Uncalibrated Data Research Articles

European beech spring phenological phase prediction with UAV-derived multispectral indices and machine learning regression

Acquiring phenological event data is crucial for studying the impacts of climate change on forest dynamics and assessing the risks associated with the early onset of young leaves. Large-scale mapping of forest phenological timing using Earth observation (EO) data could enhance our understanding of these processes through an added spatial component. However, translating traditional ground-based phenological observations into reliable ground truthing for training and validating EO mapping applications remains challenging. This study explored the feasibility of predicting high-resolution phenological phase data for European beech (Fagus sylvatica) using unoccupied aerial vehicle (UAV)-based multispectral indices and machine learning. Employing a comprehensive feature selection process, we identified the most effective sensors, vegetation indices, training data partitions, and machine learning models for phenological phase prediction. The model that performed best and generalized well across various sites utilized Green Chromatic Coordinate (GCC) and Generalized Additive Model (GAM) boosting. The GCC training data, derived from the radiometrically calibrated visual bands of a multispectral sensor, were predicted using uncalibrated RGB sensor data. The final GCC/GAM boosting model demonstrated capability in predicting phenological phases on unseen datasets within a root mean squared error threshold of 0.5. This research highlights the potential interoperability among common UAV-mounted sensors, particularly the utility of readily available, low-cost RGB sensors. However, considerable limitations were observed with indices that implement the near-infrared band due to oversaturation. Future work will focus on adapting models to better align with the ICP Forests phenological flushing stages.

Global geologic expert system and database for predicting carbonate reservoirs from seismic: Application to the Upper Jurassic

Carbonate systems are influenced by a great variety of physical and biological controlling factors that operate from global to local scales. The resulting intrinsic complexity of carbonate platforms makes them difficult to predict, especially when data are limited. Predicting geologic geometries and properties based on limited sampling or uncalibrated seismic data generally relies on a priori knowledge and equivocal interpretations that are marked by geologist perception and personal experience. To overcome these uncertain interpretations of such a complex natural system, which can become critical in frontier exploration, we developed an expert system that relies on a process-based method and a standardized data set using normalized information and parameters. The main innovation relies on the realization of knowledge- and process-based synthetic carbonate stratigraphic architectures that support seismo-stratigraphic interpretations. The workflow consists of four steps: (1) bibliographic compilation of a geologic database for each case study supported by quantitative parameters (e.g., sedimentation duration and thickness) and qualitative parameters (geodynamic context, seismic architecture, and facies model); (2) statistical analyses to establish consistent geologic classes and spatiotemporal trends; (3) process-based modeling to simulate stratigraphic architectures associated with carbonate sedimentation processes in a physically constrained numerical environment and testing different geologic hypotheses; and (4) realization of a predictive palaeogeographic map representing the global distribution of carbonate stratigraphic architectures, and estimation of controlling parameters for unconstrained case studies. The expert system is based on 77 case studies of Upper Jurassic carbonate platforms, which reveal the resemblance of these carbonate systems, in response to uniform global palaeoclimatic conditions and sea level. Significant local differences in stratigraphic architectures are related to specific geodynamic contexts and subsidence trends. The thickest carbonate platforms are developed in extensive/passive geodynamic settings such as the Central Atlantic Ocean margins, while thinner platforms form in intra- and peri-cratonic settings such as those of the Arabian region.

Towards a hygroscopic growth calibration for low-cost PM2.5 sensors

Abstract. Low-cost particulate matter (PM) sensors continue to grow in popularity, but issues such as aerosol-size-dependent sensitivity drive the need for effective calibration schemes. Here we devise a time-evolving calibration method for the Plantower PMS5003 PM2.5 mass concentration measurements. We use 2 years of measurements from the Berkeley Environmental Air-quality and CO2 Network sensors deployed in San Francisco and Los Angeles in our analysis. The calibration uses a hygroscopic growth correction factor derived from κ-Köhler theory, where the calibration parameters are determined empirically using US Environmental Protection Agency Air Quality System (EPA AQS) reference data at co-location sites during the period from 2021–2022. The parameters are found to vary cyclically through the seasons, and the seasonal cycles match changes in sulfate and elemental carbon PM composition fractions throughout the year. In both regions, the seasonal RH dependence calibration performs better than the uncalibrated data and data calibrated with the EPA's national Plantower calibration algorithm. In the San Francisco Bay Area, the seasonal RH dependence calibration reduces the root mean square error (RMSE) by ∼40 % from the uncalibrated data and maintains a mean bias much smaller than the EPA national calibration scheme (−0.90 vs −2.73 µg m−3). We also find that calibration parameters forecasted beyond those fit with the EPA reference data continue to outperform the uncalibrated data and EPA calibration data, enabling real-time application of the calibration scheme even in the absence of reference data. While the correction greatly improves the data accuracy, non-Gaussian distribution of the residuals suggests that other processes besides hygroscopic growth can be parameterized for future improvement of this calibration.

Polarimetric SAR cross-calibration method based on stable distributed targets

ABSTRACT Polarimetric calibration is essential for the pre-processing of Polarimetric Synthetic Aperture Radar (PolSAR) data because it effectively mitigates polarimetric distortions in the measured PolSAR data. Traditional methods of polarimetric calibration employ man-made calibrators that offer high accuracy. However, the frequency of calibration is often limited due to the labor-intensive and time-consuming nature of deploying such calibrators. Some polarimetric calibration methods based on distributed targets in nature enable more frequent calibration. Nevertheless, these methods are constrained by the availability of specific distributed targets with known polarimetric properties for estimating parameters related to co-polarization channel imbalance (co-pol-imba) parameters. If distributed targets are not appropriately selected or suitable targets are absent within the image scene, the accuracy of calibration will be compromised. To address this challenge, this paper introduces the idea of cross-calibration, which uses calibrated PolSAR data to determine the real polarimetric property of distributed targets and cross-estimates co-pol-imba parameters for uncalibrated PolSAR data. Furthermore, considering the disparities in imaging geometry between calibrated and uncalibrated PolSAR data, the Cross-Co-Polarization Ratio (CCPR) coefficients are proposed to select stable distributed targets in both the calibrated PolSAR data and the uncalibrated PolSAR data. This approach not only takes into account the impact of co-pol-imba on feature extraction but can also be applied in a broader range of scenes. The real data experiments on the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data show that the estimated results of the cross-calibration method for co-pol-imba are comparable to the polarimetric calibration method based on man-made calibrators, with an amplitude difference of 0.17 dB and a phase difference of 0.69°.

A novel calibration method for continuous airborne metal measurements: Implications for aerosol source apportionment

Continuous metal monitors have been widely used in environmental monitoring due to the high temporal resolution, high detection limit, and necessity for near real-time source apportionment. However, the reliability of the conventional calibration method, the deviation caused by uncalibrated monitoring data, and the subsequent impact on source identification results are rarely discussed. In this study, a reliable multi-point calibration approach by Primary Standard Aerosol Mass Concentration Calibration System (PAMAS) for the Xact625i Ambient Metals Monitor was developed and applied. The measured data was almost meaningless in the low-concentration range with bias even exceeding 100 % by using the conventional single-point calibration method based on thin-film standards. PAMAS was utilized to generate aerosols with known concentrations of the 20 metal elements including Al, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Sr, Cd, Sn, Sb, Ba, Tl, Pb, and Bi, in two concentration ranges of 150–1200 ng m−3 and 2.5–30 ng m−3 to validate the Xact625i Monitor. The results showed that the elemental concentrations were underestimated, especially in the low-concentration range, only for Cr, As, and Sr with slopes close to unity (1.00 ± 0.03). After calibration by PAMAS, the slopes of the linear relationships between measured and standard concentrations were all unity for the 19 elements in the high-concentration range, and close to unity for the 15 elements in the low-concentration range, and the accuracy of the remaining elements was also improved. After considering the calibration of aerosol metal data, it was found the number of source factors and their contributions to metals and PM2.5 in Chongming Dongtan, China, based on the PMF model significantly changed. This study highlighted the need of developing reliable calibration methods for online aerosol monitoring instruments and implied that the source apportionment results could be biased without careful data calibration.

Data-driven Shape Sensing of Continuum Dexterous Manipulators Using Embedded Capacitive Sensor.

We propose a novel inexpensive embedded capacitive sensor (ECS) for sensing the shape of Continuum Dexterous Manipulators (CDMs). Our approach addresses some limitations associated with the prevalent Fiber Bragg Grating (FBG) sensors, such as temperature sensitivity and high production costs. ECSs are calibrated using a vision-based system. The calibration of the ECS is performed by a recurrent neural network that uses the kinematic data collected from the vision-based system along with the uncalibrated data from ECSs. We evaluated the performance on a 3D printed prototype of a cable-driven CDM with multiple markers along its length. Using data from three ECSs along the length of the CDM, we computed the angle and position of its tip with respect to its base and compared the results to the measurements of the visual-based system. We found a 6.6% tip position error normalized to the length of the CDM. The work shows the early feasibility of using ECSs for shape sensing and feedback control of CDMs and discusses potential future improvements.

Seasonally optimized calibrations improve low-cost sensor performance: long-term field evaluation of PurpleAir sensors in urban and rural India

Abstract. Lower-cost air pollution sensors can fill critical air quality data gaps in India, which experiences very high fine particulate matter (PM2.5) air pollution but has sparse regulatory air monitoring. Challenges for low-cost PM2.5 sensors in India include high-aerosol mass concentrations and pronounced regional and seasonal gradients in aerosol composition. Here, we report on a detailed long-time performance evaluation of a popular sensor, the Purple Air PA-II, at multiple sites in India. We established three distinct sites in India across land use categories and population density extremes (in urban Delhi and rural Hamirpur in north India and urban Bengaluru in south India), where we collocated the PA-II model with reference beta attenuation monitors. We evaluated the performance of uncalibrated sensor data, and then developed, optimized, and evaluated calibration models using a comprehensive feature selection process with a view to reproducibility in the Indian context. We assessed the seasonal and spatial transferability of sensor calibration schemes, which is especially important in India because of the paucity of reference instrumentation. Without calibration, the PA-II was moderately correlated with the reference signal (R2 = 0.55–0.74) but was inaccurate (NRMSE ≥ 40 %). Relative to uncalibrated data, parsimonious annual calibration models improved the PurpleAir (PA) model performance at all sites (cross-validated NRMSE 20 %–30 %; R2 = 0.82–0.95), and greatly reduced seasonal and diurnal biases. Because aerosol properties and meteorology vary regionally, the form of these long-term models differed among our sites, suggesting that local calibrations are desirable when possible. Using a moving-window calibration, we found that using seasonally specific information improves performance relative to a static annual calibration model, while a short-term calibration model generally does not transfer reliably to other seasons. Overall, we find that the PA-II model can provide reliable PM2.5 data with better than ±25 % precision and accuracy when paired with a rigorous calibration scheme that accounts for seasonality and local aerosol composition.

From BEYONDPLANCK to COSMOGLOBE: Preliminary WMAP Q-band analysis

We present the first application of the COSMOGLOBE analysis framework by analyzing nine-year WMAP time-ordered observations that uses similar machinery to that of BEYONDPLANCK for the Planck Low Frequency Instrument (LFI). We analyzed only the Q-band (41 GHz) data and report on the low-level analysis process based on uncalibrated time-ordered data to calibrated maps. Most of the existing BEYONDPLANCK pipeline may be reused for WMAP analysis with minimal changes to the existing codebase. The main modification is the implementation of the same preconditioned biconjugate gradient mapmaker used by the WMAP team. Producing a single WMAP Q1-band sample requires 22 CPU-hrs, which is slightly more than the cost of a Planck 44 GHz sample of 17 CPU-hrs; this demonstrates that a full end-to-end Bayesian processing of the WMAP data is computationally feasible. In general, our recovered maps are very similar to the maps released by the WMAP team, although with two notable differences. In terms of temperature, we find a ∼2 μK quadrupole difference that most likely is caused by different gain modeling, while in polarization we find a distinct 2.5 μK signal that has been previously referred to as poorly measured modes by the WMAP team. In the COSMOGLOBE processing, this pattern arises from temperature-to-polarization leakage from the coupling between the CMB Solar dipole, transmission imbalance, and sidelobes. No traces of this pattern are found in either the frequency map or TOD residual map, suggesting that the current processing has succeeded in modeling these poorly measured modes within the assumed parametric model by using Planck information to break the sky-synchronous degeneracies inherent in the WMAP scanning strategy.

What It Takes to Solve the Hubble Tension through Modifications of Cosmological Recombination.

We construct data-driven solutions to the Hubble tension which are perturbative modifications to the fiducial ΛCDM cosmology, using the Fisher bias formalism. Taking as proof of principle the case of a time-varying electron mass and fine structure constant, and focusing first on Planck CMB data, we demonstrate that a modified recombination can solve the Hubble tension and lower S_{8} to match weak lensing measurements. Once baryonic acoustic oscillation and uncalibrated supernovae data are included, however, it is not possible to fully solve the tension with perturbative modifications to recombination.

Bias correction of 20 years of IMERG satellite precipitation data over Canada and Alaska

Study regionWe define two northern study areas: one covering all of Canada and Alaska and a second, smaller subregion surrounding the Peace-Athabasca Delta for testing. Study focusThis study aims to use bias correction to improve satellite precipitation data over a relatively data-sparse high latitude region using a network of in-situ rain gauges. We evaluate the satellite data and derive a linear bias-elevation relationship and apply the correction with a digital elevation model at a monthly scale, and further disaggregate it to produce corrected data at a daily scale. New hydrological insights for the regionWe find that the underestimation in the satellite data increases linearly with increasing elevation, above 500 m a.s.l. at the continental scale and for all elevations at the regional scale. Bias also varies seasonally, with higher bias in summer and lower bias in winter. Compared with uncalibrated data, the monthly continental correction reduces absolute bias by 16% and the root mean squared error by 6%, while the daily continental correction improves absolute bias by 17% but degrades root mean squared error slightly by 2%. We conclude that applying elevation-based bias correction reduces systematic elevational bias in northern high-latitude satellite precipitation data.

<i>In-situ</i> Simple Rapid Calibration Method for the EC-5 Moisture Sensor

By modifying a previous simple rapid method, we propose an in-situ simple, rapid calibration method based on soil porosity and the highest raw sensor output (RAW) obtained after heavy rainfall during the monitoring period. According to the results of our verifying experiments for three soils, we concluded that the modified method is applicable to monitoring results, including previously obtained uncalibrated data, that satisfy three criteria: (1) the monitoring data must include at least a few peak RAW values of around 1,200-1,300, (2) the monitoring data must capture changes in the water content at appropriate intervals, and (3) it must be possible to estimate the soil porosity of the soil at the monitoring site.

Shallow Water Object Detection, Classification, and Localization via Phase-Measured, Bathymetry-Mode Backscatter

Detection, classification, and localization (DCL) techniques are being developed around the use of a phase-measuring sidescan sonar (PMSS) in very shallow waters. The instrument simultaneously collects co-located sidescan imagery and bathymetry in extreme shallow water environments (<1 m water depth). In addition to the bathymetry, an uncalibrated backscatter data set, referred to in this study as phase-measured, bathymetry-mode backscatter (BMB), is also collected. This BMB has been minimally addressed in the literature. This work aims to use the BMB to detect and differentiate between various objects on the seafloor, including unexploded ordnance (UXO), and placed marine debris, or ‘clutter’, such as lobster pots, boat propellers, and car tires. The differentiation from multiple seafloor types including mud, sand, and gravel and different types of objects occurred through various statistical analysis methods including binomial and multinomial logistic regression. These methods have been applied to create statistical regression models for several variables including phase-measured, bathymetry-mode backscatter amplitude, sounding distance from nadir, per-ping vessel roll, orientation offset between per-ping vessel heading and object orientation, and all combinations of these variables. These statistical tests produced maximum likelihood odds ratios of individual soundings being associated with the various seafloor and object types. Results from these analyses shows that DCL could be possible with phase-measured, bathymetry-mode backscatter from this PMSS system, though these results may not be representative for all bed types and phase-measuring systems.

Ubiquitous Domain Adaptation at the Edge for Vibration-Based Machine Status Monitoring

This article presents a ubiquitous domain adaptation (UDA) and generalizability technique for vibration-based automated machine status monitoring at the edge. The method significantly reduces the effects of signal noise artifacts and device/usage-specific vibration signatures using basic time-frequency domain signal operations and a lightweight ensemble of data-driven classifiers, allowing the method to be used for reliable domain-invariant status monitoring of motorized equipment. An experimental setup using vibration data from an air-cooled electric blender motor (source domain) is used to train an automated machine state identification classifier that can identify the operating states of an eccentric rotating mass vibration motor (target domain). Initial deployment of this method on target-domain motorized devices resulted in a machine status monitoring accuracy of at least 81.6% and a maximum training accuracy of almost 99% on known data of the source domain and 91.49% for unseen data in the target domain within an acceptable time frame. The performance of the proposed method is also comparable across platforms ranging from resource-constrained edge to a resource-rich cloud. This approach facilitates the use of noisy or uncalibrated sensor data in data-driven machine status monitoring tasks, therefore allowing for the development of reusable, low-cost monitoring systems that require meagre developmental effort, resulting in accelerated deployment times.

Improving GNSS-R Ocean Wind Speed Retrieval for the BF-1 Mission Using Satellite Platform Attitude Measurements

The receive antenna gain is needed to accurately calibrate the normalized bi-statistic radar cross section (NBRCS) measured by the BF-1 mission, which is a global navigation satellite system reflectometry (GNSS-R) constellation of two microsatellites and the first Chinese GNSS-R satellite mission. The instability of the satellite platform is the main cause of receive antenna gain errors. To obtain a high precision gain value, a calibration method that remaps the ocean surface detection location to the receive antenna pattern using satellite platform attitude measurements is proposed in this paper. Thirty-two orbits of Delay Doppler maps (DDMs) data, which were greatly disturbed by the attitude, are selected to test the effectiveness of the proposed algorithm. The accuracy of wind speed retrieval is analyzed, and results show that the data calibration algorithm is effective in reducing the wind speed retrieval error. Compared with the un-calibrated data, the data subjected to the calibration algorithm show a significant improvement of 19.33% in correlation coefficient and average decreases of 30.91% and 42.57% in root mean square error (RMSE) and mean bias error (MBE), respectively. Moreover, the comparison highlights that the influence of the satellite platform attitude disturbance on wind speed retrieval is abated significantly. The proposed approach can effectively improve the quality of GNSS-R measurements, allowing for a better understanding of global weather abnormalities and generally improving weather forecasting.

ATOP: An Attention-to-Optimization Approach for Automatic LiDAR-Camera Calibration via Cross-Modal Object Matching

Due to the difference of data modalities, it’s a very challenging task to find the feature correspondences between 2D and 3D data in LiDAR-Camera calibration. In existing works, the establishment of the cross-model correspondence is always simplified by specifically designing artificial targets or restricting the region of searching correspondences with the help of initial extrinsic parameters. To achieve automatic LiDAR-Camera calibration without prior knowledge, we propose a novel self-adaptive LiDAR-Camera calibration approach named <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATOP</b> which realizes a cascaded procedure of ATtention-to-OPtimization. In the attention stage, an attention-based object-level matching network called Cross-Modal Matching Network (CMON) is designed for finding the overlapped FOV(Field of View) between camera and LiDAR, and producing 2D-3D object-level correspondences. In the optimization stage, two cascaded PSO-based (Particle Swarm Optimization) algorithms, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Point</i> -PSO and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pose</i> -PSO, are designed to estimate the LiDAR-Camera extrinsic parameters. Different from previous works, the proposed calibration method does not require any artificial targets or initial pose guesses, therefore it can be applied to achieve online self-adaptive LiDAR-Camera calibration. Besides, this is the first work, to our best knowledge, to achieve object-level matching between uncalibrated camera and LiDAR data. Experimental results on both the collected datasets and KITTI datasets demonstrate the effectiveness of the proposed method.

GNSS Based Low-Cost Magnetometer Calibration.

With the development of MEMS sensors, the magnetometer has increasingly become a part of various wearable devices. The magnetometer measures the intensity of the magnetic field in all three axes, resulting in a 3D vector-direction and power. Calibration must be done before using a magnetometer, especially in wearable electronics, due to the low quality of the sensor and high proximity to other electromagnetic emission sources. Several magnetometer calibration algorithms exist in the literature, with most of them requiring multi-sided rotation. However, such calibration is highly impractical when the sensor is mounted on larger objects, e.g., vehicles, which cannot easily be rotated. Vehicles contain a large amount of ferromagnetic soft and hard material that affects the measured magnetic field. A magnetometer can be useful for an INS system in a car as long as it does not drift over time. This article describes how to calibrate a magnetometer using the GNSS motion vector. The calibration is performed using data from the initial section of the vehicle's trajectory. The quality of the calibration is then validated using the remaining section of the trajectory, comparing the deviation between the azimuth obtained by GNSS and by the calibrated magnetometer. Based on the azimuth and speed of the vehicle, we predicted the position of the vehicle and plotted the prediction on the map. The experiment showed that such calibration is functional. The uncalibrated data were unusable due to the strong effect of ferromagnetic soft and hard materials in the vehicle.

Calibration and Validation of CYGNSS Reflectivity through Wetlands’ and Deserts’ Dielectric Permittivity

The reflection of Global Navigation Satellite Systems (GNSS) signals, namely GNSS-Reflectometry (GNSS-R), has recently proven to be able to monitor land surface properties in the microwave spectrum, at a global scale, and with very low revisiting time. Moreover, this new technique has numerous additional advantages, including low cost, low power consumption, lightweight and small payloads, and near real-time massive data availability, as compared to conventional monostatic microwave remote sensing. However, the GNSS-R surface reflectivity values estimated through the bistatic radar equation, and the Fresnel coefficients have shown a lack of coincidence with real surface reflectivity data, mostly due to calibration issues. Previous studies have attempted to avoid this matter with direct regression methods between uncalibrated GNSS-R reflectivity data and external soil moisture content (SMC) products. However, calibration of GNSS-R reflectivity used in traditional inversion models is still a challenge, such as those to estimate SMC, freeze/thaw, or biomass. In this paper, a successful procedure for GNSS-R reflectivity calibration is established using data from the CYGNSS (Cyclone GNSS) constellation. The scale and bias parameters are estimated from the theoretical dielectric properties of water and dry sand, which are well-known and empirically validated values. We employ four calibration areas that provide maximum range limits of reflectivity, such as deserts and wetlands. The CYGNSS scale factor and the bias parameter resulted in a = 3.77 and b = 0.018, respectively. The derived scale and bias parameters are applied to the CYGNSS dataset, and the retrieved SMC values through the Fresnel reflection coefficients are in excellent agreement with the Soil Moisture Active Passive (SMAP) SMC product. Then, the SMAP SMC is used as a reference true value, and provides a standard linear regression with an R-square coefficient of 0.803, a root mean square error (RMSE) of 0.084, and a Pearson’s correlation coefficient of 0.896.

Implications of the S8 tension for decaying dark matter with warm decay products

Recent weak lensing surveys have revealed that the direct measurement of the parameter combination $S_8\equiv\sigma_8(\Omega_m/0.3)^{0.5}$ -- where $\sigma_8$ is a measure of the amplitude of matter fluctuations on 8 $h^{-1}$Mpc scales -- is $\sim3\sigma$ discrepant with the value reconstructed from cosmic microwave background (CMB) data assuming the $\Lambda$CDM model. In this article, we show that it is possible to resolve the tension if dark matter (DM) decays with a lifetime of $\Gamma^{-1} \simeq 55 \ \text{Gyrs}$ into one massless and one massive product, and transfers a fraction $\varepsilon\simeq 0.7 \ \%$ of its rest mass energy to the massless component. The velocity-kick received by the massive daughter leads to a suppression of gravitational clustering below its free-streaming length, thereby reducing the $\sigma_8$ value as compared to that inferred from the standard $\Lambda$CDM model, in a similar fashion to massive neutrino and standard warm DM. Contrarily to the latter scenarios, the time-dependence of the power suppression and the free-streaming scale allows the 2-body decaying DM scenario to accommodate CMB, baryon acoustic oscillation, growth factor and un-calibrated supernova Ia data. We briefly discuss implications for DM model building, galactic small-scale structure problems and the recent Xenon-1T excess. Future experiments measuring the growth factor to high accuracy at $0\lesssim z\lesssim1$ can further test this scenario.

MODIS Vegetation Continuous Fields tree cover needs calibrating in tropical savannas

Abstract. The Moderate Resolution Imaging Spectroradiometer Vegetation Continuous Fields (MODIS VCF) Earth observation product is widely used to estimate forest cover changes and to parameterize vegetation and Earth system models and as a reference for validation or calibration where field data are limited. However, although limited independent validations of MODIS VCF have shown that MODIS VCF's accuracy decreases when estimating tree cover in sparsely vegetated areas such as tropical savannas, no study has yet assessed the impact this may have on the VCF-based tree cover data used by many in their research. Using tropical forest and savanna inventory data collected by the Tropical Biomes in Transition (TROBIT) project, we produce a series of calibration scenarios that take into account (i) the spatial disparity between the in situ plot size and the MODIS VCF pixel and (ii) the trees' spatial distribution within in situ plots. To identify if a disparity also exists in products trained using VCF, we used a similar approach to evaluate the finer-scale Landsat Tree Canopy Cover (TCC) product. For MODIS VCF, we then applied our calibrations to areas identified as forest or savanna in the International Geosphere-Biosphere Programme (IGBP) land cover mapping product. All IGBP classes identified as “savanna” show substantial increases in cover after calibration, indicating that the most recent version of MODIS VCF consistently underestimates woody cover in tropical savannas. We also found that these biases are propagated in the finer-scale Landsat TCC. Our scenarios suggest that MODIS VCF accuracy can vary substantially, with tree cover underestimation ranging from 0 % to 29 %. Models that use MODIS VCF as their benchmark could therefore be underestimating the carbon uptake in forest–savanna areas and misrepresenting forest–savanna dynamics. Because of the limited in situ plot number, our results are designed to be used as an indicator of where the product is potentially more or less reliable. Until more in situ data are available to produce more accurate calibrations, we recommend caution when using uncalibrated MODIS VCF data in tropical savannas.

Magnetic Field-Enhanced Learning-Based Inertial Odometry for Indoor Pedestrian

Pedestrian dead-reckoning (PDR) is a vital technique in pedestrian localization. Compared with traditional PDR, learning-based inertial odometry has the advantages of smaller position drift and is insensitive to pedestrian motion patterns. However, the heading drift of the trajectory is still the dominant error source for the position error drift in these methods. This study focuses on providing a pedestrian trajectory estimation method with low drift by properly fusing learned-based inertial odometry and magnetometer measurements under an indoor scenario containing significant magnetic field disturbance. The proposed method reduces the impact of magnetic field disturbance by adopting a long-term average magnetic vector, which is far more stable than using local magnetic vectors. Meanwhile, the proposed method can estimate the magnetometer bias online rather than depending on precalibrated magnetometer measurements. The test results show that the proposed method can obtain superior positioning performance using uncalibrated raw magnetometer data compared to other methods, even using calibrated magnetometer data. Simultaneously, this method achieves a balance between algorithm accuracy and efficiency.