Temporal Anomalies Research Articles

Surface-wave coupling in double Floquet sheets supporting phased temporal Wood anomalies

Abstract We investigate symmetry-selective surface-mode excitation in a general periodically time-modulated double-layer system, where the modulation of the two layers has a constant phase difference. By deriving a semi-analytic transfer matrix formalism of a Drude-dispersive double-layer structure with periodic time-modulation, we calculate the scattering amplitudes and the corresponding transmission coefficient. Our results show that the phase-difference between the modulation of the two sheets plays an essential role in significantly enhancing and selectively exciting either the even or odd surface mode with high efficiency. We verify our calculations with full-wave time-domain simulations, showing that efficient switching between the surface-wave excitation of the two distinct modal channels can be achieved, even under illumination from a single off-resonant Gaussian pulse, by controlling the phase difference between the two modulations. Our results pave the way towards ultrafast, symmetry-selective mode excitation and switching via temporal modulation.

Multisource and temporal thermal infrared remote sensing of Hasandağ Stratovolcano (Central Anatolia, Turkey)

Hasandağ is a double-peaked long-dormant volcano, where associated volcanic risk is poorly known and needs to be assessed. Weak fumaroles and water vapor emissions have been observed at several vents, on the western flank of the Greater Hasandağ summit, between 3000 and 3100 m (a.s.l.). The highest temperature and CO2 values measured in those vents were ~ 70 °C and > 100,000 ppm respectively. Multisource and wide-period thermal remote sensing analyses were performed to investigate the thermal state and to test the possibilities of remote thermal monitoring of the volcano.MODIS daily surface temperature data were analyzed for a period of 18 years. We used a statistical approach using the Seasonal Decomposition of Time-series procedure to decompose the temperature data into the trend, seasonal, and remainder components. The trend component was used to analyze temporal thermal anomalies in comparison to the decomposed meteorological temperature data. 56 nighttime ASTER TIR imagery acquired between 2001 and 2018 had been used to derive and analyze the Surface temperature, Surface temperature anomaly and Relative radiative heat flux. High-resolution ground-based and aerial thermal mosaic images were built to investigate the thermal radiation over the summit fumarole zone and its structural dependence.A baseline reference value for thermal monitoring of the fumarole zone was achieved using remotely sensed data and time-series decomposition of long-term TIR data. The resolution of the thermal imagery positively affects the mapping and monitoring capability. A permanent ground-based thermal observation station should be established for direct and remote thermal monitoring of the fumarole zone.

18F-FDG Brain PET Findings in Narcolepsy Type 2.

A 16-year-old adolescent boy with extensive travel throughout West Africa presented with a 6-year history of social withdrawal, anhedonia, and daytime sleepiness. The patient's electroencephalography was normal. Initial MRI revealed small pituitary gland and left temporal developmental venous anomaly. Subsequently obtained 18F-FDG brain PET was notable for markedly severe hypometabolism in the brainstem. Further workup revealed a normal orexin, autoimmune encephalitis panel, and negative titers for Trypanosoma brucei and cruzi in the CSF. Outpatient sleep study showed mild obstructive sleep apnea, and multiple sleep latency test revealed reduced mean sleep latency at 7 minutes with sleep-onset REM in 3/5 naps, findings consistent with narcolepsy type 2.

How morning soil moisture conditions influence afternoon precipitation events in South America

AbstractThe land–atmosphere interactions play an important role in the South American climate system. Previous studies have explored the coupling between soil moisture (SM), surface heat fluxes and atmospheric variables such as temperature and precipitation; however, the SM–precipitation feedback is still debated, especially at the diurnal scale. We computed three diurnal local coupling diagnostics between spatial, temporal and heterogeneity morning SM anomalies and the occurrence of afternoon precipitation events. We analyse South America in the period October–March 1983–2012 with the ERA5 reanalysis and the RCA4 regional climate model. Two 30‐year climate simulations are performed: a control and an uncoupled simulation where the SM is prescribed with daily climatological values, limiting the SM–atmosphere interaction. Comparing both simulations we isolate the effect of SM variability, allowing us to confirm that the diagnostics are indeed showing the contribution of the SM–precipitation coupling. We test different methods of calculating the morning SM anomalies and observe that all diagnostics show some methodological sensitivity, especially the spatial and temporal diagnostics. Despite the differences, in eastern Brazil we find a preference for afternoon precipitation to develop when the soil is wetter and heterogeneous, over points that are wetter than their surroundings. Towards southeastern South America, there is a spatial and temporal preference for drier or average conditions, depending on the method, with heterogeneity preference, and the same is observed in southwestern Amazonia in RCA4. Ultimately, it is important that future studies disentangle the spatiotemporal coupling mechanisms that may act simultaneously into its distinct components to avoid erroneous assumptions and contradictory results.

Are the Significant Ionospheric Anomalies Associated with the 2007 Great Deep-Focus Undersea Jakarta–Java Earthquake?

This work is an attempt to critically analyze the correlation between great deep-focus undersea earthquake and possible ionospheric anomalies. The significant TEC (total electron content) temporal and spatial anomalies were detected over the epicenter of 2007 Mw7.5 Jakarta–Java earthquake, and they coincide well with the striking plasma anomalies in the ionosphere in situ observed by the LEO (low Earth orbit) satellite. The localization and synchronization of the disturbances during the earthquake suggest that these ionospheric anomalies are highly related to this large undersea earthquake. In order to identify this correlation, we made efforts to distinguish seismo-associated signals from large electromagnetic noise due to solar and geomagnetic activities, natural non-seismic sources and known artificial signals. Nevertheless, the difficulties of this work should be recognized and approached with caution. This special (deep-focus and undersea) case study here provides us with valuable information on the study of lithosphere–atmosphere–ionosphere (LAI) coupling process and reminds us that deep-focus undersea seismic events need to be treated more carefully and discreetly in the future.

Groundwater monitoring near oil sands development: Insights from regional water quality datasets in the Alberta Oil Sands Region (AOSR)

Study regionThis study is carried out across a 142,000 km2 area within the Alberta Oil Sands Region (AOSR), Alberta, Canada. Study focusGroundwater quality data for the AOSR are compiled and interpreted to provide information on regional water quality to inform groundwater monitoring and land use planning. A database of 546 water quality parameters measured between 1958 and 2015 from 5118 water wells is compiled, cleaned, and analyzed by hydrostratigraphic unit (HSU). New hydrologic insights for the regionBaseline water quality conditions were found to vary in the 12 main HSU’s, with wide ranges in total dissolved solids and geochemical facies, reflecting variable lithology and geochemical processes. Median concentrations for multiple parameters exceeded “interim trigger values” under consideration by government regulators. Statistically significant temporal changes in water quality were detected in the 2000’s in isolated areas of Surficial Sands aquifer, the Cretaceous and Devonian formations in the North Athabasca Oil Sands, and in Quaternary aquifers in the South Athabasca Oil Sands and Cold Lake Beaver River. Temporal anomalies occur in areas with enhanced vertical connectivity due to the presence of buried channels, incised rivers, or where the Colorado Group is thin or completely absent. The compiled dataset highlights the role of geochemical data in identifying aquifer connectivity and monitoring priority. Lack of publicly available data for key aquifers near some mining areas are noted.

A hybrid deep learning approach for driver anomalous lane changing identification

Reliable knowledge of driving states is of great importance to ensure road safety. Anomaly detection in driving behavior means recognizing anomalous driving states as a direct result of either environmental or psychological factors. This paper provides an efficient anomaly recognition approach to identify anomalous lane-changing events in a personalized manner. The proposed framework includes three unsupervised algorithms. First, a Recurrent-Convolutional Autoencoder extracts the spatio-temporal characteristics from a high-dimensional naturalistic driving dataset. Second, in order to recognize anomalous lane-changing events of individual drivers, the extracted latent feature space is analyzed using Pauta criterion-based reconstruction loss analysis, as well as one-class Support Vector Machine. Last, t-Distributed Stochastic Neighbor Embedding is employed to visualize the latent space for better understanding and interpretability. Temporal anomalies of lane-changing events were analyzed by a personalized grey relational coefficient analysis, to represent robust similarities for individual drivers. Validation and calibration were performed with a natural driving study dataset collected from 50 drivers with 59,372 lane change events. The results showed heterogeneity in the pattern of abnormal lane changing behavior across the sample. At the same time, each driver exhibited heterogeneous anomalous behaviors in both temporal and spatial sequences. Without prior labels, the proposed model effectively captures personalized driving patterns and abnormal lane-changing events from high-dimensional time-series data. This unsupervised hybrid approach is a novel attempt to complete personalized anomalous lane-changing behaviors identification based on naturalistic driving data involving various traffic environments. Our approach enables the extraction of natural individual lane-changing behavior patterns and provides insights for the improvement of personalized driving behavior monitoring systems.

Unsupervised dam anomaly detection with spatial–temporal variational autoencoder

The anomaly detection and health monitoring of dams have attracted increasing attention. To detect the temporal and spatial anomalies of the dam, a novel spatial–temporal variational autoencoder is proposed. The proposed model is based on the sequential variational autoencoder, and its backbone is fulfilled by the recurrent neural network and graph convolutional network to capture the temporal and spatial features in both the generative and inference models. To obtain a normal pattern, we made an assumption that the normal values should be temporally smooth and spatially similar. Then, the smoothness and similarity-inducing operations are used in the framework of the proposed model. Through the addition of smoothness and similarity losses in sequential variational autoencoder, the proposed model can obtain a temporally smooth and spatially similar pattern. For verification, an arch dam is taken as an example. Through comparison among six baseline models, the proposed model detects the temporal and spatial anomalies accurately and stably.

A multi-metric assessment of drought vulnerability across different vegetation types using high resolution remote sensing

Drought impact monitoring is of crucial importance in light of climate change. However, we lack an understanding of the concomitant responses of ecosystems to a variety of drought characteristics and the links between drought and ecosystem anomaly characteristics for a comprehensive set of vegetation types to provide needed information for water management. In response, this study presents a new framework that allows us to explore the relationship between drought and its impact on ecosystems in greater detail. Specifically, our framework focuses on estimating jointly the hydrological and ecosystem temporal evolution and anomalies around a drought event using four pairs of metrics: onset-onset, duration-duration, intensity-intensity, and severity-severity of drought and vegetation damage. Additionally, we incorporated a metric on vegetation vulnerability based on changes in damage severity along a gradient of increasing drought severity. Based on this framework, we evaluated drought vulnerability patterns of various vegetation types across the Netherlands and Belgium in 2018 at high spatiotemporal resolution. Our results reveal a differential vulnerability of vegetation between ecosystems with increasing drought severity, which could aid future drought impact predictions. In particular, mosaic grasslands and tree/shrub croplands are highly sensitive to increasing drought severity. Individual characteristics (onset, duration, intensity and severity) of drought and vegetation damage behave differently in various vegetation types. For instance, broadleaved forests respond faster than other forests, while mixed forests suffer less damage than other types. The early warning threshold to drought for most vegetation types is around a Standardized Precipitation Evapotranspiration Index (SPEI) value of −1. The characterization of a suite of drought response characteristics through our impact analysis framework can be used in a wide variety of regions to understand current and possible future responses to drought.

Temporal and spatial changes in the b-value prior to the 2021 Luxian M S 6.0 earthquake in Sichuan, China

Using the seismic data collected since 2010 by the China Earthquake Networks Center and based on the maximum likelihood method, we analyzed the temporal and spatial anomalies of the b-value prior to the M S 6.0 Luxian earthquake on September 16, 2021. The results are as follows. (1) The Luxian earthquake broke the historical record of no M ≥ 6 earthquake occurring in the Huayingshan fault zone, revealing that the Huayingshan fault zone still has the seismogenic ability of medium and strong earthquakes. (2) Before the M S 6.0 Luxian earthquake, the b-values around the focal areas exhibited increasing–peaking–decreasing anomalous characteristics, indicating that this is an effective method of predicting the occurrence of moderate to strong earthquakes in the region. (3) In the past 10 years, the b-values of the middle segment of the Huayingshan fault zone have been relatively low, but the b-value of this section may be in an abnormal stage of slow increase, indicating that this segment may be in a stage of stress accumulation and concentration and is preparing for moderate to strong earthquakes. The middle segment of the Huayingshan fault zone may become the site of moderate to strong earthquakes in the future. We should continue to pay attention to it.

A comparison of proximity-based methods for detecting temporal anomalies in business processes

A comparison of proximity-based methods for detecting temporal anomalies in business processes

Detection of Small Moving Targets in Cluttered Infra-red Imagery

Deep convolutional neural networks can achieve remarkable results for detecting and recognizing large and medium sized objects in visible band color images. However, the ability to detect small objects has yet to achieve the same level performance. Our focus is on applications that require the accurate detection and localization of small moving objects that are distant from the sensor. State of the art object detection and classification networks (such YOLOv3 &#x005B;1&#x005D; and Mask R-CNN &#x005B;2&#x005D;) are not well suited for finding small moving objects in infra-red imagery, nor do they handle temporal information in video sequences. To overcome the limitations of these methods, we propose the Moving Target Indicator Network (MTINet), a new model specifically designed for detecting small moving targets in clutter. Several versions of the MTINet are presented with different refinements (such as spatial pyramid blocks and improved attention mechanism) to increase the probability of detection and reduce false alarm rates. The MTINet is trained to maximize the <i>target to clutter ratio</i> (TCR) metric, which represents the first use of the TCR loss function for detecting moving objects. The further challenge the MTINet, we also evaluated its performance with simulated sensor movement mimicking effects of image stabilization processes. Finally, we also propose a modification of the Reed-Xiaoli detector &#x005B;3&#x005D; (originally developed for anomaly detection in hyperspectral data) to enable it to detect temporal anomalies caused by moving objects, and refer to it as the temporal-ARX (t-ARX) algorithm. We find that the t-ARX algorithm can achieve better probability of detection at lower false alarm rates, while the MTINet is superior at finding difficult targets at higher false alarm rates. We then show that the combination of the two algorithms achieves the best overall performance.

Ionospheric TEC and plasma anomalies possibly associated with the 14 July 2019 &lt;i&gt;M&lt;sub&gt;w&lt;/sub&gt;&lt;/i&gt;7.2 Indonesia Laiwui earthquake, from analysis of GPS and CSES data

This study presents signatures of seismo-ionospheric perturbations possibly related to the 14 July 2019 <inline-formula><tex-math id="M2">\begin{document}$ {M}_{w} $\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="RA305-wenyuanzheng-F_M2.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="RA305-wenyuanzheng-F_M2.png"/></alternatives></inline-formula>7.2 Laiwui earthquake, detected by a cross-validation analysis of total electron content (TEC) data of the global ionospheric map (GIM) from GPS and plasma parameter data recorded by the China Seismo-Electromagnetic Satellite (CSES). After separating pre-seismic ionospheric phenomena from the ionospheric disturbances due to the magnetospheric and solar activities, we have identified three positive temporal anomalies, around the epicenter, at 1 day, 3 days and 8 days before the earthquake (14 July 2019), along with a negative anomaly 6 days after the earthquake. These results agree well with the TEC spatial variations in latitude–longitude–time (LLT) maps. To confirm these anomalies further, we employed the moving mean method (MMM) to analyze ionospheric plasma parameters (electron, <inline-formula><tex-math id="M3">\begin{document}$ {\mathrm{O}}^{+} $\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="RA305-wenyuanzheng-F_M3.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="RA305-wenyuanzheng-F_M3.png"/></alternatives></inline-formula>and <inline-formula><tex-math id="M4">\begin{document}$ {\mathrm{H}\mathrm{e}}^{+} $\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="RA305-wenyuanzheng-F_M4.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="RA305-wenyuanzheng-F_M4.png"/></alternatives></inline-formula>densities) recorded by the Langmuir probe (LAP) and Plasma Analyzer Package (PAP) onboard the CSES. The analysis detected on, on Day Two, Day Four, and Day Seven before the earthquake, remarkable enhancements along the orbits around when in proximity to the epicenter. To make the investigations still more convincing, we compared the orbits on which anomalous readings were recorded to their corresponding four nearest revisiting orbits; the comparison did indeed indicate the existence of plasma parameter anomalies that appear to be associated with the Laiwui earthquake. All these results illustrate that the unusual ionospheric perturbations detected through GPS and CSES data are possibly associated with the <inline-formula><tex-math id="M5">\begin{document}$ {M}_{w}7.2 $\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="RA305-wenyuanzheng-F_M5.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="RA305-wenyuanzheng-F_M5.png"/></alternatives></inline-formula> Laiwui earthquake, which suggests that at least some earthquakes may be predicted by alertness to pre-seismic ionospheric anomalies over regions known to be at seismic risk. This case study also contributes additional information of value to our understanding of lithosphere–atmosphere–ionosphere coupling.

Detecting temporal anomalies with pseudo age groups: Homeownership in Canada, 1981 to 2016

AbstractMethodological advances in demographic research, especially age‐period‐cohort (APC) analysis, primarily focus on developing new models yet often fail to consider practical concerns in empirical analysis. We propose a mixed approach that integrates multiple data imputation and structural change analysis in time series so that scholars can (i) construct pseudo age groups based on more coarsely grouped age data and (ii) identify temporal anomalies. This approach is illustrated using multiple waves of Canadian Population Census data (1981–2016). We construct pseudo age groups based on more coarse age information available in the Census data and identify a local anomaly in the temporal trajectory of homeownership in Canada's less populous provinces and territories. These findings are assessed and validated in comparison with results from more populous Canadian provinces. This research broadens the methodological repertoire for demographers, geographers, and social scientists in general and extends the literature on homeownership in an understudied area.

Neuroradiological findings in Alagille syndrome.

Alagille syndrome (ALGS) is a multisystemic disease caused by mutations in genes of Notch pathway, which regulates embryonic cell differentiation and angiogenesis. Clinically, ALGS is characterized by cholestasis, cardiac defects, characteristic facial features, skeletal and ophthalmologic abnormalities. The aim of this review is to illustrate neuroradiological findings in ALGS, which are less well-known and prevalent, including cerebrovascular anomalies (such as aneurysms, dolichoectasia, Moyamoya syndrome and venous peculiarities), Chiari 1 malformation, craniosynostosis, intracranial hypertension, and vertebral anomalies (namely butterfly vertebra, hemivertebra, and craniocervical junction anomalies). Rarer cerebral midline malformations and temporal bone anomalies have also been described.

New Paleointensity Data Suggest Possible Phanerozoic‐Type Paleomagnetic Variations in the Precambrian

AbstractThe state of the geomagnetic field throughout the Precambrian era is largely unknown. Approximately 8% of global paleointensity records account for ∼4 billion years of Earth history. Despite this severe sparsity, the data are used to constrain models that predict the timing of significant deep earth events such as inner core nucleation. This carries with it the assumption that the Precambrian paleomagnetic field was less variable when compared to the Phanerozoic, or at least that the sparse data can be averaged to accurately represent a particular time period. This study reports new paleointensities from the West Australian Craton at 755 Ma (the Mundine Wells dyke swarm) and 1,070 Ma (the Bangemall Sills); both of which occurred within ∼30 Ma from times at which extremely weak and anomalously strong fields, respectively, have been reported. Virtual dipole moments of 6.3 ± 0.1 Am2 × 1022 and 1.8 ± 1.2 Am2 × 1022 have been obtained from the two suites of mafic rock units which are substantially different to the previous measurements for the two respective ages. The findings suggest that field variability over tens of Myrs in the Precambrian was greater than has previously been assumed. This is supported by comparisons of paleosecular variation and distributions of virtual dipole moments. If variability in the Precambrian field is similar to that observed in the Phanerozoic, spatial or temporal anomalies may introduce significant bias to statistical analyses and model constraints, implying that caution should be employed in the interpretation of the Precambrian dipole moment records.

SPATIO-TEMPORAL ANOMALIES IN SURFACE BRIGHTNESS TEMPERATURE PRECEDING VOLCANO ERUPTIONS DETECTED BY THE LANDSAT-8 THERMAL INFRARED SENSOR (CASE STUDY: KARANGETANG VOLCANO)

Indonesia's geological as part of the “ring of fire” includes the consequence that community life could be affected by volcanic activity. The catastrophic incidence of volcanic eruptions in the last ten years has had a disastrous impact on human life. To overcome this problem, it is necessary to conduct research on the strengthening of the early warning system for volcanic eruptions utilising remote sensing technology. This study analyses spatial and temporal anomalies of surface brightness temperature in the peak area of Karangetang volcano during the 2018-2019 eruption. Karangetang volcano is an active volcano located in North Sulawesi, with a magmatic eruption type that releases lava flow. We analyse the anomalies in the brightness temperature from channel-10 of the Landsat-8 TIRS (Thermal Infrared Scanner) time series during the period in question. The results of the research demonstrate that in the case of Karangetang Volcano the eruptions of 2018-2019 indicate increases in the surface brightness temperature of the crater region. As this volcano has many craters, the method is also very useful to establish in which crater the center of the eruption occurred.

Grid-Based Anomaly Detection of Freight Vehicle Trajectory considering Local Temporal Window

The security travel of freight vehicles is of high societal concern and is the key issue for urban managers to effectively supervise and assess the possible social security risks. With continuous improvements in motion-based technology, the trajectories of freight vehicles are readily available, whose unusual changes may indicate hidden urban risks. Moreover, the increasing high spatial and temporal resolution of trajectories provides the opportunity for the real-time recognition of the abnormal or risky vehicle motion. However, the existing researches mainly focus on the spatial anomaly detection, and there are few researches on the real-time temporal anomaly detection. In this paper, a grid-based algorithm, which combines the spatial and temporal anomaly detection, is proposed for tracing the risk of urban freight vehicles trajectory by considering local temporal window. The travel time probability distribution of vehicle historical trajectory is analyzed to meet the time complexity requirements of real-time anomaly calculation. The developed methodology is applied to a case study in Beijing to demonstrate its accuracy and effectiveness.

Studying drought-induced forest mortality using high spatiotemporal resolution evapotranspiration data from thermal satellite imaging

Drought can have pervasive and wide-spread impacts to forest health, as evidenced in several severe events occurring over the recent decades. Extensive forest die-off due to drought can impair the ecological functioning of forests, impacting habitat, water yield and quality from forested lands, and altering forest fire dynamics and intensity. Satellite remote sensing provides an effective means for detecting and monitoring spatial patterns of forest mortality over large areas, exploiting free and open long-term image archives available at a range in spatial and temporal resolutions. While remotely sensed surface reflectances and vegetation indices have been widely used to study optical response of forest canopies to drought events, retrievals of evapotranspiration (ET) derived from thermal satellite imagery – particularly at resolutions approaching crown scale - can provide insights into cumulative tree stresses that can incite disease and trigger mortality. In this study, we applied a multi-sensor satellite data fusion approach to estimate daily 30-m resolution ET and an associated Evaporative Stress Index (ESI) to study drought-induced mortality in a temperate forest at the Missouri Ozark AmeriFlux (MOFLUX) site, located in the central United States. The study covered the period from 2010 to 2014, including an exceptional drought year of 2012. Modeled ET agreed well with eddy flux measurements from the MOFLUX tower, with average monthly relative errors of 15%. Plot-scale ESI, describing temporal anomalies in the ratio of actual-to-reference ET, was used as an index of relative forest health to investigate relationships between forest mortality and drought severity. ESI showed good agreement with observed predawn leaf water potential, especially during the drought year. Furthermore, plot-scale ESI was also correlated with the subsequent year's tree mortality, suggesting the importance of considering the forest health condition prior to drought when studying drought-induced forest impacts. This study demonstrates the utility of multi-year ET remote sensing data at the stand or plot scale as an indicator of forest health and as a predictor of future mortality due to drought.

Interannual Variations of Vegetation Optical Depth are Due to Both Water Stress and Biomass Changes

AbstractMicrowave radiometry can be used to measure vegetation water content through vegetation optical depth (VOD). VOD can vary due to changes in water stress alone, but also scales with aboveground biomass. Several studies have therefore interpreted VOD temporal anomalies as linearly proportional to biomass anomalies, neglecting the influence of water stress. Here, we explicitly test this assumption using a new annual‐resolution data set of biomass derived from optical, radar, and lidar remote sensing. Both L‐band and X‐band VOD datasets are tested. Although VOD and biomass variations are highly correlated in space, their temporal anomalies are almost uncorrelated. At regional scale, it is as common for VOD anomalies to be more correlated to root‐zone soil moisture anomalies (a proxy for water stress) than to biomass anomalies as for the opposite to occur. Care should therefore be taken when deriving biomass anomalies from VOD, especially in the absence of any large‐scale biomass disturbances.