Evapotranspiration Modeling Using Remote Sensing and Empirical Models in the Fogera Floodplain, Ethiopia

2011 ◽  
pp. 163-178 ◽  
Author(s):  
Temesgen Enku ◽  
Christiaan van der Tol ◽  
Ambro S.M. Gieske ◽  
Tom H.M. Rientjes
Keyword(s):  
Remote Sensing ◽  
Empirical Models ◽  
Evapotranspiration Modeling

Pursuing Forecasts of the Behavior and Arrival of Coronal Mass Ejections through Modeling and Observations

2017 ◽  
Vol 13 (S335) ◽  
pp. 58-64 ◽  
Author(s):  
Hebe Cremades
Keyword(s):  
Remote Sensing ◽  
Coronal Mass Ejections ◽  
Empirical Models ◽  
Time Of Arrival ◽  
Data Sets ◽  
Radio Waves ◽  
Weather Forecasts ◽  
The Past ◽  
Increasing Demand ◽  
Solar Disturbances

AbstractSophisticated instrumentation dedicated to studying and monitoring our Sun’s activity has proliferated in the past few decades, together with the increasing demand of specialized space weather forecasts that address the needs of commercial and government systems. As a result, theoretical and empirical models and techniques of increasing complexity have been developed, aimed at forecasting the occurrence of solar disturbances, their evolution, and time of arrival to Earth. Here we will review groundbreaking and recent methods to predict the propagation and evolution of coronal mass ejections and their driven shocks. The methods rely on a wealth of data sets provided by ground- and space-based observatories, involving remote-sensing observations of the corona and the heliosphere, as well as detections of radio waves.

Total ozone mapping by integrating databases from remote sensing instruments and empirical models

2004 ◽  
Vol 42 (5) ◽  
pp. 991-1008 ◽  
Author(s):  
G. Christakos ◽  
A. Kolovos ◽  
M.L. Serre ◽  
F. Vukovich
Keyword(s):  
Remote Sensing ◽  
Total Ozone ◽  
Empirical Models

Soil water content estimation using a remote sensing based hybrid evapotranspiration modeling approach

2012 ◽  
Vol 50 ◽  
pp. 152-161 ◽  
Author(s):  
Christopher M.U. Neale ◽  
Hatim M.E. Geli ◽  
William P. Kustas ◽  
Joseph G. Alfieri ◽  
Prasanna H. Gowda ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Modeling Approach ◽  
Evapotranspiration Modeling

scholarly journals Investigating Live Fuel Moisture Content Estimation in Fire-Prone Shrubland from Remote Sensing Using Empirical Modelling and RTM Simulations

2020 ◽  
Vol 12 (14) ◽  
pp. 2251 ◽  
Author(s):  
Eva Marino ◽  
Marta Yebra ◽  
Mariluz Guillén-Climent ◽  
Nur Algeet ◽  
José Luis Tomé ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Moisture Content ◽  
Empirical Model ◽  
Fire Management ◽  
Empirical Models ◽  
Spectral Information ◽  
Fuel Moisture ◽  
Fuel Moisture Content ◽  
Sentinel 2

Previous research has demonstrated that remote sensing can provide spectral information related to vegetation moisture variations essential for estimating live fuel moisture content (LFMC), but accuracy and timeliness still present challenges to using this information operationally. Consequently, many regional administrations are investing important resources in field campaigns for LFMC monitoring, often focusing on indicator species to reduce sampling time and costs. This paper compares different remote sensing approaches to provide LFMC prediction of Cistus ladanifer, a fire-prone shrub species commonly found in Mediterranean areas and used by fire management services as an indicator species for wildfire risk assessment. Spectral indices (SI) were derived from satellite imagery of different spectral, spatial, and temporal resolution, including Sentinel-2 and two different reflectance products of the Moderate Resolution Imaging Spectrometer (MODIS); MCD43A4 and MOD09GA. The SI were used to calibrate empirical models for LFMC estimation using on ground field LFMC measurements from a monospecific shrubland area located in Madrid (Spain). The empirical models were fitted with different statistical methods: simple (LR) and multiple linear regression (MLR), non-linear regression (NLR), and general additive models with splines (GAMs). MCD43A4 images were also used to estimate LFMC from the inversion of radiative transfer models (RTM). Empirical model predictions and RTM simulations of LFMC were validated and compared using an independent sample of LFMC values observed in the field. Empirical models derived from MODIS products and Sentinel-2 data showed R2 between estimated and observed LFMC from 0.72 to 0.75 and mean absolute errors ranging from 11% to 13%. GAMs outperformed regression methods in model calibration, but NLR had better results in model validation. LFMC derived from RTM simulations had a weaker correlation with field data (R2 = 0.49) than the best empirical model fitted with MCD43A4 images (R2 = 0.75). R2 between observations and LFMC derived from RTM ranged from 0.56 to 0.85 when the validation was performed for each year independently. However, these values were still lower than the equivalent statistics using the empirical models (R2 from 0.65 to 0.94) and the mean absolute errors per year for RTM were still high (ranging from 25% to 38%) compared to the empirical model (ranging 7% to 15%). Our results showed that spectral information derived from Sentinel-2 and different MODIS products provide valuable information for LFMC estimation in C. ladanifer shrubland. However, both empirical and RTM approaches tended to overestimate the lowest LFMC values, and therefore further work is needed to improve predictions, especially below the critical LFMC threshold used by fire management services to indicate higher flammability (<80%). Although lower extreme LFMC values are still difficult to estimate, the proposed empirical models may be useful to identify when the critical threshold for high fire risk has been reached with reasonable accuracy. This study demonstrates that remote sensing data is a promising source of information to derive reliable and cost-effective LFMC estimation models that can be used in operational wildfire risk systems.

scholarly journals Pseudo-nitzschia Blooms in a Coastal Upwelling System: Remote Sensing Detection, Toxicity and Environmental Variables

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1954 ◽  
Author(s):  
Jesus M. Torres Palenzuela ◽  
Luis González Vilas ◽  
Francisco M. Bellas ◽  
Elina Garet ◽  
África González-Fernández ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Domoic Acid ◽  
Coastal Upwelling ◽  
Empirical Models ◽  
Full Resolution ◽  
Shellfish Farming ◽  
Sensing Model ◽  
Key Variables

The NW coast of the Iberian Peninsula is dominated by extensive shellfish farming, which places this region as a world leader in mussel production. Harmful algal blooms in the area frequent lead to lengthy harvesting closures threatening food security. This study developed a framework for the detection of Pseudo-nitzschia blooms in the Galician rias from satellite data (MERIS full-resolution images) and identified key variables that affect their abundance and toxicity. Two events of toxin-containing Pseudo-nitzschia were detected (up to 2.5 μg L−1 pDA) in the area. This study suggests that even moderate densities of Pseudo-nitzschia in this area might indicate high toxin content. Empirical models for particulate domoic acid (pDA) were developed based on MERIS FR data. The resulting remote-sensing model, including MERIS bands centered around 510, 560, and 620 nm explain 73% of the pDA variance (R2 = 0.73, p < 0.001). The results show that higher salinity values and lower Si(OH)4/N ratios favour higher Pseudo-nitzschia spp. abundances. High pDA values seem to be associated with relatively high PO43, low NO3− concentrations, and low Si(OH)4/N. While MERIS FR data and regionally specific algorithms can be useful for detecting Pseudo-nitzschia blooms, nutrient relationships are crucial for predicting the toxicity of these blooms.

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