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This study provides a comprehensive, long-term evaluation of inter-sensor radiometric calibration biases for the NOAA OMPS Nadir and CrIS instruments using four complementary validation methodologies implemented within the Inter-Sensor Radiometric Bias Assessment (iSensor-RCBA) portal, a component of the STAR Integrated Calibration/Validation System. Overall, SDR data quality from the three OMPS Nadir instruments and three CrIS instruments aboard SNPP, NOAA-20, and NOAA-21 remains stable. The iSensor-RCBA portal has also proven to be a powerful diagnostic resource, enabling the detection of both new and previously unrecognized calibration issues and anomalies. Using the 32-day averaged difference method, we were the first to discover and identify the root cause of an inconsistency near 280 nm in inter-sensor radiometric biases between the SNPP and NOAA-20 OMPS NP instruments. The same method also revealed an unusual radiometric feature in NOAA-21 CrIS SDRs over the southern high latitudes during spring and summer. In addition, we derived decade-long degradation rates at 11 Metop-B GOME-2 wavelengths using an independent dataset—Simultaneous Nadir Overpass observations between SNPP OMPS and Metop-B GOME-2. Furthermore, iSensor-RCBA monitoring confirmed two geolocation anomalies in SNPP CrIS through a new approach involving SNO-based inter-sensor biases between GOES-16 ABI and SNPP CrIS. These cases demonstrate that iSensor-RCBA is not only a monitoring visualization tool but also a diagnostic tool that delivers unique, complementary insight into instrument performance, enabling early identification of radiometric and geolocation issues across JPSS and other satellite missions. Importantly, the analysis methods used in this study are broadly applicable to current and future missions, including JPSS-03, JPSS-04, and non-NOAA satellite systems.

Geospatial co-registration is a mandatory prerequisite when dealing with remote sensing data. Inter- or intra-sensoral misregistration will negatively affect any subsequent image analysis, specifically when processing multi-sensoral or multi-temporal data. In recent decades, many algorithms have been developed to enable manual, semi- or fully automatic displacement correction. Especially in the context of big data processing and the development of automated processing chains that aim to be applicable to different remote sensing systems, there is a strong need for efficient, accurate and generally usable co-registration. Here, we present AROSICS (Automated and Robust Open-Source Image Co-Registration Software), a Python-based open-source software including an easy-to-use user interface for automatic detection and correction of sub-pixel misalignments between various remote sensing datasets. It is independent of spatial or spectral characteristics and robust against high degrees of cloud coverage and spectral and temporal land cover dynamics. The co-registration is based on phase correlation for sub-pixel shift estimation in the frequency domain utilizing the Fourier shift theorem in a moving-window manner. A dense grid of spatial shift vectors can be created and automatically filtered by combining various validation and quality estimation metrics. Additionally, the software supports the masking of, e.g., clouds and cloud shadows to exclude such areas from spatial shift detection. The software has been tested on more than 9000 satellite images acquired by different sensors. The results are evaluated exemplarily for two inter-sensoral and two intra-sensoral use cases and show registration results in the sub-pixel range with root mean square error fits around 0.3 pixels and better.

This study evaluates mission-long inter-sensor radiometric calibration biases in Sensor Data Record (SDR) and/or Temperature Data Record (TDR) radiances from NOAA microwave sounders, including Advanced Technology Microwave Sounder (ATMS) (Suomi National Polar-orbiting Partnership or SNPP, NOAA-20, NOAA-21) and Advanced Microwave Sounding Unit-A (AMSU-A) (NOAA-19). Using four complementary validation techniques within the Inter-Sensor Radiometric Bias Assessment (iSensor-RCBA) system—32-day averaging, Community Radiative Transfer Model (CRTM) Double Difference (DD), Simultaneously Nadir Overpass (SNO), and sensor-DD via SNO—we characterize long-term performance. Results indicate that the SDR/TDR radiance quality remains stable and generally meets scientific requirements throughout their operational lifetimes with minimal anomalies; observed anomalies were infrequent and primarily correlated with calibration-table updates or spacecraft events or instrument degradation. Moreover, this research examines how radiometric calibration biases for the three ATMS instruments vary with Earth scene radiance or temperatures using the CRTM and SNO methods, as well as the radiance-dependency of inter-sensor calibration biases across the three instruments. Notably, due to its exceptional stability over 14 years, despite an approximate two-month data gap, the SNPP ATMS TDR and SDR datasets are recommended as the ideal reference to link legacy AMSU-A and Microwave Humidity Sounder (MHS) with Joint Polar Satellite System (JPSS), QuickSounder, and MetOp-Second Generation (MetOp-SG) microwave instruments. Beyond quantifying data quality, our multi-method framework with iSensor-RCBA effectively diagnosed critical issues, including a simulation error for CRTM ATMS radiance related to the CRTM spectral-response approximation and a NOAA-19 AMSU-A channel-8 performance anomaly. These findings confirm the long-term integrity of NOAA microwave sounder records and reinforce the value of integrated cross-sensor calibration assessments.

Sensor bias faults in closed-loop HVAC systems pose a detection challenge that is both subtle and costly. Because the control loop compensates for biased readings by driving the affected sensor back toward its setpoint, the fault becomes invisible to conventional threshold monitors. The anomaly does not vanish, however; it is redistributed across correlated sensors, disrupting their mutual consistency. We propose a framework that automatically derives virtual temperature sensor models from BRICK schema metadata. LightGBM regressors, trained on fault-free inter-sensor relationships, produce z-scored prediction residuals that serve as detection signals. Fault isolation is achieved by ranking sensors by their median daily anomaly scores; fault-type discrimination relies on analysis of actuator command-position discrepancies. On the Lawrence Berkeley National Laboratory (LBNL) fault detection and diagnosis (FDD) benchmark, the method achieves an area under the receiver operating characteristic curve (AUC) of 0.9992 for the mildest sensor bias (SA +2 °C), an AUC of 1.0 for all other single-duct air handling unit (SD-AHU) scenarios, and an AUC of 1.0 for all fan coil unit (FCU) sensor bias scenarios. In all four SD-AHU sensor bias scenarios, the biased sensor (SA_TEMP) ranks first or second; for the larger biases (±4 °C), SA_TEMP consistently ranks first. A robustness analysis over 10 random seeds confirms that detection AUC remains above 0.997 in all cases. Sensor and mechanical faults fall into non-overlapping clusters in the command-position discrepancy space. On the FCU system, the proposed method substantially outperforms principal component analysis (PCA) (AUC = 1.0 versus 0.63-0.90) and provides diagnostic capabilities not available with PCA. Notably, a single pipeline function handles both system types without modification, confirming cross-system scalability through the BRICK metadata layer. The results confirm that BRICK-automated virtual sensor construction is a viable approach for scalable, deployment-ready sensor validation in smart-building HVAC systems.

Two existing double-difference (DD) methods, using either a 3rdSensor or Radiative Transfer Modeling (RTM) as a transfer, are applicable primarily for limited regions and channels, and, thus critical in capturing inter-sensor calibration radiometric bias features. A supplementary method is also desirable for estimating inter-sensor calibration biases at the window and lower sounding channels where the DD methods have non-negligible errors. In this study, using the Suomi National Polar-orbiting Partnership (SNPP) and Joint Polar Satellite System (JPSS)-1 (alias NOAA-20) as an example, we present a new inter-sensor bias statistical method by calculating 32-day averaged differences (32D-AD) of radiometric measurements between the same instrument onboard two satellites. In the new method, a quality control (QC) scheme using one-sigma (for radiance difference), or two-sigma (for radiance) thresholds are established to remove outliers that are significantly affected by diurnal biases within the 32-day temporal coverage. The performance of the method is assessed by applying it to estimate inter-sensor calibration radiometric biases for four instruments onboard SNPP and NOAA-20, i.e., Advanced Technology Microwave Sounder (ATMS), Cross-track Infrared Sounder (CrIS), Nadir Profiler (NP) within the Ozone Mapping and Profiler Suite (OMPS), and Visible Infrared Imaging Radiometer Suite (VIIRS). Our analyses indicate that the globally-averaged inter-sensor differences using the 32D-AD method agree with those using the existing DD methods for available channels, with margins partially due to remaining diurnal errors. In addition, the new method shows its capability in assessing zonal mean features of inter-sensor calibration biases at upper sounding channels. It also detects the solar intrusion anomaly occurring on NOAA-20 OMPS NP at wavelengths below 300 nm over the Northern Hemisphere. Currently, the new method is being operationally adopted to monitor the long-term trends of (globally-averaged) inter-sensor calibration radiometric biases at all channels for the above sensors in the Integrated Calibration/Validation System (ICVS). It is valuable in demonstrating the quality consistencies of the SDR data at the four instruments between SNPP and NOAA-20 in long-term statistics. The methodology is also applicable for other POES cross-sensor calibration bias assessments with minor changes.

The Ozone Mapping and Profiler Suites (OMPS) Nadir Mapper (NM) is a grating spectrometer within the OMPS nadir instruments onboard the SNPP, NOAA-20, and NOAA-21 satellites. It is designed to measure Earth radiance and solar irradiance spectra in wavelengths from 300 nm to 380 nm for operational retrievals of the nadir total column ozone. This study presents calibration and validation analysis results for the NOAA-21 OMPS NM SDR data to meet the JPSS scientific requirements. The NOAA-21 OMPS SDR calibration derives updates of several previous OMPS algorithms, including the dark current correction algorithm, one-time wavelength registration from ground to on-orbit, daily intra-orbit wavelength shift correction, and stray light correction. Additionally, this study derives an empirical scale factor to remove 2.2% of systematic biases in solar flux data, which were caused by pre-launch solar calibration errors of the OMPS nadir instruments. The validation of the NOAA-21 OMPS SDR data is conducted using various methods. For example, the 32-day average method and radiative transfer model are employed to estimate inter-sensor radiometric calibration differences from either the SNPP or NOAA-20 data. The quality of the NOAA-21 OMPS NM SDR data is largely consistent with that of the SNPP and NOAA-20 OMPS data, with differences generally within ±2%. This meets the scientific requirements, except for some deviations mainly in the dichroic range between 300 nm and 303 nm. The deep convective cloud target approach is used to monitor the stability of NOAA-21 OMPS reflectance above 330 nm, showing a variation of 0.5% over the observed period. Data from the NOAA-21 VIIRS M1 band are used to estimate OMPS NM data geolocation errors, revealing that along-track errors can reach up to 3 km, while cross-track errors are generally within ±1 km.

Snow cover has significant impacts on the global water cycle, ecosystem, and climate change. At present, satellite remote sensing is regarded as the most efficient approach to detect long-term and multiscale observations of snow cover extent. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard Joint Polar Satellite System (JPSS) satellites will replace the Moderate-Resolution Imaging Spectroradiometer (MODIS) to prolong data recording in the future. Therefore, it is a fundamental task to analyze and evaluate the consistency of the snow cover products retrieved from these two sensors. In this study, we performed comparisons and a consistency evaluation between the MODIS and VIIRS snow cover products in three major snow distribution regions in China: Northeast China (NE), Northwest China (NW) and the Qinghai–Tibet Plateau (QT). The results demonstrated that (1) the normalized difference snow index (NDSI)-derived snow cover products showed suitable consistency between VIIRS and MODIS under clear sky conditions, with a mean difference value of less than 5%; (2) the VIIRS snow cover product presented much more snow and fewer clouds than that of MODIS in the snow season due to the differences in cloud-masking algorithms; (3) cloud mask strongly affects the potential of snow cover observation, and presents seasonal pattern in the test regions; and (4) VIIRS is able to distinguish clouds from snow with greater accuracy. The comparisons indicated that the greater the difference in cloud cover, the poorer the agreement in snow cover. This evaluation implies that perfecting the cloud-masking algorithm of VIIRS to update the MODIS would be the best solution to achieve better consistency for long-term and high-quality snow cover products.

This study carries out the calibration and validation of Antenna Temperature Data Record (TDR) and Brightness Temperature Sensor Data Record (SDR) data from the last National Oceanic and Atmospheric Administration (NOAA) Advanced Microwave Sounding Unit-A (AMSU-A) flown on the Meteorological Operational satellite programme (MetOp)-C satellite. The calibration comprises the selection of optimal space view positions for the instrument and the determination of coefficients in calibration equations from the Raw Data Record (RDR) to TDR and SDR. The validation covers the analyses of the instrument noise equivalent differential temperature (NEDT) performance and the TDR and SDR data quality from the launch until 15 November 2019. In particular, the Metop-C data quality is assessed by comparing to radiative transfer model simulations and observations from Metop-A/B AMSU-A, respectively. The results demonstrate that the on-orbit instrument NEDTs have been stable since launch and continue to meet the specifications at most channels except for channel 3, whose NEDT exceeds the specification after April 2019. The quality of the Metop-C AMSU-A data for all channels except channel 3 have been reliable since launch. The quality at channel 3 is degraded due to the noise exceeding the specification. Compared to its TDR data, the Metop-C AMSU-A SDR data exhibit a reduced and more symmetric scan angle-dependent bias against radiative transfer model simulations, demonstrating the great performance of the TDR to SDR conversion coefficients. Additionally, the Metop-C AMSU-A data quality agrees well with Metop-A/B AMSU-A data, with an averaged difference in the order of 0.3 K, which is confirmed based on Simultaneous Nadir Overpass (SNO) inter-sensor comparisons between Metop-A/B/C AMSU-A instruments via either NOAA-18 or NOAA-19 AMSU-A as a transfer.

Wireless Body Area Networks (WBANs) are increasingly employed in different medical applications, such as remote health monitoring, early detection of medical conditions, and computer-assisted rehabilitation. A WBAN connects a number of sensor nodes implanted in and/or fixed on the human body for monitoring his/her physiological characteristics. Although medical healthcare systems could significantly benefit from the advancement of WBAN technology, collecting and transmitting private physiological data in such an open environment raises serious security and privacy concerns. In this paper, we propose a novel key-agreement protocol to secure communications among sensor nodes of WBANs. The proposed protocol is based on measuring and verifying common physiological features at both sender and recipient sensors prior to communicating. Unlike existing protocols, the proposed protocol enables communicating sensors to use their previous session pre-knowledge for secure communication within a specific period of time. This will reduce the time required for establishing the shared key as well as avoid retransmitting extracted features in the medium and hence thwarting eavesdropping attacks while maintaining randomness of the key. Experimental results illustrate the superiority of the proposed key agreement protocol in terms of both feature extraction and key agreement phases with an accuracy of 99.50% and an error rate of 0.005%. The efficacy of the proposed protocol with respect to energy and memory utilization is demonstrated compared with existing key agreement protocols.

Snow on sea ice is a sensitive indicator of climate change because it plays an important role regulating surface and near surface air temperatures. Given its high albedo and low thermal conductivity, snow cover is considered a key reason for amplified warming in polar regions. This study focuses on retrieving snow depth on sea ice from brightness temperatures recorded by the Microwave Radiation Imager (MWRI) on board the FengYun (FY)-3B satellite. After cross calibration with the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) Level 2A data from January 1 to May 31, 2011, MWRI brightness temperatures were used to calculate sea ice concentrations based on the Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) algorithm. Snow depths were derived according to the proportional relationship between snow depth and surface scattering at 18.7 and 36.5 GHz. To eliminate the influence of uncertainties in snow grain sizes and sporadic weather effects, seven-day averaged snow depths were calculated. These results were compared with snow depths from two external data sets, the IceBridge ICDIS4 and AMSR-E Level 3 Sea Ice products. The bias and standard deviation of the differences between the MWRI snow depth and IceBridge data were respectively 1.6 and 3.2 cm for a total of 52 comparisons. Differences between MWRI snow depths and AMSR-E Level 3 products showed biases ranging between −1.01 and −0.58 cm, standard deviations from 3.63 to 4.23 cm, and correlation coefficients from 0.61 to 0.79 for the different months.