scholarly journals Fog Measurements with IR Whole Sky Imager and Doppler Lidar, Combined with In Situ Instruments

2021 ◽  
Vol 13 (16) ◽  
pp. 3320
Author(s):  
Ayala Ronen ◽  
Tamir Tzadok ◽  
Dorita Rostkier-Edelstein ◽  
Eyal Agassi
Keyword(s):  
Remote Sensing ◽  
Regional Scale ◽  
Ground Level ◽  
Doppler Lidar ◽  
Radiation Fog ◽  
Extensive Field ◽  
Rate Of Increase ◽  
Sky Imager ◽  
Lidar Equation

This study describes comprehensive measurements performed for four consecutive nights during a regional-scale radiation fog event in Israel’s central and southern areas in January 2021. Our data included both in situ measurements of droplets size distribution, visibility range, and meteorological parameters and remote sensing with a thermal IR Whole Sky Imager and a Doppler Lidar. This work is the first extensive field campaign aimed to characterize fog properties in Israel and is a pioneer endeavor that encompasses simultaneous remote sensing measurements and analysis of a fog event with a thermal IR Whole Sky Imager. Radiation fog, as monitored by the sensor’s field of view, reveals three distinctive properties that make it possible to identify it. First, it exhibits an azimuthal symmetrical shape during the buildup phase. Second, the zenith brightness temperature is very close to the ground-level air temperature. Lastly, the rate of increase in cloud cover up to a completely overcast sky is very fast. Additionally, we validated the use of a Doppler Lidar as a tool for monitoring fog by proving that the measured backscatter-attenuation vertical profile agrees with the calculation of the Lidar equation fed with data measured by in situ instruments. It is shown that fog can be monitored by those two, off-the-shelf-stand-off-sensing technologies that were not originally designed for fog purposes. It enables the monitoring of fog properties such as type, evolution with time and vertical depth, and opens the path for future works of studying the different types of fog events.

A data-driven approach for mapping global surface soil moisture at 100 m using high-resolution remote sensing data and land surface parameters

2021 ◽  
Author(s):  
Jingyi Huang ◽  
Ankur Desai ◽  
Jun Zhu ◽  
Alfred Hartemink ◽  
Paul Stoy ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Field Scale ◽  
Land Surface Parameters ◽  
Global Surface

<p>Current in situ soil moisture monitoring networks are sparsely distributed while remote sensing satellite soil moisture maps have a very coarse spatial resolution. In this study, an empirical global surface soil moisture (SSM) model was established via fusion of in situ continental and regional scale soil moisture networks, remote sensing data (SMAP and Sentinel-1) and high-resolution land surface parameters (e.g., soil texture, terrain) using a quantile random forest (QRF) algorithm. The model had a spatial resolution of 100m and performed moderately well under cultivated, herbaceous, forest, and shrub soils (R<sup>2</sup> = 0.524, RMSE = 0.07 m<sup>3</sup> m<sup>−3</sup>). It has a relatively good transferability at the regional scale among different continental and regional networks (mean RMSE = 0.08–0.10 m<sup>3</sup> m<sup>−3</sup>). The global model was then applied to map SSM dynamics at 30–100m across a field-scale network (TERENO-Wüstebach) in Germany and an 80-ha irrigated cropland in Wisconsin, USA. Without local training data, the model was able to delineate the variations in SSM at the field scale but contained large bias. With the addition of 10% local training datasets (“spiking”), the bias of the model was significantly reduced. The QRF model was also affected by the resolution and accuracy of soil maps. It was concluded that the empirical model has the potential to be applied elsewhere across the globe to map SSM at the regional to field scales for research and applications. Future research is required to improve the performance of the model by incorporating more field-scale soil moisture sensor networks and high-resolution soil maps as well as assimilation with process-based water flow models.</p>

scholarly journals Modeling Evapotranspiration during SMACEX: Comparing Two Approaches for Local- and Regional-Scale Prediction

2005 ◽  
Vol 6 (6) ◽  
pp. 910-922 ◽  
Author(s):  
H. Su ◽  
M. F. McCabe ◽  
E. F. Wood ◽  
Z. Su ◽  
J. H. Prueger
Keyword(s):  
Remote Sensing ◽  
Surface Energy ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Surface Fluxes ◽  
Heat Fluxes ◽  
High Quality ◽  
Sensing Data ◽  
Mean Square Errors

Abstract The Surface Energy Balance System (SEBS) model was developed to estimate land surface fluxes using remotely sensed data and available meteorology. In this study, a dual assessment of SEBS is performed using two independent, high-quality datasets that are collected during the Soil Moisture–Atmosphere Coupling Experiment (SMACEX). The purpose of this comparison is twofold. First, using high-quality local-scale data, model-predicted surface fluxes can be evaluated against in situ observations to determine the accuracy limit at the field scale using SEBS. To accomplish this, SEBS is forced with meteorological data derived from towers distributed throughout the Walnut Creek catchment. Flux measurements from 10 eddy covariance systems positioned on these towers are used to evaluate SEBS over both corn and soybean surfaces. These data allow for an assessment of modeled fluxes during a period of rapid vegetation growth and varied hydrometeorology. Results indicate that SEBS can predict evapotranspiration with accuracies approaching 10%–15% of that of the in situ measurements, effectively capturing the temporal development of surface flux patterns for both corn and soybean, even when the evaporative fraction ranges between 0.50 and 0.90. Second, utilizing high-resolution remote sensing data and operational meteorology, a catchment-scale examination of model performance is undertaken. To extend the field-based assessment of SEBS, information derived from the Landsat Enhanced Thematic Mapper (ETM) and data from the North American Land Data Assimilation System (NLDAS) were combined to determine regional surface energy fluxes for a clear day during the field experiment. Results from this analysis indicate that prediction accuracy was strongly related to crop type, with corn predictions showing improved estimates compared to those of soybean. Although root-mean-square errors were affected by the limited number of samples and one poorly performing soybean site, differences between the mean values of observations and SEBS Landsat-based predictions at the tower sites were approximately 5%. Overall, results from this analysis indicate much potential toward routine prediction of surface heat fluxes using remote sensing data and operational meteorology.

scholarly journals Tropospheric CH<sub>4</sub> signals as observed by NDACC FTIR at globally distributed sites and comparison to GAW surface in situ measurements

2014 ◽  
Vol 7 (7) ◽  
pp. 2337-2360 ◽  
Author(s):  
E. Sepúlveda ◽  
M. Schneider ◽  
F. Hase ◽  
S. Barthlott ◽  
D. Dubravica ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Correction Method ◽  
Atmospheric Composition ◽  
Small Scale ◽  
Mean Values ◽  
Sensing Data ◽  
Globally Distributed

Abstract. We present lower/middle tropospheric column-averaged CH4 mole fraction time series measured by nine globally distributed ground-based FTIR (Fourier transform infrared) remote sensing experiments of the Network for the Detection of Atmospheric Composition Change (NDACC). We show that these data are well representative of the tropospheric regional-scale CH4 signal, largely independent of the local surface small-scale signals, and only weakly dependent on upper tropospheric/lower stratospheric (UTLS) CH4 variations. In order to achieve the weak dependency on the UTLS, we use an a posteriori correction method. We estimate a typical precision for daily mean values of about 0.5% and a systematic error of about 2.5%. The theoretical assessments are complemented by an extensive empirical study. For this purpose, we use surface in situ CH4 measurements made within the Global Atmosphere Watch (GAW) network and compare them to the remote sensing data. We briefly discuss different filter methods for removing the local small-scale signals from the surface in situ data sets in order to obtain the in situ regional-scale signals. We find good agreement between the filtered in situ and the remote sensing data. The agreement is consistent for a variety of timescales that are interesting for CH4 source/sink research: day-to-day, monthly, and inter-annual. The comparison study confirms our theoretical estimations and proves that the NDACC FTIR measurements can provide valuable data for investigating the cycle of CH4.

Monitoring and Mapping of Soil and Snow Water Across Scales with Cosmic-Ray Neutron Sensor Networks and Mobile Platforms

2020 ◽  
Author(s):  
Martin Schrön ◽  
Sascha E Oswald ◽  
Steffen Zacharias ◽  
Peter Dietrich ◽  
Sabine Attinger
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Cosmic Ray ◽  
Ongoing Research ◽  
Sensing Data ◽  
Research Activities ◽  
Snow Water

&lt;p&gt;Cosmic-ray neutron albedo sensing (CRNS) is a modern technology that can be used to non-invasively measure the average water content in the environment (i.e., in soil, snow, or vegetation). The sensor footprint encompasses an area of 10-15 hectares and extends tens of decimeters deep into the soil. This method might have the potential to bridge the scale gap between conventional in-situ sensors and remote-sensing data in both, the horizontal and the vertical domain.&lt;/p&gt;&lt;p&gt;Currently, more than 200 sensors are operated in the growing networks of national and continental observatories. While single CRNS stations are continuously monitoring the local water dynamics at fixed field sites, mobile CRNS platforms are used for on-demand soil moisture mapping at the regional scale. The sensors are rapidly operational on any ground- or airborne vehicle. The data is particularly useful to study hydrological extreme events, heatwaves, and snow melt/accumulation, and it is being applied in hydrological models and agricultural irrigation management.&lt;/p&gt;&lt;p&gt;In the presentation we will explore the potential of the CRNS method to support and complement in-situ and remote-sensing data for hydrological event monitoring. We will discuss ongoing research activities that are aimed at improving the operationality, frequency, and spatial extend of CRNS measurements. New measurement strategies that are currently explored are, for example: dense clusters of 20 CRNS stations fully covering a 100 hectare catchment; heat wave monitoring with mobile car-based CRNS; regular soil/snow water mapping using mobile CRNS on cars and trains; and airborne surveys using CRNS on gyrocopters.&lt;/p&gt;&lt;p&gt;Future CRNS observations could provide a valuable contribution to the multi-sensor approach, e.g. to help tracking and characterizing surface water movement, to map regional-scale soil moisture patterns, or to calibrate and evaluate satellite data.&lt;/p&gt;

scholarly journals Using remote-sensing data to determine equilibrium-line altitude and mass-balance time series: validation on three French glaciers, 1994–2002

2005 ◽  
Vol 51 (175) ◽  
pp. 539-546 ◽  
Author(s):  
Antoine Rabatel ◽  
Jean-Pierre Dedieu ◽  
Christian Vincent
Keyword(s):  
Remote Sensing ◽  
Mass Balance ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Ground Level ◽  
Equilibrium Line ◽  
Glacier Mass Balance ◽  
Mountain Range ◽  
Equilibrium Line Altitude ◽  
Sensing Data

AbstractAlpine glaciers are very sensitive to climate fluctuations, and their mass balance can be used as an indicator of regional-scale climate change. Here, we present a method to calculate glacier mass balance using remote-sensing data. Snowline measurements from remotely sensed images recorded at the end of the hydrological year provide an effective proxy of the equilibrium line. Mass balance can be deduced from the equilibrium-line altitude (ELA) variations. Three well-documented glaciers in the French Alps, where the mass balance is measured at ground level with a stake network, were selected to assess the accuracy of the method over the 1994–2002 period (eight mass-balance cycles). Results obtained by ground measurements and remote sensing are compared and show excellent correlation (r2 > 0.89), both for the ELA and for the mass balance, indicating that the remote-sensing method can be applied to glaciers where no ground data exist, on the scale of a mountain range or a given climatic area. The main differences can be attributed to discrepancies between the dates of image acquisition and field measurements. Cloud cover and recent snowfalls constitute the main restrictions of the image-based method.

scholarly journals Evapotranspiration estimate from water balance closure using satellite data for the Upper Yangtze River basin

2013 ◽  
Vol 45 (4-5) ◽  
pp. 603-614 ◽  
Author(s):  
C. Corbari ◽  
M. Mancini ◽  
Z. Su ◽  
J. Li
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
River Basin ◽  
Satellite Data ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Regional Scale ◽  
Upper Yangtze River ◽  
The Upper Yangtze River

Application of hydrological models for water resources management at large continental river basins is often limited by the scarcity of in situ meteorological forcing data. Remote sensing information provides an alternative to in situ data, with observations that are, in some cases, at higher spatial and temporal resolutions than those available from traditional ground sources. In this work, the water balance equation is solved using precipitation retrieved from Tropical Rainfall Measuring Mission, water storage from Gravity Recovery and Climate Experiment satellite data and ground discharge. Evapotranspiration (ET) is then computed as a residual term of the water balance. Satellite data are compared with ground data to understand to what extent remote sensing observations can be used to improve estimates of the terrestrial water balance at regional scale. ET estimates are also compared with the ET computed from a detailed distributed energy water balance model and with the ET product from the Moderate Resolution Imaging Spectroradiometer Global Evapotranspiration Project. These analyses are performed for the Upper Yangtze River basin (China) in the framework of NRSCC-ESA DRAGON-2 Programme.

scholarly journals Monitoring presence and streaming patterns of Icelandic volcanic ash during its arrival to Slovenia

2011 ◽  
Vol 8 (2) ◽  
pp. 3863-3898 ◽  
Author(s):  
F. Gao ◽  
S. Stanič ◽  
K. Bergant ◽  
T. Bolte ◽  
F. Coren ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chemical Analysis ◽  
Atmospheric Aerosol ◽  
Volcanic Ash ◽  
Ground Level ◽  
In Situ Measurements ◽  
Volcanic Origin ◽  
Air Masses ◽  
X Ray

Abstract. The eruption of the Eyjafjallajökull volcano starting on 14 April 2010 resulted in the spreading of volcanic ash over most parts of Europe. In Slovenia, the presence of volcanic ash was monitored using ground-based in-situ measurements, lidar-based remote sensing and airborne in-situ measurements. Volcanic origin of the detected aerosols was confirmed by subsequent spectral and chemical analysis of the collected samples. The initial arrival of volcanic ash to Slovenia was detected at ground level using in-situ measurements during the night of 17 April 2010, but was not observed via lidar-based remote sensing due to the presence of clouds at lower altitudes while the streaming height of ash-loaded air masses was above 5 km a.s.l. The second arrival of volcanic ash on 20 April 2010 was detected by both lidar-based remote sensing and airborne in-situ measurement, revealing two or more elevated atmospheric aerosol layers above Slovenia. Identification of samples from ground-based in-situ and airborne in-situ measurements based on energy-dispersive X-ray spectroscopy confirmed that a fraction of particles was volcanic ash from the Eyjafjallajökull eruption. We performed simulations of airflow trajectories to explain the arrival of the air masses containing volcanic ash to Slovenia.

scholarly journals Assessing Uncertainties in Estimating Surface Energy Fluxes from Remote Sensing over Natural Grasslands in Brazil

2021 ◽  
Author(s):  
Pâmela Suélen Käfer ◽  
Nájila Souza da Rocha ◽  
Gustavo Pujol Veeck ◽  
Lucas Ribeiro Diaz ◽  
Savannah Tâmara Lemos da Costa ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Regional Scale ◽  
Late Summer ◽  
Conservation Strategies ◽  
Accurate Information ◽  
Energy Fluxes ◽  
In Situ Data ◽  
Natural Grasslands

Abstract Evapotranspiration (ET) is one of the main fluxes in the global water cycle. As the Pampa biome carries a rich biodiversity, accurate information on the ET dynamics is essential to support its proper monitoring and establish conservation strategies. In this context, we assessed an operational methodology based on the S-SEBI model to estimate energy fluxes over the natural grasslands of the Brazilian Pampa between 2014-2019. The S-SEBI is a remote sensing-based ET model that requires a minimum of meteorological inputs, being fully applicable for regions lacking in situ observations. Therefore, we investigated the model performance considering radiation data from both ERA5 reanalysis and Eddy Covariance measurements. Furthermore, comparisons from satellite-based estimates with in situ data were performed with and without energy balance closure (EBC). Results showed that the RMSE is impacted in 0.32 mm/day when reanalysis data are used, indicating that the meteorological inputs have low sensitivity on daily ET estimates from the S-SEBI model. In contrast, the instantaneous energy balance components are more affected. The strong climate seasonality of the Brazilian Pampa impacts the evaporative fraction, which is more evident in late summer and autumn and may compromise the performance of the model. The effects in the daily ET are lower when in situ data without EBC are considered as ground truth. However, they are less correlated with the remote sensing-based estimates. These insights are useful to monitor water and energy fluxes from local to regional scale and provide the opportunity to capture ET trends over the biome.

scholarly journals Tropospheric CH<sub>4</sub> signals as observed by NDACC FTIR at globally distributed sites and comparison to GAW surface in-situ measurements

2014 ◽  
Vol 7 (1) ◽  
pp. 633-701 ◽  
Author(s):  
E. Sepúlveda ◽  
M. Schneider ◽  
F. Hase ◽  
S. Barthlott ◽  
D. Dubravica ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Correction Method ◽  
Atmospheric Composition ◽  
Small Scale ◽  
Mean Values ◽  
Sensing Data ◽  
Globally Distributed

Abstract. We present lower/middle tropospheric column-averaged CH4 mole fraction time series measured by nine globally distributed ground-based FTIR (Fourier Transform InfraRed) remote sensing experiments of the Network for the Detection of Atmospheric Composition Change (NDACC). We show that these data are well representative of the tropospheric regional-scale CH4 signal, largely independent of the local small-scale signals of the boundary layer, and only weakly dependent on upper tropospheric/lower stratospheric (UTLS) CH4 variations. In order to achieve the weak dependency on the UTLS, we use an a posteriori correction method. We estimate a typical precision for daily mean values of about 0.5% and a systematic error of about 2.5%. The theoretical assessments are complemented by an extensive empirical study. For this purpose, we use surface in-situ CH4 measurements made within the Global Atmosphere Watch (GAW) network and compare them to the remote sensing data. We briefly discuss different filter methods for removing the local small-scale signals from the surface in-situ datasets in order to obtain the in-situ regional-scale signals. We find good agreement between the filtered in-situ and the remote sensing data. The agreement is consistent for a variety of time scales that are interesting for CH4 source/sink research: day-to-day, monthly, and inter-annual. The comparison study confirms our theoretical estimations and proves that the NDACC FTIR measurements can provide valuable data for investigating the cycle of CH4.

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