Near-Field Remote Sensing of Surface Velocity and River Discharge Using Radars and the Probability Concept at 10 U.S. Geological Survey Streamgages

Near-field remote sensing of surface velocity and river discharge (discharge) were measured using coherent, continuous wave Doppler and pulsed radars. Traditional streamgaging requires sensors be deployed in the water column; however, near-field remote sensing has the potential to transform streamgaging operations through non-contact methods in the U.S. Geological Survey (USGS) and other agencies around the world. To differentiate from satellite or high-altitude platforms, near-field remote sensing is conducted from fixed platforms such as bridges and cable stays. Radar gages were collocated with 10 USGS streamgages in river reaches of varying hydrologic and hydraulic characteristics, where basin size ranged from 381 to 66,200 square kilometers. Radar-derived mean-channel (mean) velocity and discharge were computed using the probability concept and were compared to conventional instantaneous measurements and time series. To test the efficacy of near-field methods, radars were deployed for extended periods of time to capture a range of hydraulic conditions and environmental factors. During the operational phase, continuous time series of surface velocity, radar-derived discharge, and stage-discharge were recorded, computed, and transmitted contemporaneously and continuously in real time every 5 to 15 min. Minimum and maximum surface velocities ranged from 0.30 to 3.84 m per second (m/s); minimum and maximum radar-derived discharges ranged from 0.17 to 4890 cubic meters per second (m3/s); and minimum and maximum stage-discharge ranged from 0.12 to 4950 m3/s. Comparisons between radar and stage-discharge time series were evaluated using goodness-of-fit statistics, which provided a measure of the utility of the probability concept to compute discharge from a singular surface velocity and cross-sectional area relative to conventional methods. Mean velocity and discharge data indicate that velocity radars are highly correlated with conventional methods and are a viable near-field remote sensing technology that can be operationalized to deliver real-time surface velocity, mean velocity, and discharge.

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QCam: sUAS-Based Doppler Radar for Measuring River Discharge

Remote Sensing ◽

10.3390/rs12203317 ◽

2020 ◽

Vol 12 (20) ◽

pp. 3317

Author(s):

John W. Fulton ◽

Isaac E. Anderson ◽

C.-L. Chiu ◽

Wolfram Sommer ◽

Josip D. Adams ◽

...

Keyword(s):

Remote Sensing ◽

High Altitude ◽

River Discharge ◽

Near Field ◽

Surface Velocity ◽

Sectional Area ◽

Mean Velocity ◽

Cross Sectional ◽

Arkansas River ◽

South Platte River

The U.S. Geological Survey is actively investigating remote sensing of surface velocity and river discharge (discharge) from satellite-, high altitude-, small, unmanned aircraft systems- (sUAS or drone), and permanent (fixed) deployments. This initiative is important in ungaged basins and river reaches that lack the infrastructure to deploy conventional streamgaging equipment. By coupling alternative discharge algorithms with sensors capable of measuring surface velocity, streamgage networks can be established in regions where data collection was previously impractical or impossible. To differentiate from satellite or high-altitude platforms, near-field remote sensing is conducted from sUAS or fixed platforms. QCam is a Doppler (velocity) radar mounted and integrated on a 3DR© Solo sUAS. It measures the along-track surface velocity by spot dwelling in a river cross section at a vertical where the maximum surface velocity is recorded. The surface velocity is translated to a mean-channel (mean) velocity using the probability concept (PC), and discharge is computed using the PC-derived mean velocity and cross-sectional area. Factors including surface-scatterer quality, flight altitude, propwash, wind drift, and sample duration may affect the radar-returns and the subsequent computation of mean velocity and river discharge. To evaluate the extensibility of the method, five science flights were conducted on four rivers of varying size and dynamics and included the Arkansas River, Colorado (CO), USA (two events); Salcha River near Salchaket, Alaska (AK), USA; South Platte River, CO, USA; and the Tanana River, AK, USA. QCam surface velocities and river discharges were compared to conventional streamgaging methods, which represented truth. QCam surface velocities for the Arkansas River, Salcha River, South Platte River, and Tanana River were 1.02 meters per second (m/s) and 1.43 m/s; 1.58 m/s; 0.90 m/s; and 2.17 m/s, respectively. QCam discharges (and percent differences) were 9.48 (0.3%) and 20.3 cubic meters per second (m3/s) (2.5%); 62.1 m3/s (−10.4%); 3.42 m3/s (7.3%), and 1579 m3/s (−18.8%). QCam results compare favorably with conventional streamgaging and are a viable near-field remote sensing technology that can be operationalized to deliver real-time surface velocity, mean velocity, and river discharge, if cross-sectional area is available.

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Surface velocity variations of glaciers on Kenai Peninsula, Alaska, 2014-2019

10.5194/egusphere-egu21-16020 ◽

2021 ◽

Author(s):

Ruitang Yang ◽

Regine Hock ◽

Shichang Kang ◽

Donghui Shangguan ◽

Wanqin Guo

Keyword(s):

Time Series ◽

Velocity Profile ◽

Velocity Fields ◽

Surface Velocity ◽

Spatiotemporal Variations ◽

Outburst Flood ◽

Mean Velocity ◽

Kenai Peninsula ◽

Speed Up ◽

Offset Tracking

We characterize the spatiotemporal variations surface velocity of glaciers on the Kenai Peninsula, Alaska, using intensity offset tracking on a set of repeat-pass Sentinel-1 data and TerraSAR-X data. We derived 92 velocity fields and generated time-averaged annual and seasonal surface velocity maps for the period October 2014 to December 2019, as well as time series surface velocity profiles along centerlines for individual glaciers. We find considerable spatial and seasonal variations in surface velocity in the study area, especially a pronounced average spring speedup of 50% averagely compared to annual mean velocity. Ice velocities varied systematically between glaciers with different terminus types. Generally, the pixel-averaged velocity of tidewater and lake-terminating glaciers are up to 2 and 1.5 times greater than those of the land-terminating glaciers, respectively. For Bear glacier, with the analysis of surface velocity profile and the terminus change, we state this glacier retreat and accelerate. While the time-series result shows the velocity speed-up of the Bear glacier synchronizes well with the ice-damaged lake outburst flood (GLOF) events.

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Estimation of River Discharge using Multi-Mission Satellite Altimetry and Optical Remote Sensing Imagery

10.5194/egusphere-egu2020-2650 ◽

2020 ◽

Author(s):

Daniel Scherer ◽

Christian Schwatke ◽

Denise Dettmering

Keyword(s):

Remote Sensing ◽

Time Series ◽

Water Level ◽

River Discharge ◽

Water Surface ◽

Satellite Altimetry ◽

Remote Sensing Data ◽

Sensing Data

Despite increasing interest in monitoring the global water cycle, the availability of in-situ discharge time series is decreasing. However, this lack of ground data can be compensated by using remote sensing techniques to observe river discharge.In this contribution, a new approach for estimating the discharge of large rivers by combining various long-term remote sensing data with physical flow equations is presented. For this purpose, water levels derived from multi-mission satellite altimetry and water surface extents extracted from optical satellite images are used, both provided by DGFI-TUM&#8217;s &#8220;Database of Hydrological Time series of Inland Waters&#8221; (DAHITI, https://dahiti.dgfi.tum.de). The datasets are combined by fitting a hypsometric curve in order to describe the stage-width relation, which is then used to derive the water level for each acquisition epoch of the long-term multi-spectral remote sensing missions. In this way, the chance of detecting water level extremes is increased and a bathymetry can be estimated from water surface extent observations. Below the minimum hypsometric water level, the river bed elevation is estimated using an empirical width-to-depth relationship in order to determine the final cross-sectional geometry. The required flow gradient is computed based on a linear adjustment of river surface slope using all altimetry-observed water level differences between synchronous measurements at various virtual stations along the river. The roughness coefficient is set based on geomorphological features quantified by adjustment factors. These are chosen using remote sensing data and a literature decision guide.Within this study, all parameters are estimated purely based on remote sensing data, without using any ground data. In-situ data is only used for the validation of the method at the Lower Mississippi River. It shows that the presented approach yields best results for uniform and straight river sections. The resulting normalized root mean square error for those targets varies between 10% to 35% and is comparable with other studies.

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Observing traveling waves in glaciers with remote sensing: new flexible time series methods and application to Sermeq Kujalleq (Jakobshavn Isbræ), Greenland

The Cryosphere ◽

10.5194/tc-15-407-2021 ◽

2021 ◽

Vol 15 (1) ◽

pp. 407-429

Author(s):

Bryan Riel ◽

Brent Minchew ◽

Ian Joughin

Keyword(s):

Remote Sensing ◽

Time Series ◽

Traveling Waves ◽

Remote Sensing Data ◽

Surface Velocity ◽

Least Squares Regression ◽

Glacier Surface ◽

Sensing Data ◽

Velocity Variations ◽

Discontinuous Data

Abstract. The recent influx of remote sensing data provides new opportunities for quantifying spatiotemporal variations in glacier surface velocity and elevation fields. Here, we introduce a flexible time series reconstruction and decomposition technique for forming continuous, time-dependent surface velocity and elevation fields from discontinuous data and partitioning these time series into short- and long-term variations. The time series reconstruction consists of a sparsity-regularized least-squares regression for modeling time series as a linear combination of generic basis functions of multiple temporal scales, allowing us to capture complex variations in the data using simple functions. We apply this method to the multitemporal evolution of Sermeq Kujalleq (Jakobshavn Isbræ), Greenland. Using 555 ice velocity maps generated by the Greenland Ice Mapping Project and covering the period 2009–2019, we show that the amplification in seasonal velocity variations in 2012–2016 was coincident with a longer-term speedup initiating in 2012. Similarly, the reduction in post-2017 seasonal velocity variations was coincident with a longer-term slowdown initiating around 2017. To understand how these perturbations propagate through the glacier, we introduce an approach for quantifying the spatially varying and frequency-dependent phase velocities and attenuation length scales of the resulting traveling waves. We hypothesize that these traveling waves are predominantly kinematic waves based on their long periods, coincident changes in surface velocity and elevation, and connection with variations in the terminus position. This ability to quantify wave propagation enables an entirely new framework for studying glacier dynamics using remote sensing data.

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Early Identification of Seed Maize and Common Maize Production Fields Using Sentinel-2 Images

Remote Sensing ◽

10.3390/rs12132140 ◽

2020 ◽

Vol 12 (13) ◽

pp. 2140 ◽

Cited By ~ 3

Author(s):

Tianwei Ren ◽

Zhe Liu ◽

Lin Zhang ◽

Diyou Liu ◽

Xiaojie Xi ◽

...

Keyword(s):

Remote Sensing ◽

Time Series ◽

Real Time ◽

Classification Accuracy ◽

Growing Season ◽

Remote Sensing Images ◽

Maize Production ◽

Seed Supply ◽

Real Time Identification ◽

Sentinel 2

Accurate and timely access to the production area of crop seeds allows the seed market and secure seed supply to be monitored. Seed maize and common maize production fields typically share similar phenological development profiles with differences in the planting patterns, which makes it challenging to separate these fields from decametric-resolution satellite images. In this research, we proposed a method to identify seed maize production fields as early as possible in the growing season using a time series of remote sensing images in the Liangzhou district of Gansu province, China. We collected Sentinel-2 and GaoFen-1 (GF-1) images captured from March to September. The feature space for classification consists of four original bands, namely red, green, blue, and near-infrared (nir), and eight vegetation indexes. We analyzed the timeliness of seed maize identification using Sentinel-2 time series of different time spans and identified the earliest time frame for reasonable classification accuracy. Then, the earliest time series that met the requirements of regulatory accuracy were compared and analyzed. Four machine/deep learning algorithms were tested, including K-nearest neighbor (KNN), support vector classification (SVC), random forest (RF), and long short-term memory (LSTM). The results showed that using Sentinel-2 images from March to June, the RF and LSTM algorithms achieve over 88% accuracy, with the LSTM performing the best (90%). In contrast, the accuracy of KNN and SVC was between 82% and 86%. At the end of June, seed maize mapping can be carried out in the experimental area, and the precision can meet the basic requirements of monitoring for the seed industry. The classification using GF-1 images were less accurate and reliable; the accuracy was 85% using images from March to June. To achieve near real-time identification of seed maize fields early in the growing season, we adopted an automated sample generation approach for the current season using only historical samples based on clustering analysis. The classification accuracy using new samples extracted from historical mapping reached 74% by the end of the season (September) and 63% by the end of July. This research provides important insights into the classification of crop fields cultivated with the same crop but different planting patterns using remote sensing images. The approach proposed by this study enables near-real time identification of seed maize production fields within the growing season, which could effectively support large-scale monitoring of the seed supply industry.

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Mapping Crop Phenology in Near Real-Time Using Satellite Remote Sensing: Challenges and Opportunities

Journal of Remote Sensing ◽

10.34133/2021/8379391 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Feng Gao ◽

Xiaoyang Zhang

Keyword(s):

Remote Sensing ◽

Time Series ◽

Real Time ◽

Land Surface ◽

Vegetation Index ◽

Growing Season ◽

Crop Growth ◽

Growth Stages ◽

Crop Phenology ◽

Crop Growth Stages

Crop phenology is critical for agricultural management, crop yield estimation, and agroecosystem assessment. Traditionally, crop growth stages are observed from the ground, which is time-consuming and lacks spatial variability. Remote sensing Vegetation Index (VI) time series has been used to map land surface phenology (LSP) and relate to crop growth stages mostly after the growing season. In recent years, high temporal and spatial resolution remote sensing data have allowed near-real-time mapping of crop phenology within the growing season. This paper summarizes two classes of near-real-time mapping methods, i.e., curve-based and trend-based approaches. The curve-based approaches combine the time series VIs and crop growth stages from historical years with the current observations to estimate crop growth stages. The curve-based approaches are capable of a short-term prediction. The trend-based approaches detect upward or downward trends from time series and confirm the trends using the increasing or decreasing momentum and VI thresholds. The trend-based approaches only use current observations. Both curve-based and trend-based approaches are promising in mapping crop growth stages timely. Nevertheless, mapping crop phenology near real-time is challenging since remote sensing observations are not always sensitive to crop growth stages. The accuracy of crop phenology detection depends on the frequency and availability of cloud-free observations within the growing season. Recent satellite datasets such as the harmonized Landsat and Sentinel-2 (HLS) are promising for mapping crop phenology within the season over large areas. Operational applications in the near future are feasible.

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Hydrological assessment of atmospheric forcing uncertainty in the Euro-Mediterranean area using a land surface model

Hydrology and Earth System Sciences ◽

10.5194/hess-22-2091-2018 ◽

2018 ◽

Vol 22 (4) ◽

pp. 2091-2115 ◽

Cited By ~ 5

Author(s):

Emiliano Gelati ◽

Bertrand Decharme ◽

Jean-Christophe Calvet ◽

Marie Minvielle ◽

Jan Polcher ◽

...

Keyword(s):

Climate Change ◽

Remote Sensing ◽

Time Series ◽

Land Surface ◽

River Discharge ◽

Land Surface Model ◽

Mediterranean Area ◽

Surface Model ◽

Atmospheric Forcing ◽

Discharge Time

Abstract. Physically consistent descriptions of land surface hydrology are crucial for planning human activities that involve freshwater resources, especially in light of the expected climate change scenarios. We assess how atmospheric forcing data uncertainties affect land surface model (LSM) simulations by means of an extensive evaluation exercise using a number of state-of-the-art remote sensing and station-based datasets. For this purpose, we use the CO2-responsive ISBA-A-gs LSM coupled with the CNRM version of the Total Runoff Integrated Pathways (CTRIP) river routing model. We perform multi-forcing simulations over the Euro-Mediterranean area (25–75.5∘ N, 11.5∘ W–62.5∘ E, at 0.5∘ resolution) from 1979 to 2012. The model is forced using four atmospheric datasets. Three of them are based on the ERA-Interim reanalysis (ERA-I). The fourth dataset is independent from ERA-Interim: PGF, developed at Princeton University. The hydrological impacts of atmospheric forcing uncertainties are assessed by comparing simulated surface soil moisture (SSM), leaf area index (LAI) and river discharge against observation-based datasets: SSM from the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative projects (ESA-CCI), LAI of the Global Inventory Modeling and Mapping Studies (GIMMS), and Global Runoff Data Centre (GRDC) river discharge. The atmospheric forcing data are also compared to reference datasets. Precipitation is the most uncertain forcing variable across datasets, while the most consistent are air temperature and SW and LW radiation. At the monthly timescale, SSM and LAI simulations are relatively insensitive to forcing uncertainties. Some discrepancies with ESA-CCI appear to be forcing-independent and may be due to different assumptions underlying the LSM and the remote sensing retrieval algorithm. All simulations overestimate average summer and early-autumn LAI. Forcing uncertainty impacts on simulated river discharge are larger on mean values and standard deviations than on correlations with GRDC data. Anomaly correlation coefficients are not inferior to those computed from raw monthly discharge time series, indicating that the model reproduces inter-annual variability fairly well. However, simulated river discharge time series generally feature larger variability compared to measurements. They also tend to overestimate winter–spring high flows and underestimate summer–autumn low flows. Considering that several differences emerge between simulations and reference data, which may not be completely explained by forcing uncertainty, we suggest several research directions. These range from further investigating the discrepancies between LSMs and remote sensing retrievals to developing new model components to represent physical and anthropogenic processes.

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Observing traveling waves in glaciers with remote sensing: New flexible time series methods and application to Sermeq Kujalleq (Jakobshavn Isbræ), Greenland

10.5194/tc-2020-193 ◽

2020 ◽

Author(s):

Bryan Riel ◽

Brent Minchew ◽

Ian Joughin

Keyword(s):

Remote Sensing ◽

Time Series ◽

Traveling Waves ◽

Remote Sensing Data ◽

Surface Velocity ◽

Least Squares Regression ◽

Glacier Surface ◽

Sensing Data ◽

Velocity Variations ◽

Discontinuous Data

Abstract. The recent influx of remote sensing data provides new opportunities for quantifying spatiotemporal variations in glacier surface velocity and elevation fields. Here, we introduce a flexible time series reconstruction and decomposition technique for forming continuous, time-dependent surface velocity and elevation fields from discontinuous data and partitioning these time series into short- and long-term variations. The time series reconstruction consists of a sparsity-regularized least squares regression for modeling time series as a linear combination of generic basis functions of multiple temporal scales, allowing us to capture complex variations in the data using simple functions. We apply this method to the multitemporal evolution of Sermeq Kujalleq (Jakobshavn Isbrae), Greenland. Using 555 ice velocity maps generated by the Greenland Ice Mapping Project and covering the period 2009–2019, we show that the amplification in seasonal velocity variations in 2012–2016 was coincident with a longer-term speedup initiating in 2012. Similarly, the reduction in post-2017 seasonal velocity variations was coincident with a longer-term slowdown initiating around 2017. To understand how these perturbations propagate through the glacier, we introduce an approach for quantifying the spatially varying and frequency-dependent phase velocities and attenuation length scales of the resulting traveling waves. We hypothesize that these traveling waves are predominantly kinematic waves based on their long periods, coincident changes in surface velocity and elevation, and connection with variations in the terminus position. This ability to quantify wave propagation enables an entirely new framework for studying glacier dynamics using remote sensing data.

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