Quantifying the carbon balance of managed grasslands in near-real time and at field scale by using satellite data and biogeochemical modelling

2021 ◽  
Author(s):  
Vasilis Myrgiotis ◽  
Mathew Williams
Keyword(s):  
Real Time ◽  
Satellite Data ◽  
Carbon Balance ◽  
Field Scale

scholarly journals Prediction of Solar Power Using Near-Real Time Satellite Data

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5865
Author(s):  
Abhnil Amtesh Prasad ◽  
Merlinde Kay
Keyword(s):  
Real Time ◽  
Satellite Data ◽  
Solar Power ◽  
Short Term ◽  
Motion Vectors ◽  
Clear Sky ◽  
Cloud Motion ◽  
Visible Images ◽  
Grid Operators ◽  
Short Term Prediction

Solar energy production is affected by the attenuation of incoming irradiance from underlying clouds. Often, improvements in the short-term predictability of irradiance using satellite irradiance models can assist grid operators in managing intermittent solar-generated electricity. In this paper, we develop and test a satellite irradiance model with short-term prediction capabilities using cloud motion vectors. Near-real time visible images from Himawari-8 satellite are used to derive cloud motion vectors using optical flow estimation techniques. The cloud motion vectors are used for the advection of pixels at future time horizons for predictions of irradiance at the surface. Firstly, the pixels are converted to cloud index using the historical satellite data accounting for clear, cloudy and cloud shadow pixels. Secondly, the cloud index is mapped to the clear sky index using a historical fitting function from the respective sites. Thirdly, the predicated all-sky irradiance is derived by scaling the clear sky irradiance with a clear sky index. Finally, a power conversion model trained at each site converts irradiance to power. The prediction of solar power tested at four sites in Australia using a one-month benchmark period with 5 min ahead prediction showed that errors were less than 10% at almost 34–60% of predicted times, decreasing to 18–26% of times under live predictions, but it outperformed persistence by >50% of the days with errors <10% for all sites. Results show that increased latency in satellite images and errors resulting from the conversion of cloud index to irradiance and power can significantly affect the forecasts.

An integrated decision support system using satellite and in-situ data for coastal area hazard mitigation and resilience to natural disasters

2021 ◽  
Author(s):  
Christos Kontopoulos ◽  
Nikos Grammalidis ◽  
Dimitra Kitsiou ◽  
Vasiliki Charalampopoulou ◽  
Anastasios Tzepkenlis ◽  
...  
Keyword(s):  
Decision Support ◽  
Surface Water ◽  
Decision Support System ◽  
Real Time ◽  
Support System ◽  
Satellite Data ◽  
Hazard Mitigation ◽  
Google Earth ◽  
Landsat 8

&lt;p&gt;Nowadays, the importance of coastal areas is greater than ever, with approximately 10% of the global population living in these areas. These zones are an intermediate space between sea and land and are exposed to a variety of natural (e.g. ground deformation, coastal erosion, flooding, tornados, sea level rise, etc.) and anthropogenic (e.g. excessive urbanisation) hazards. Therefore, their conservation and proper sustainable management is deemed crucial both for economic and environmental purposes. The main goal of the Greece-China bilateral research project &amp;#8220;EPIPELAGIC: ExPert Integrated suPport systEm for coastaL mixed urbAn &amp;#8211; industrial &amp;#8211; critical infrastructure monitorinG usIng Combined technologies&amp;#8221; is the design and deployment of an integrated Decision Support System (DSS) for hazard mitigation and resilience. The system exploits near-real time data from both satellite and in-situ sources to efficiently identify and produce alerts for important risks (e.g. coastal flooding, soil erosion, degradation, subsidence), as well as to monitor other important changes (e.g. urbanization, coastline). To this end, a robust methodology has been defined by fusing satellite data (Optical/multispectral, SAR, High Resolution imagery, DEMs etc.) and in situ real-time measurements (tide gauges, GPS/GNSS etc.). For the satellite data pre-processing chain, image composite/mosaic generation techniques will be implemented via Google Earth Engine (GEE) platform in order to access Sentinel 1, Sentinel 2, Landsat 5 and Landsat 8 imagery for the studied time period (1991-2021). These optical and SAR composites will be stored into the main database of the EPIPELAGIC server, after all necessary harmonization and correction techniques, along with other products that are not yet available in GEE (e.g. ERS or Sentinel-1 SLC products) and will have to be locally processed. A Machine Learning (ML) module, using data from this main database will be trained to extract additional high-level information (e.g. coastlines, surface water, urban areas, etc.). Both conventional (e.g. Otsu thresholding, Random Forest, Simple Non-Iterative Clustering (SNIC) algorithm, etc.) and deep learning approaches (e.g. U-NET convolutional networks) will be deployed to address problems such as surface water detection and land cover/use classification. Additionally, in-situ or auxiliary/cadastral datasets will be used as ground truth data. Finally, a Decision Support System (DSS), will be developed to periodically monitor the evolution of these measurements, detect significant changes that may indicate impending risks and hazards, and issue alarms along with suggestions for appropriate actions to mitigate the detected risks. Through the project, the extensive use of Explainable Artificial Intelligence (xAI) techniques will also be investigated in order to provide &amp;#8220;explainable recommendations&amp;#8221; that will significantly facilitate the users to choose the optimal mitigation approach. The proposed integrated monitoring solutions is currently under development and will be applied in two Areas of Interest, namely Thermaic Gulf in Thessaloniki, Greece, and the Yellow River Delta in China. They are expected to provide valuable knowledge, methodologies and modern techniques for exploring the relevant physical mechanisms and offer an innovative decision support tool. Additionally, all project related research activities will provide ongoing support to the local culture, society, economy and environment in both involved countries, Greece and China.&lt;/p&gt;

Delivering real-time satellite data to a broader audience

2009 ◽  
Author(s):  
Lan Zhao ◽  
Shuang Wu ◽  
Rakesh Veeramacheneni ◽  
Carol X. Song ◽  
Larry Biehl
Keyword(s):  
Real Time ◽  
Satellite Data

scholarly journals Evapotranspiration Partitioning at Field Scales Using TSEB and Multi-Satellite Data Fusion in The Middle Reaches of Heihe River Basin, Northwest China

2020 ◽  
Vol 12 (19) ◽  
pp. 3223
Author(s):  
Yan Li ◽  
Chunlin Huang ◽  
William P. Kustas ◽  
Hector Nieto ◽  
Liang Sun ◽  
...  
Keyword(s):  
Data Fusion ◽  
River Basin ◽  
Satellite Data ◽  
Temporal Dynamics ◽  
Surface Flux ◽  
Northwest China ◽  
Heihe River Basin ◽  
Heihe River ◽  
Field Scale ◽  
Natural Ecosystems

Daily evapotranspiration (ET) and its components of evaporation (E) and transpiration (T) at field scale are often required for improving agricultural water management and maintaining ecosystem health, especially in semiarid and arid regions. In this study, multi-year daily ET, E, and T at a spatial resolution of 100 m in the middle reaches of Heihe River Basin were computed based on an ET partitioning method developed by combing remote sensing-based ET model and multi-satellite data fusion methodology. Evaluations using flux tower measurements over irrigated cropland and natural desert sites indicate that this method can provide reliable estimates of surface flux partitioning and daily ET. Modeled daily ET yielded root mean square error (RMSE) values of 0.85 mm for cropland site and 0.84 mm for desert site, respectively. The E and T partitioning capabilities of this proposed method was further assessed by using ratios E/ET and T/ET derived from isotopic technology at the irrigated cropland site. Results show that apart from early in the growing season when the actual E was reduced by plastic film mulching, the modeled E/ET and T/ET agree well with observations in terms of both magnitude and temporal dynamics. The multi-year seasonal patterns of modeled ET, E, and T at field scale from this ET partitioning method shows reasonable seasonal variation and spatial variability, which can be used for monitoring plant water consumption in both agricultural and natural ecosystems.

scholarly journals Construction and Application of Snow-melting Runoff Model Based on MODIS Satellite Data

2018 ◽  
Vol 246 ◽  
pp. 01100
Author(s):  
Jian Chen ◽  
Kai Shu ◽  
Jianping Wang ◽  
Chunhong Li ◽  
Feng Wang
Keyword(s):  
Snow Cover ◽  
Real Time ◽  
Relative Error ◽  
Satellite Data ◽  
Snow Melting ◽  
Melting Snow ◽  
Kaidu River ◽  
Basin Area ◽  
Runoff Model ◽  
Operation Scheduling

It is very complicated to accurately describe the process of watershed runoff yield and concentration, which is comprehensive influenced by snow covering, temperature, precipitation, the wetland areas and other factors in the basin of Kaidu River upstream of Chahanwusu Reservoir. It is that real-time updating MODIS satellite snow cover products MOD10A2 and 30 meters by 30 meters of DEM data are applied to calculate elevation~ basin area ~ snow covering area curve, virtual free reservoir is put forward to simulate the wetlands concentration of upstream Bayinbuluke and sahentuohai hydrological gauge stations and mixed melting snow and runoff yield under saturated storage concentration model is constructed in this article. The model behaved good to simulate the Inflow process of Chahanwusu Reservoir, and the relative error between simulated and measured processes reached 83.79%, the deterministic coefficient reaches about 0.8, which is better supporting Chahanwusu Reservoir’s operation scheduling and dispatch decision.

Estimation of soil properties at the field scale from satellite data: a comparison between spatial and non-spatial techniques

2014 ◽  
Vol 65 (6) ◽  
pp. 842-851 ◽  
Author(s):  
F. Castaldi ◽  
R. Casa ◽  
A. Castrignanò ◽  
S. Pascucci ◽  
A. Palombo ◽  
...  
Keyword(s):  
Soil Properties ◽  
Satellite Data ◽  
Field Scale
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