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 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.

scholarly journals Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data

2021 ◽  
Vol 13 (9) ◽  
pp. 1715
Author(s):  
Foyez Ahmed Prodhan ◽  
Jiahua Zhang ◽  
Fengmei Yao ◽  
Lamei Shi ◽  
Til Prasad Pangali Sharma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
South Asia ◽  
Model Performance ◽  
Remote Sensing Data ◽  
Agricultural Drought ◽  
Gradient Boosting ◽  
Complex Nature ◽  
Sensing Data

Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.

scholarly journals ESTIMATION OF SURFACE SNOW WETNESS USING SENTINEL-2 MULTISPECTRAL DATA

2018 ◽  
Vol IV-5 ◽  
pp. 223-228 ◽  
Author(s):  
D. Varade ◽  
O. Dikshit
Keyword(s):  
Remote Sensing ◽  
Water Resource Management ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Physical Relationship ◽  
Sensing Data ◽  
Surface Snow ◽  
Sentinel 2

<p><strong>Abstract.</strong> Snow cover characterization and estimation of snow geophysical parameters is a significant area of research in water resource management and surface hydrological processes. With advances in spaceborne remote sensing, much progress has been achieved in the qualitative and quantitative characterization of snow geophysical parameters. However, most of the methods available in the literature are based on the microwave backscatter response of snow. These methods are mostly based on the remote sensing data available from active microwave sensors. Moreover, in alpine terrains, such as in the Himalayas, due to the geometrical distortions, the missing data is significant in the active microwave remote sensing data. In this paper, we present a methodology utilizing the multispectral observations of Sentinel-2 satellite for the estimation of surface snow wetness. The proposed approach is based on the popular triangle method which is significantly utilized for the assessment of soil moisture. In this case, we develop a triangular feature space using the near infrared (NIR) reflectance and the normalized differenced snow index (NDSI). Based on the assumption that the NIR reflectance is linearly related to the liquid water content in the snow, we derive a physical relationship for the estimation of snow wetness. The modeled estimates of snow wetness from the proposed approach were compared with in-situ measurements of surface snow wetness. A high correlation determined by the coefficient of determination of 0.94 and an error of 0.535 was observed between the proposed estimates of snow wetness and in-situ measurements.</p>

Cloud-based Research Data Infrastructures Integrating In-Situ and Remote Sensing Data

2021 ◽  
Author(s):  
Simon Jirka ◽  
Benedikt Gräler ◽  
Matthes Rieke ◽  
Christian Autermann
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Research Data ◽  
Monitoring Networks ◽  
Sensing Data ◽  
Data Infrastructures ◽  
Practical Experiences ◽  
The One ◽  
Cloud Infrastructures

&lt;p&gt;For many scientific domains such as hydrology, ocean sciences, geophysics and social sciences, geospatial observations are an important source of information. Scientists conduct extensive measurement campaigns or operate comprehensive monitoring networks to collect data that helps to understand and to model current and past states of complex environment. The variety of data underpinning research stretches from in-situ observations to remote sensing data (e.g., from the European Copernicus programme) and contributes to rapidly increasing large volumes of geospatial data.&lt;/p&gt;&lt;p&gt;However, with the growing amount of available data, new challenges arise. Within our contribution, we will focus on two specific aspects: On the one hand, we will discuss the specific challenges which result from the large volumes of remote sensing data that have become available for answering scientific questions. For this purpose, we will share practical experiences with the use of cloud infrastructures such as the German platform CODE-DE and will discuss concepts that enable data processing close to the data stores. On the other hand, we will look into the question of interoperability in order to facilitate the integration and collaborative use of data from different sources. For this aspect, we will give special consideration to the currently emerging new generation of standards of the Open Geospatial Consortium (OGC) and will discuss how specifications such as the OGC API for Processes can help to provide flexible processing capabilities directly within Cloud-based research data infrastructures.&lt;/p&gt;

scholarly journals Data assimilation of in situ and satellite remote sensing data to 3D hydrodynamic lake models: a case study using Delft3D-FLOW v4.03 and OpenDA v2.4

2020 ◽  
Vol 13 (3) ◽  
pp. 1267-1284 ◽  
Author(s):  
Theo Baracchini ◽  
Philip Y. Chu ◽  
Jonas Šukys ◽  
Gian Lieberherr ◽  
Stefan Wunderle ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Satellite Remote Sensing ◽  
Model Performance ◽  
Remote Sensing Data ◽  
Lake Ecosystem ◽  
Sensing Data ◽  
Localization Scheme ◽  
Spatiotemporal Scales

Abstract. The understanding of physical dynamics is crucial to provide scientifically credible information on lake ecosystem management. We show how the combination of in situ observations, remote sensing data, and three-dimensional hydrodynamic (3D) numerical simulations is capable of resolving various spatiotemporal scales involved in lake dynamics. This combination is achieved through data assimilation (DA) and uncertainty quantification. In this study, we develop a flexible framework by incorporating DA into 3D hydrodynamic lake models. Using an ensemble Kalman filter, our approach accounts for model and observational uncertainties. We demonstrate the framework by assimilating in situ and satellite remote sensing temperature data into a 3D hydrodynamic model of Lake Geneva. Results show that DA effectively improves model performance over a broad range of spatiotemporal scales and physical processes. Overall, temperature errors have been reduced by 54 %. With a localization scheme, an ensemble size of 20 members is found to be sufficient to derive covariance matrices leading to satisfactory results. The entire framework has been developed with the goal of near-real-time operational systems (e.g., integration into meteolakes.ch).

scholarly journals Remote Sensing Approaches and Related Techniques to Map and Study Landslides

2020 ◽  
Author(s):  
Ram L. Ray ◽  
Maurizio Lazzari ◽  
Tolulope Olutimehin
Keyword(s):  
Remote Sensing ◽  
Hazard Assessment ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Landslide Monitoring ◽  
In Situ Observation ◽  
Remotely Sensed Data ◽  
Sensing Data ◽  
Stability Model

Landslide is one of the costliest and fatal geological hazards, threatening and influencing the socioeconomic conditions in many countries globally. Remote sensing approaches are widely used in landslide studies. Landslide threats can also be investigated through slope stability model, susceptibility mapping, hazard assessment, risk analysis, and other methods. Although it is possible to conduct landslide studies using in-situ observation, it is time-consuming, expensive, and sometimes challenging to collect data at inaccessible terrains. Remote sensing data can be used in landslide monitoring, mapping, hazard prediction and assessment, and other investigations. The primary goal of this chapter is to review the existing remote sensing approaches and techniques used to study landslides and explore the possibilities of potential remote sensing tools that can effectively be used in landslide studies in the future. This chapter also provides critical and comprehensive reviews of landslide studies focus¬ing on the role played by remote sensing data and approaches in landslide hazard assessment. Further, the reviews discuss the application of remotely sensed products for landslide detection, mapping, prediction, and evaluation around the world. This systematic review may contribute to better understanding the extensive use of remotely sensed data and spatial analysis techniques to conduct landslide studies at a range of scales.

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