indispensable for climate measurement and model initialization/validation.  Hence, passive microwave sounders are deployed on large, operational satellites and operated by NOAA, EUMETSAT and other similar national/international organizations.

In the past five years or so, advances in CubeSats and other small satellites have enabled highly affordable space technology, providing access to space to private industries, universities and smaller nations.  This provides a valuable opportunity for organizations such as NOAA and EUMETSAT to explore the added value of acquiring data from passive microwave sounders on small, low-cost spacecraft for relatively small investments, both for sensor and spacecraft acquisition and launch.  This provides the potential for deployment of constellations of low-Earth orbiting microwave sounders to provide much more frequent revisit times than are currently available.

For passive microwave sounding data to be valuable for weather prediction and climate monitoring, each sensor needs to be calibrated and validated to acceptable accuracy and stability.  In this context, the first CubeSat-based multi-frequency microwave sounder to provide global data over a substantial period is the Temporal Experiment for Storms and Tropical Systems Demonstration (TEMPEST-D) mission.  This mission was designed to demonstrate on-orbit capabilities of a new, five-frequency millimeter-wave radiometer to enable a complete TEMPEST mission using a closely-spaced train of eight 6U CubeSats with identical low-mass, low-power millimeter-wave sensors to sample rapid changes in convection and surrounding water vapor every 3-4 minutes for up to 30 minutes.  TEMPEST millimeter-wave radiometers scan across track and observe at five frequencies from 87 to 181 GHz, with spatial resolution ranging from 25 km to 13 km, respectively.

The TEMPEST-D satellite was launched on May 21, 2018 from NASA Wallops to the ISS and was successfully deployed on July 13, 2018, into a 400-km orbit at 51.6° inclination.  The TEMPEST-D sensor has been operating nearly continuously since its first light data on September 5, 2018.  With more than 16 months of operations to date, TEMPEST-D met all of its Level-1 mission objectives within the first 90 days of operations and has successfully achieved TRL 9 for both instrument and spacecraft systems. 

Validation of observed TEMPEST-D brightness temperatures is performed by comparing to coincident observations by well-calibrated on-orbit instruments, including GPM/GMI and MHS on NOAA-19, MetOp-A and MetOp-B satellites. Absolute calibration accuracy is within 0.9 K for all except the 164 GHz channel, well within the required 4 K for all channels. Calibration stability is within 0.5 K for all channels, also well within the 2 K requirement. TEMPEST-D has NEDTs similar to or lower than MHS. Therefore, although the TEMPEST-D radiometer is substantially smaller, lower power, and lower cost than operational radiometers, it has comparable performance, i.e. instrument noise, calibration accuracy and calibration stability.

ACS Style

Steven C. Reising; Wesley Berg; Shannon T. Brown; Todd C. Gaier; Christian D. Kummerow; Venkatchalam Chandrasekar; Sharmila Padmanabhan; Boon H. Lim; Richard Schulte; Yuriy Goncharenko; Chandrasekar Radhakrishnan. Calibration and Validation of Microwave Atmospheric Sounders on CubeSats and Small Satellites for Applications in Weather Prediction and Climate Monitoring. 2020, 1 .

AMA Style

Steven C. Reising, Wesley Berg, Shannon T. Brown, Todd C. Gaier, Christian D. Kummerow, Venkatchalam Chandrasekar, Sharmila Padmanabhan, Boon H. Lim, Richard Schulte, Yuriy Goncharenko, Chandrasekar Radhakrishnan. Calibration and Validation of Microwave Atmospheric Sounders on CubeSats and Small Satellites for Applications in Weather Prediction and Climate Monitoring. . 2020; ():1.

Chicago/Turabian Style

Steven C. Reising; Wesley Berg; Shannon T. Brown; Todd C. Gaier; Christian D. Kummerow; Venkatchalam Chandrasekar; Sharmila Padmanabhan; Boon H. Lim; Richard Schulte; Yuriy Goncharenko; Chandrasekar Radhakrishnan. 2020. "Calibration and Validation of Microwave Atmospheric Sounders on CubeSats and Small Satellites for Applications in Weather Prediction and Climate Monitoring." , no. : 1.

Journal article
Published: 18 December 2019 in IEEE Transactions on Geoscience and Remote Sensing
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Calibration plays an important role in improving the accuracy of the microwave and millimeter-wave radiometric measurements. Several calibration techniques have been used in radiometers including external calibration targets, vicarious sources, and internal calibrators such as noise diodes or matched reference load. A new calibration technique based on deep learning has recently been developed to calibrate microwave and millimeter-wave radiometers. The deep-learning calibrator has been previously demonstrated on a computer noise-wave modeled Dicke-switching radiometer. This article applies the new deep-learning calibration technique for the calibration of the high-frequency airborne microwave and millimeter-wave radiometer (HAMMR) instrument. A deep-learning neural network model is built to calibrate the 2014 West Coast Flight Campaign antenna temperature measurements of the HAMMR. The deep-learning calibrator antenna temperature estimates are obtained from the radiometric measurements. The deep-learning calibration results are compared with the existing conventional calibration techniques used in HAMMR 2014 field campaign. The results have shown that the deep-learning calibrator is in agreement with the conventional calibration techniques. In this article, it is demonstrated that the deep-learning calibrator can be employed for calibrating the radiometers with high accuracy.

ACS Style

Mehmet Ogut; Xavier Bosch-Lluis; Steven C. Reising. Deep Learning Calibration of the High-Frequency Airborne Microwave and Millimeter-Wave Radiometer (HAMMR) Instrument. IEEE Transactions on Geoscience and Remote Sensing 2019, 58, 3391 -3399.

AMA Style

Mehmet Ogut, Xavier Bosch-Lluis, Steven C. Reising. Deep Learning Calibration of the High-Frequency Airborne Microwave and Millimeter-Wave Radiometer (HAMMR) Instrument. IEEE Transactions on Geoscience and Remote Sensing. 2019; 58 (5):3391-3399.

Chicago/Turabian Style

Mehmet Ogut; Xavier Bosch-Lluis; Steven C. Reising. 2019. "Deep Learning Calibration of the High-Frequency Airborne Microwave and Millimeter-Wave Radiometer (HAMMR) Instrument." IEEE Transactions on Geoscience and Remote Sensing 58, no. 5: 3391-3399.