High Temporal Frequency Research Articles

To Blend or Not to Blend? A Framework for Nationwide Landsat–MODIS Data Selection for Crop Yield Prediction

The onus for monitoring crop growth from space is its ability to be applied anytime and anywhere, to produce crop yield estimates that are consistent at both the subfield scale for farming management strategies and the country level for national crop yield assessment. Historically, the requirements for satellites to successfully monitor crop growth and yield differed depending on the extent of the area being monitored. Diverging imaging capabilities can be reconciled by blending images from high-temporal-frequency (HTF) and high-spatial-resolution (HSR) sensors to produce images that possess both HTF and HSR characteristics across large areas. We evaluated the relative performance of Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat, and blended imagery for crop yield estimates (2009–2015) using a carbon-turnover yield model deployed across the Australian cropping area. Based on the fraction of missing Landsat observations, we further developed a parsimonious framework to inform when and where blending is beneficial for nationwide crop yield prediction at a finer scale (i.e., the 25-m pixel resolution). Landsat provided the best yield predictions when no observations were missing, which occurred in 17% of the cropping area of Australia. Blending was preferred when <42% of Landsat observations were missing, which occurred in 33% of the cropping area of Australia. MODIS produced a lower prediction error when ≥42% of the Landsat images were missing (~50% of the cropping area). By identifying when and where blending outperforms predictions from either Landsat or MODIS, the proposed framework enables more accurate monitoring of biophysical processes and yields, while keeping computational costs low.

Assimilation of GOES-R Geostationary Lightning Mapper Flash Extent Density Data in GSI EnKF for the Analysis and Short-Term Forecast of a Mesoscale Convective System

AbstractThe recently launched Geostationary Operational Environmental Satellite “R-series” (GOES-R) satellites carry the Geostationary Lightning Mapper (GLM) that measures from space the total lightning rate in convective storms at high spatial and temporal frequencies. This study assimilates, for the first time, real GLM total lightning data in an ensemble Kalman filter (EnKF) framework. The lightning flash extent density (FED) products at 10-km pixel resolution are assimilated. The capabilities to assimilate GLM FED data are first implemented into the GSI-based EnKF data assimilation (DA) system and tested with a mesoscale convective system (MCS). FED observation operators based on graupel mass or graupel volume are used. The operators are first tuned through sensitivity experiments to determine an optimal multiplying factor to the operator, before being used in FED DA experiments FEDM and FEDV that use the graupel-mass or graupel-volume-based operator, respectively. Their results are compared to a control experiment (CTRL) that does not assimilate any FED data. Overall, both DA experiments outperform CTRL in terms of the analyses and short-term forecasts of FED and composite/3D reflectivity. The assimilation of FED is primarily effective in regions of deep moist convection, which helps improve short-term forecasts of convective threats, including heavy precipitation and lightning. Direct adjustments to graupel mass via observation operator as well as adjustments to other model state variables through flow-dependent ensemble cross covariance within EnKF are shown to work together to generate model-consistent analyses and overall improved forecasts.

The potential use of operational radar network data to evaluate the representation of convective storms in NWP models

AbstractOperational forecasting centres increasingly rely on convection‐permitting NWP simulations to assist in their forecasting of convective events. The evaluation of upgrades in the underlying NWP modelling system normally happens through routine verification using traditional metrics on two‐dimensional fields, such as gridded rainfall data. Object‐ and process‐based evaluation can identify specific physical mechanisms for model improvement, but such evaluation procedures normally require targeted and expensive field campaigns. Here, we explore the potential use of the UK operational radar network observations and its derived 3D composite product for evaluating the representation of convective storms in the Met Office Unified Model. A comparison of the 1 × 1 × 0.5 km 3D radar composites against observations made with the research‐grade radar at Chilbolton in the southern UK indicates that the 3D radar composite data can reliably be used to evaluate the morphology of convective storms. The 3D radar composite data are subsequently used to evaluate the development of convective storms in the Met Office Unified Model. Such analysis was previously unavailable due to a lack of 3D radar data of high temporal frequency. The operational nature of the UK radar data makes these 3D composites a valuable resource for future studies of the initiation, growth, development, and organisation of convective storms over the UK.

Continuous Monitoring of Cotton Stem Water Potential using Sentinel-2 Imagery

Monitoring cotton status during the growing season is critical in increasing production efficiency. The water status in cotton is a key factor for yield and cotton quality. Stem water potential (SWP) is a precise indicator for assessing cotton water status. Satellite remote sensing is an effective approach for monitoring cotton growth at a large scale. The aim of this study is to estimate cotton water stress at a high temporal frequency and at a large scale. In this study, we measured midday SWP samples according to the acquisition dates of Sentinel-2 images and used them to build linear-regression-based and machine-learning-based models to estimate cotton water stress during the growing season (June to August, 2018). For the linear-regression-based method, we estimated SWP based on different Sentinel-2 spectral bands and vegetation indices, where the normalized difference index 45 (NDI45) achieved the best performance (R2 = 0.6269; RMSE = 3.6802 (-1*swp (bars))). For the machine-learning-based method, we used random forest regression to estimate SWP and received even better results (R2 = 0.6709; RMSE = 3.3742 (-1*swp (bars))). To find the best selection of input variables for the machine-learning-based approach, we tried three different data input datasets, including (1) 9 original spectral bands (e.g., blue, green, red, red edge, near infrared (NIR), and shortwave infrared (SWIR)), (2) 21 vegetation indices, and (3) a combination of original Sentinel-2 spectral bands and vegetation indices. The highest accuracy was achieved when only the original spectral bands were used. We also found the SWIR and red edge band were the most important spectral bands, and the vegetation indices based on red edge and NIR bands were particularly helpful. Finally, we applied the best approach for the linear-regression-based and the machine-learning-based methods to generate cotton water potential maps at a large scale and high temporal frequency. Results suggests that the methods developed here has the potential for continuous monitoring of SWP at large scales and the machine-learning-based method is preferred.

High-frequency analysis of parabolic stochastic PDEs

We consider the problem of estimating stochastic volatility for a class of second-order parabolic stochastic PDEs. Assuming that the solution is observed at a high temporal frequency, we use limit theorems for multipower variations and related functionals to construct consistent nonparametric estimators and asymptotic confidence bounds for the integrated volatility process. As a byproduct of our analysis, we also obtain feasible estimators for the regularity of the spatial covariance function of the noise.

An autonomous low-power instrument platform for monitoring water and solid discharges in mesoscale rivers

Abstract. We present the development of the River Platform for Monitoring Erosion (RIPLE) designed for monitoring at high temporal frequency (∼10 min) of water discharge, solid fluxes (bedload and suspended load) and properties of fine particles (settling velocity) in mesoscale rivers, i.e. which drain mesoscale catchments (≈10–103 km2). This platform responds to a request to continuously measure these variables in rivers using a single, centralized device, and to do this in the most direct way possible. The platform integrates the following instruments: (i) for water discharge, water level radar, and surface velocity radar, digital cameras and an echo sounder; (ii) for fine sediment load, turbidimeters and automatic samplers including the SCAF (a sediment settling velocity characterization device); (iii) for bedload, a hydrophone; and (iv) for water quality, a conductivity probe and water sampling. As far as water discharge monitoring is concerned, priority has been given to non-intrusive instruments to improve the robustness of the system. All the instruments are driven by a data logger (Campbell® CR6), which locally stores the data and then uploads them to a remote server every hour during the day using a 3G modem. SMS (Short Message Service) alerts can be sent depending on scheduled conditions (e.g. low battery voltage, water level threshold, all samples of the automatic sampler collected). The platform has been designed to be as autonomous as possible: it is powered by a battery that is supplied by a solar panel. Limiting the power consumption of the platform was one of the main technical challenges because of the quantity of instruments integrated. A simple 100 W solar panel is sufficient to power the entire platform, even during winter or low insulation conditions. A user-friendly interface has been developed, enabling to visualize the data collected by the platform from an internet connection. It is also possible to remotely configure the platform within this interface, e.g. to modify water sampling thresholds or alert thresholds. Finally, the platform is relatively easy to move from one site to another, because its installation requires little civil engineering. To date, RIPLE has been tested on two rivers of the Alps in France: the Romanche river in Bourg d'Oisans from September 2016 to July 2018 and the Galabre river in La Robine sur Galabre since October 2018, demonstrating the proper functioning of the platform.

Seasonality Analysis of Sentinel-1 and ALOS-2/PALSAR-2 Backscattered Power over Salar de Aguas Calientes Sur, Chile

Seasonal changes control the development of salt crust over the Salar de Aguas Calientes Sur located in Andes Highlands, Chile. Precipitations throughout the Altiplanic winter (December to March) and austral winter (June to September) caused ponds to enlarge and surface salt crusts to dissolve driving roughness and dielectric features of the salar surface change over time. A four-year time series backscattering coefficient analysis, obtained by Sentinel 1 and ALOS-2/PALSAR-2 with 10 m of spatial resolution, demonstrated the capability of microwaves to discriminate seasonal patterns illustrated in this paper. Both sensors showed to be sensitive to changes in the surface crust due to weather conditions. Backscattered power gradually increased during the driest months as the rough salt crusts develop and decreased rapidly due to precipitations or flooding events, which lead to a smoothing appearance to radar. The high temporal frequency of acquisition in Sentinel 1 (5–13 scenes/month) allowed the discrimination among climate and annual seasonality and episodic events in the C-band backscatter coefficient. On the other hand, ALOS-2/PALSAR-2 showed subsurface changes at L-band since the salinity of the brine in the soil reduces the penetration depth of backscattered power for shorter wavelengths. Results might be useful to monitor salars with geographic and weather conditions similar to Salar de Aguas Calientes Sur.

Deep learning-based water quality estimation and anomaly detection using Landsat-8/Sentinel-2 virtual constellation and cloud computing

ABSTRACT Monitoring of inland water quality is of significant importance due to the increase in water quality related issues, especially within the Midwestern United States. Traditional monitoring techniques, although highly accurate, are vastly insufficient in terms of spatial and temporal coverage. Using a virtual constellation by harmonizing Landsat-8 and Sentinel-2 data a high temporal frequency dataset can be created at a relatively fine spatial scale. In this study, we apply a novel deep learning method for the estimation of blue-green algae (BGA), chlorophyll-α (Chl), fluorescent dissolved organic matter (fDOM), dissolved oxygen (DO), specific conductance (SC), and turbidity. The developed model is evaluated against previously studied machine learning methods and found to outperform multiple linear regression (MLR), support vector machine regression (SVR), and extreme learning machine regression (ELR) generating R2 of 0.91 for BGA, 0.88, 0.89, 0.93, 0.87, and 0.84 for Chl, DO, SC, and turbidity respectfully. This model is then applied to all available data ranging from 2013–2018 and time series for each variable were generated for four selected waterbodies. We then use the Empirical Data Analytics (EDA) anomaly detection method on the time series to identify abnormal data points. Upon further analysis, the EDA method successfully identifies abnormal events in water quality. Our results also demonstrate strong correlation between non-optically active variables such as SC with Chl and fDOM. The framework developed in this study represents an efficient and accurate empirical method for inland water quality monitoring at the regional scale.

Graph-time spectral analysis for atrial fibrillation

Atrial fibrillation is a clinical arrhythmia with multifactorial mechanisms still unresolved. Time-frequency analysis of epicardial electrograms has been investigated to study atrial fibrillation. However, deeper understanding can be achieved by incorporating the spatial dimension. Unfortunately, the physical models describing the spatial relations of atrial fibrillation signals are complex and non-linear; hence, conventional signal processing techniques to study electrograms in the joint space, time, and frequency domain are less suitable. In this study, we wish to put forward a radically different approach to analyze atrial fibrillation with a higher-level model. This approach relies on graph signal processing to represent the spatial relations between epicardial electrograms. To capture the frequency content along both the time and graph domain, we propose the joint graph and short-time Fourier transform. The latter allows us to analyze the spatial variability of the electrogram temporal frequencies. With this technique, we found the spatial variation of the atrial electrograms decreases during atrial fibrillation since the high temporal frequencies of the atrial waves reduce. The proposed analysis further confirms that the ventricular activity is smoother over the atrial area compared with the atrial activity. Besides using the proposed graph-time analysis to conduct a first study on atrial fibrillation, we demonstrate its potential by applying it to the cancellation of ventricular activity from the atrial electrograms. Experimental results on simulated and real data further corroborate our findings in this atrial fibrillation study.

Effects of varying solar-view geometry and canopy structure on solar-induced chlorophyll fluorescence and PRI

The increasing amount of continuous time series of solar-induced fluorescence (SIF) and vegetation indices (e.g. Photochemical Reflectance Index, PRI) acquired with high temporal (sub-minute) frequencies is foreseen to allow tracking of the structural and physiological changes of vegetation in a variety of ecosystems. Coupled with observations of CO2, water, and energy fluxes from eddy covariance flux towers, these measurements can bring new insights into the remote monitoring of ecosystem functioning. However, continuously changing solar-view geometry imposes directional effects on diurnal cycles of the fluorescence radiance in the observation direction (F) and PRI, controlled by structural and biochemical vegetation properties. An improved understanding of these variations can potentially help to disentangle directional responses of vegetation from physiological ones in the continuous long-term optical measurements and, therefore, allow to deconvolve the physiological information relevant to ecosystem functioning. Moreover, this will also be useful for better interpreting and validating F and PRI satellite products (e.g., from the upcoming ESA FLEX mission).Many previous studies focused on the characterization of reflectance directionality, but only a handful of studies investigated directional effects on F and vegetation indices related to plant physiology. The aim of this study is to contribute to the understanding of red (F687) and far-red (F760) fluorescence and PRI anisotropy based on field spectroscopy data and simulations with the Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model. We present an extensive dataset of multi-angular measurements of F and PRI collected at canopy level with a high-resolution instrument (FloX, JB Hyperspectral Devices UG, Germany) over different ecosystems: Mediterranean grassland, alfalfa, chickpea and rice.We found, that F760 and F687 directional responses of horizontally homogeneous canopies are characterized by higher values in the backscatter direction with a maximum in the hotspot and lower values in the forward scatter direction. The PRI exhibited similar response due to its sensitivity to sunlit-shaded canopy fractions.As confirmed by radiative transfer forward simulations, we show that in the field measurements leaf inclination distribution function controls the shape of F and PRI anisotropic response (bowl-like/dome-like shapes), while leaf area index and the ratio of leaf width to canopy height affect the magnitude and the width of the hotspot. Finally, we discuss the implications of off-nadir viewing geometry for continuous ground measurements. F observations under oblique viewing angles showed up to 67 % difference compared to nadir observations, therefore, we suggest maintaining nadir viewing geometry for continuous measurements of F and vegetation indices. Alternatively, a correction scheme should be developed and tested against multi-angular measurements to properly account for anisotropy of canopy F and PRI observations. The quantitative characterization of these effects in varying illumination geometries for different canopies that was performed in this study will also be useful for the validation of remote sensing F and PRI products at different spatial and temporal scales.

Impact of Assimilating PECAN Profilers on the Prediction of Bore-Driven Nocturnal Convection: A Multiscale Forecast Evaluation for the 6 July 2015 Case Study

Abstract Using data from the 6 July 2015 PECAN case study, this paper provides the first objective assessment of how the assimilation of ground-based remote sensing profilers affects the forecasts of bore-driven convection. To account for the multiscale nature of the phenomenon, data impacts are examined separately with respect to (i) the bore environment, (ii) the explicitly resolved bore, and (iii) the bore-initiated convection. The findings from this work suggest that remote sensing profiling instruments provide considerable advantages over conventional in situ observations, especially when the retrieved data are assimilated at a high temporal frequency. The clearest forecast improvements are seen in terms of the predicted bore environment where the assimilation of kinematic profilers reduces a preexisting bias in the structure of the low-level jet. Data impacts with respect to the other two forecast components are mixed in nature. While the assimilation of thermodynamic retrievals from the Atmospheric Emitted Radiance Interferometer (AERI) results in the best convective forecast, it also creates a positive bias in the height of the convectively generated bore. Conversely, the assimilation of wind profiler data improves the characteristics of the explicitly resolved bore, but tends to further exacerbate the lack of convection in the control forecasts. Various dynamical diagnostics utilized throughout this study provide a physical insight into the data impact results and demonstrate that a successful prediction of bore-driven convection requires an accurate depiction of the internal bore structure as well as the ambient environment ahead of it.

The Sensitivity of Multi-spectral Satellite Sensors to Benthic Habitat Change

Coral reef ecosystems are under stress due to human-driven climate change and coastal activities. Satellite-based monitoring approaches offer an alternative to traditional field sampling measurements for detecting coral reef composition changes, especially given the advantages in their broad spatial coverage and high temporal frequency. However, the effect of benthic composition changes on water-leaving reflectance remains underexplored. In this study, we examined benthic change detection abilities of four representative satellite sensors: Landsat-8, Sentinel-2, Planet Dove and SkySat. We measured the bottom reflectance of different benthic compositions (live coral, bleached coral, dead coral with algal cover, and sand) in the field and developed an analytical bottom-up radiative transfer model to simulate remote sensing reflectance at the water surface for different compositions at a variety of depths and in varying water clarity conditions. We found that green spectral wavelengths are best for monitoring benthic changes such as coral bleaching. Moreover, we quantified the advantages of high spatial resolution imaging for benthic change detection. Together, our results provide guidance as to the potential use of the latest generation of multi-spectral satellites for monitoring coral reef and other submerged coastal ecosystems.

Spectral unmixing based spatiotemporal downscaling fusion approach

Time-series remote sensing data are important in monitoring land surface dynamics. Due to technical limitations, satellite sensors have a trade-off between temporal, spatial and spectral resolutions when acquiring remote sensing images. In order to obtain remote sensing images with high spatial resolution and high temporal frequency, spatiotemporal fusion methods have been developed. In this paper, we propose a Linear Spectral Unmixing-based Spatiotemporal Data Fusion Model (LSUSDFM) for spatial and temporal data fusion. In this model, the endmember abundance of the low-resolution image pixel is calculated based on that of the high-resolution image by the spectral mixture analysis. The endmember spectrum signals of low-resolution images are then calculated continuously within an optimized moving window. Subsequently, the fused image is reconstructed according to the endmember spectrum and its corresponding abundance map. A simulated dataset and real satellite images are used to test the fusion model, and the fusion results are compared with a current spectral unmixing based downscaling fusion model (SUDFM). Our experimental work shows that, compared to the SUDFM, the proposed LSUSDFM can achieve better quality and accuracy of fused images, especially in effectively eliminating the “plaque” phenomenon in the results by the SUDFM. The LSUSDFM has great potential in generating images with both high spatial resolution and high temporal frequency, as well as increasing the number of spectral bands of the high spatial resolution data.

Understanding the Structure of Rapid Intensity Fluctuations in the Chromosphere with IRIS

Abstract Several recent data sets have taken advantage of the unique capabilities of orbital and suborbital spacecraft to image the chromosphere and transition region at a very high cadence. We find that both the chromosphere and transition region exhibit spatially coherent transient features at frequencies above 80 mHz. We have analyzed narrowband imaging data from H i Lyα 1215 Å (provided by CLASP), Mg ii k 2796 Å, and Si iv 1400 Å (provided by the Interface Region Imaging Spectrograph). We categorize the spatial structure of these rapid fluctuations in different magnetic environments. Intensity fluctuations above the noise level are observed in plages in all passbands. We have found jet-like features in 1215 and 1400 Å data that extend from the edges of plages as well as network magnetic concentrations. We have not found any recurrent features that are identified in both 1400 and 1215 Å data. Short loop-like features were only found in 1400 Å data. Temperature minimum grains generate non-propagating features in 1400 Å data. We compare our observations with previous research on dynamic chromospheric phenomena at lower frequencies. Candidate mechanisms, related to both jets and waves, predict chromospheric and transition region intensity fluctuations at high temporal frequencies, which can now be observationally probed.

Temporal information loss in the macaque early visual system.

Stimuli that modulate neuronal activity are not always detectable, indicating a loss of information between the modulated neurons and perception. To identify where in the macaque visual system information about periodic light modulations is lost, signal-to-noise ratios were compared across simulated cone photoreceptors, lateral geniculate nucleus (LGN) neurons, and perceptual judgements. Stimuli were drifting, threshold-contrast Gabor patterns on a photopic background. The sensitivity of LGN neurons, extrapolated to populations, was similar to the monkeys' at low temporal frequencies. At high temporal frequencies, LGN sensitivity exceeded the monkeys' and approached the upper bound set by cone photocurrents. These results confirm a loss of high-frequency information downstream of the LGN. However, this loss accounted for only about 5% of the total. Phototransduction accounted for essentially all of the rest. Together, these results show that low temporal frequency information is lost primarily between the cones and the LGN, whereas high-frequency information is lost primarily within the cones, with a small additional loss downstream of the LGN.

An Indicator and Min-Cost Approach for Shoreline Extraction From Satellite Imagery in Muddy Coasts

Shoreline location plays a key role in coastal research, management, and engineering. Remote sensing enables the quantification of shoreline information with the large spatial extent and high temporal frequency. Driven by river discharge and ocean dynamics, muddy coasts exhibit complicated spatiotemporal variations. It is essential, yet challenging to extract effective shoreline features from satellite images. Taking the Yellow River Delta coast in China as the study area, we present an indicator and min-cost approach to extract the shoreline in muddy coasts. The shoreline is represented as a set of linearly connected central points with high shoreline probabilities, and a set of image and spatial indicators are developed to assess these probabilities. The Salient Value indicator integrates the gradient magnitude and the edge intensity to detect the boundary strength; the Regional Difference indicator separates the water/land class from edge intensity to measure the possibility of being water or land; and the Seaward Distance indicator spatially distinguishes the true shoreline from other spectrally similar boundaries. A cost function combines these indicators to evaluate the local shoreline possibilities. A shoreline set is produced by an improved min-cost path method to evaluate the overall shoreline possibilities. The optimal shoreline paths are selected based on the parameter analyses of the shoreline set. The performance of the approach is confirmed by comparing with the ground truth and state-of-the-art methods. The effectiveness of the approach is tested for different spatial resolution data and coastal environments.

Can the Cognitive Phenotype in Neurofibromatosis Type 1 (NF1) Be Explained by Neuroimaging? A Review.

Neurofibromatosis type 1 (NF1) is one of the most frequent monogenetic disorders. It can be associated with cognitive dysfunctions in several domains such as executive functioning, language, visual perception, motor skills, social skills, memory and/or attention. Neuroimaging is becoming more and more important for a clearer understanding of the neural basis of these deficits. In recent years, several studies have used different imaging techniques to examine structural, morphological and functional alterations in NF1 disease. They have shown that NF1 patients have specific brain characteristics such as Unidentified Bright Objects (UBOs), macrocephaly, a higher volume of subcortical structures, microstructure integrity alterations, or connectivity alterations. In this review, which focuses on the studies published after the last 2 reviews of this topic (in 2010 and 2011), we report on recent structural, morphological and functional neuroimaging studies in NF1 subjects, with special focus on those that examine the neural basis of the NF1 cognitive phenotype. Although UBOs are one of the most obvious and visible elements in brain imaging, correlation studies have failed to establish a robust and reproducible link between major cognitive deficits in NF1 and their presence, number or localization. In the same vein, the results among structural studies are not consistent. Functional magnetic resonance imaging (fMRI) studies appear to be more sensitive, especially for understanding the executive function deficit that seems to be associated with a dysfunction in the right inferior frontal areas and the middle frontal areas. Similarly, fMRI studies have found that visuospatial deficits could be associated with a dysfunction in the visual cortex and especially in the magnocellular pathway involved in the processing of low spatial frequency and high temporal frequency. Connectivity studies have shown a reduction in anterior-posterior “long-range” connectivity and a deficit in deactivation in default mode network (DMN) during cognitive tasks. In conclusion, despite the contribution of new imaging techniques and despite relative advancement, the cognitive phenotype of NF1 patients is not totally understood.

The interruption of longitudinal hydrological connectivity causes delayed responses in dissolved organic matter

Hydrology is the main driver of dissolved organic matter (DOM) dynamics in intermittent rivers and ephemeral streams. However, it is still unclear how the timing and the spatial variation in flow connectivity affect the dynamics of DOM and inorganic solutes. This study focuses on the impact of flow cessation on the temporal and spatial heterogeneity of DOM quantity and quality along an intermittent stream. We monitored a headwater intermittent stream at high spatial and temporal frequencies during a summer drying episode and analysed dissolved organic carbon (DOC) and its spectroscopic properties, inorganic solutes and dissolved CO2. The drying period determined the disruption of the fluvial continuum with a recession of stream continuum at a rate of ~60 m/d and the gradual formation of a patched system of isolated pools of different sizes. Our results showed that the period of time that had elapsed since isolated pool formation (CI-days) was an essential factor for understanding how drying shaped the biogeochemistry of the fluvial system. Overall, drying caused a high DOC concentration and an increase in the humic-like fluorescence signal. Additionally, solutes showed contrasting responses to hydrological disconnection. Electrical conductivity, for instance, is a clear “sentinel” of the fragmentation process because it starts to increase before the hydrological disruption occurs. In contrast, DOC, most spectroscopic DOM descriptors and CO2 showed delayed responses of approximately 5–21 days after the formation of isolated pools. Furthermore, the spatial location and volume of each isolated pool seemed to exert a significant impact on most variables. In contrast, the temperature did not follow a clear pattern. These findings indicate that the fragmentation of longitudinal hydrological connectivity does not induce a single biogeochemical response but rather stimulates a set of solute-specific responses that generates a complex biogeochemical mosaic in a single fluvial unit.

Deriving high-spatiotemporal-resolution leaf area index for agroecosystems in the U.S. Corn Belt using Planet Labs CubeSat and STAIR fusion data

Leaf area index (LAI) is a key variable for characterizing crop growth conditions and estimating crop productivity. Despite continuing efforts to develop LAI estimation algorithms, LAI datasets still need improvement at spatial and temporal resolutions to meet the requirements of agricultural applications. Advancements in data fusion technique and the emergence of new satellite data provide opportunities for LAI data at higher resolutions in both space and time. In this study, we derived new LAI estimations by leveraging novel satellite remote sensing datasets, STAIR fusion (MODIS-Landsat fusion) and Planet Labs' CubeSat data (through a reprocessed pipeline) for a typical agricultural landscape in the U.S. Corn Belt. The STAIR fused data and our reprocessed CubeSat data have both fine spatial resolutions (30 m and 3.125 m, respectively) and high frequencies (daily for both). To reliably estimate LAI from these advanced satellite datasets, we used two methods: inversion of a radiative transfer model (RTM), and empirical relationship with vegetation index (VI) calibrated from field measured LAI. Compared to the ground-truth LAI collected at 36 sites across the study region, reliable approximations were achieved by both LAI estimations based on PROSAIL RTM (STAIR: R2 = 0.69 and root mean squared error (RMSE) = 1.12 (m2 m−2), CubeSat: R2 = 0.76 and RMSE = 1.09 (m2 m−2)), and LAI estimations based on Green Wide Dynamic Range Vegetation Index (GrWDRVI) (STAIR: R2 = 0.75, RMSE = 1.10 (m2 m−2), CubeSat: R2 = 0.76, RMSE = 1.08 (m2 m−2), where validation ground-truth is independent from calibration data). Newly estimated high-resolution LAI data were aggregated at 500 m resolution and compared with MODIS and VIIRS LAI products, revealing substantial uncertainties and biases in these two products. We also demonstrated phenology stage estimation at fine spatial resolutions based on our high-frequency LAI data. The proposed LAI estimation methods at both high spatial resolution and temporal frequency can be applied to the entire U.S. Corn Belt and provide significant advancement to crop monitoring and precision agriculture.

Dynamic Origin-Destination Demand Estimation From Link Counts, Cellular Data And Travel Time Data

Accurate real-time Origin-Destination (O-D) demand estimation is critical for development of Advanced Traveller Information Systems (ATIS) as well as Advanced Traffic Management Systems (ATMS). Traditional O-D estimation techniques typically pose it as optimization problems involving offline calibration of O-D priors from historical data and updating these prior based on real-time data sources such as link counts, link travel times and Global Positioning System (GPS) probe data. However, two major drawbacks have emerged from this approach. Firstly, the generation of effective priors requires extensive population data with high temporal sampling frequency. Such data is generally hard to obtain and process with low memory and compute power. Secondly, the optimization problems proposed for are often non-convex in nature. Therefore, convergence to an optimal solution is not guaranteed. Moreover, the algorithms suggested don’t scale well with increase in the size of the transportation network. In order to tackle these challenges, we first generate priors through a convex optimization framework by Wu et al. (2015) for O-D estimation in quasi-static settings from cell phone Call Detail Records (CDRs). We extend this approach to a completely dynamic setting by utilizing real-time link counts and link travel times through an Entropy Maximization framework proposed by Janson et al. (1992). Approximate solution to the traffic assignment sub-problem is also achieved from Wu et al. (2015). We test this procedure for estimating commute demand for a simplified freeway network representing nine counties of the San Francisco Bay Area. We estimate demands for 2,916 O-D pairs for three hours at five-minute intervals, making it a 104,976 dimensional problem. We analyze the spatio-temporal distribution of errors, the effects of wider cell-phone coverage and updating estimates over time. Results indicate that an increase in cell-phone coverage from 0% to 100% leads to a reduction in average Root Mean Squared Error (RMSE) from 15.7 to 7.2 while updating estimates also leads to reductions in RMSE when O-D demand is high.