Radiometers play a crucial role in providing accurate geo-physical information, relying heavily on precise calibration for both radiometric accuracy and spectral consistency. Radiometers consistently allocate time and hardware resources to calibration, resources that could otherwise be utilized for environmental sensing. In addition, calibration faces challenges such as frequency dependence and environmental influences, requiring to the need for innovative solutions. In this study, advancements in deep learning (DL) techniques are utilized, using NASA’s Soil Moisture Active Passive (SMAP) satellite data to create a DL-based radiometer calibrator. The use of 2-D spectral features as input in a convolutional neural network shows promising results with high correlation and low error. Notably, ancillary features like internal thermistor temperature prove accurate for estimating antenna temperature. This compensates for changes in receiver noise temperature and short-term gain fluctuations, even when there’s no reference load or noise diode power. The proposed calibration technique, emphasizing reduced reference information, holds significant potential for a higher number of antenna scene observations within a footprint.

The Smart Ice Cloud Sensing (SMICES) is an active/passive sensor. SMICES is sponsored by NASA Earth Science Technology Office (ESTO) under Instrument Incubator Program 19 (IIP-19) awarded to Northrop Grumman Corporation (NGC) and Jet Propulsion Laboratory (JPL). The instrument is designed to measure upper tropospheric and lower stratospheric cloud ice and water vapor. SMICES uses a suite of passive radiometers that are constantly conically scanning to locate ice clouds. The ice clouds are located using an artificial intelligence controller that identifies key labels related to the ice cloud. Once an ice cloud is identified, the artificial intelligence controller activates and targets the on-board radar. While the SMICES instrument is currently being developed for an airborne demonstration, the final goal is to deploy it as a small satellite (SmallSat) instrument in low-Earth orbit (LEO). The onboard AI controller will significantly reduce DC power consumption of the satellite mission. This will enable the SMICES system to be hosted on a smaller platform with fewer solar cells and significantly drive down mission costs while maintaining the quality of scientific data. This work presents the latest development on the SMICES microwave radiometer.

The retrieval of soil moisture (SM) under forest canopy has long been an important goal for low frequency remote sensing. The NASA Soil Moisture Active Passive (SMAP) mission is engaged at three separate experiment sites to improve its SM retrieval algorithm in forested areas. Two of the sites are located in the deciduous forest region in Massachusetts and New York, US and one is located in southern boreal forest zone in Saskatchewan, Canada. Each site has a SM measurement network of 20-25 stations spread out over an area of about 30 km, which covers the SMAP radiometer footprint. In 2022, intensive observations will be carried out at each site which involve deployments of an airborne instrument, which is similar to the SMAP instrument, and intensive manual measurements of SM, surface and vegetation. The measurements also include tower-based radiometer observations with ground truth measurements within the instrument footprint. Here we show some early results using the networks and SMAP measurements to analyze the sensitivity of the SMAP L-band measurements to SM changes in forested area and the impact of the vegetation to the signal. The results suggest an upper limit for vegetation attenuation accounting for surface roughness effect and relate that to the values used in the current SMAP SM products.

The Smart Ice Cloud Sensing (SMICES) instrument aims at providing onboard smart autonomous observation of upper tropospheric water vapor and ice particle size distribution in clouds at various local times. SMICES is an active/passive combined sensor with sounding channels at 380 GHz, radiometric channels at 250, 310 and 670 GHz, and a radar instrument operating at 239 GHz. A low-noise, low-power radiometer command and data handling (C&DH) subsystem has been designed to acquire the 24 analog radiometer channels and 8 analog thermistor data. A radiometric power regulation system provides the required power supplies for the other radiometric subsystems of the SMICES instrument. An on-board FPGA provides command and control of other instrument subsystems, performs synchronous data acquisition. The radiometer electronics are designed to fit into less than 2U horizontal dimensions of a CubeSat instrument. An AI controller unit directly interfacing with radar and radiometer C&DH subsystems performs on-board artificial intelligence operations for full system autonomy. The AI unit will control the radar instrument depending on the system health conditions, including the battery level, and based on the observed scene through the radiometer instrument.

The ultra-wideband spectroradiometer instrument aims at measuring the brightness temperature gradient in the upper lunar regolith using a wideband passive microwave spectrometer covering a continuous band from 300 MHz to 6 GHz. As a part of the Development and Advancement of Lunar Instrumentation (DALI) program, the designed ultra-wideband spectrometer is expected to provide lunar heat flux measurements. Difficulty in RF matching across the ultra-wideband and lack of isolators covering the large bandwidth make it challenging to design and calibrate the instrument. The heat-flow spectroradiometer instruments employ internal calibration sources for tracking and detecting mismatch changes in addition to gain variations measurements for stable and reliable radiometric operation.

A spaceborne P-band signals of opportunity synthetic aperture radar concept is proposed for the remote sensing of terrestrial snow. We have completed a performance analysis assuming a formation flight of 3 to 5 SmallSats on one orbit plane. The spacing between the SmallSats is chosen so that their ground tracks will be separated by 50 to 100 m to allow the use of interferometric synthetic aperture radar processing technique to obtain a spatial resolution of a few hundred meters. A point system design has been completed to determine the antenna concept and to indicate the dependence of spatial resolution and signal to noise ratio on the number of receivers. The performance for range delay determination was analyzed to assess the impact of various error sources, including instrument receiver noise and ionospheric delay. The dominant error source is the ionospheric delay, which will be corrected using the split-spectrum algorithm. Our overall error budget analysis indicates that an accuracy of about 3 cm for the snow water equivalent in dry snow and 5 cm for the snow depth of wet snow can be achieved.

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.

A new wideband radiometer covering P/L-band was developed at the Jet Propulsion Laboratory for polar ocean salinity and seasonal sea-ice thickness measurements. The instrument was deployed on the US Coast Guard Cutter Healy for an Arctic Ocean research cruise from September 13, 2018 to October 20, 2018. This work shows the first results relating sea ice thickness obtained from the measurements taken with the wideband P/L-band radiometer during the campaign. Results from the Artic cruise campaign were also used to study wideband spectral properties of salinity. In addition to this paper, Salinity and wide-band calibration challenges are presented in two other companion papers.

Global observations with information content on water vapor content, ice water content and ice particle size distribution are needed to enhance knowledge of the impact of ice clouds on Earth's weather and climate. These observations may also help to reduce the uncertainty of global climate models. The Tropospheric Water and Cloud Ice (TWICE) microwave radiometer instrument has been designed to perform temperature and humidity sounding of the atmosphere near the 118.75, 183.31 and 380.20 GHz atmospheric absorption lines, as well as to retrieve ice cloud particle size information from radiometric measurements at 240, 310, 670 and 850 GHz. To acquire high-quality data, the TWICE instrument performs end-to-end, on-orbit calibration of all radiometer channels during each scan. The TWICE instrument is designed to fit within the mass, volume and power constraints of the 6U CubeSat platform.

A new ultra-wide P/L-band radiometer instrument has been developed at the Jet Propulsion Laboratory for polar ocean salinity and seasonal sea-ice thickness measurements. The Arctic field campaign performed with the instrument deployed on the US Coast Guard Cutter Healy from September 13, 2018 to October 20, 2018. A new calibration strategy is developed for the ultra-wide band instrument to minimize the mismatch related effects. A noise-wave model is built to analyze and validate the instrument behavior for the calibration. The calibration strategy is analyzed using the results from the cruise campaign. Sea-ice thickness and sea surface salinity are presented in two other companion papers.

Deep learning artificial neural network techniques can be applied for on-orbit calibration of microwave and millimeter-wave radiometer spaceborne instruments, including those for small satellites. The noise-wave model has been employed for noise characterization and validation of the proposed deep learning calibration technique for a synthetically generated Dicke-switching radiometer. The developed deep learning neural network radiometer calibrator produces high accuracy estimates of antenna temperatures from the measurements of radiometer output voltage and thermistor readings. Tests with noise-free and noisy samples of the developed model have shown that the proposed calibration method does not add any significant noise to the radiometer calibration. The performance of the proposed method does not degrade with increased nonlinearity for a radiometer, while nonlinearity is a challenging issue for conventional calibration techniques. The deep learning calibration model learns the radiometer noise characteristics from radiometer prelaunch measurements during thermal vacuum chamber testing. The neural network calibrator proposed in this paper has self-learning capability during the on-orbit operation of a radiometer that can be used to improve the performance of on-orbit calibration. The proposed technique is demonstrated by comparing the residual uncertainty of the deep learning calibration with the theoretical value. No numerical study is presented to compare the performance with conventional calibration techniques. The new method may be solely applied to calibrate the radiometer or applied along with conventional calibration techniques.

The Tropospheric Water and Cloud ICE (TWICE) millimeter and sub-millimeter radiometer instrument is being developed to enable global observations of upper tropospheric/lower-stratospheric water vapor and ice particle size distribution in clouds. Global observations using the TWICE instrument are critically needed to reduce uncertainties in weather and climate models. A low-noise, power-efficient command and data handling (C&DH) subsystem has been designed and tested to control TWICE data acquisition and other subsystems. Considering the limited power resources available on such platforms, a highly-efficient power regulation board has been designed to minimize power losses and reduce system noise. Furthermore, heavy-ion radiation testing has been performed for some critical commercial-off-the-shelf components to analyze radiation tolerance in low-Earth orbit. The C&DH prototype board meets the functional, noise and size, weight and power (SWaP) requirements for deployment on a 6U -Class satellite.

Global measurements of upper tropospheric/lower-stratospheric water vapor and ice particle size distribution in clouds are critically needed to reduce uncertainties in global weather and climate models. To address this need, the conically scanning Tropospheric Water and Cloud ICE (TWICE) millimeter and submillimeter radiometer instrument is being developed. A low-noise, power-efficient command and data handling (C&DH) subsystem has been designed to control TWICE data acquisition and other subsystems. The C&DH prototype board meets functional, noise and size, weight and power (SWaP) requirements for deployment in a 6U-class satellite. Considering the limited power resources available on such platforms, a highly-efficient power regulation board has been designed to minimize power losses and reduce system noise. Furthermore, all of the components have been tested for radiation tolerance in low-Earth orbit.