scholarly journals Upscaling of evapotranspiration fluxes from instantaneous to daytime scales for thermal remote sensing applications

2014 ◽  
Vol 18 (5) ◽  
pp. 1885-1894 ◽  
Author(s):  
C. Cammalleri ◽  
M. C. Anderson ◽  
W. P. Kustas
Keyword(s):  
Remote Sensing ◽  
Solar Radiation ◽  
Eddy Covariance ◽  
Time Of Day ◽  
Added Value ◽  
Surface Solar Radiation ◽  
Evaluation Approach ◽  
Sensing Applications ◽  
Wide Range ◽  
Systematic Biases

Abstract. Four upscaling methods for estimating daytime actual evapotranspiration (ET) from single time-of-day snapshots, as commonly retrieved using remote sensing, were compared. These methods assume self-preservation of the ratio between ET and a given reference variable over the daytime hours. The analysis was performed using eddy covariance data collected at 12 AmeriFlux towers, sampling a fairly wide range in climatic and land cover conditions. The choice of energy budget closure method significantly impacted performance using different scaling methodologies. Therefore, a statistical evaluation approach was adopted to better account for the inherent uncertainty in ET fluxes using eddy covariance technique. Overall, this approach suggested that at-surface solar radiation was the most robust reference variable amongst those tested, due to high accuracy of upscaled fluxes and absence of systematic biases. Top-of-atmosphere irradiance was also tested and proved to be reliable under near clear-sky conditions, but tended to overestimate the observed daytime ET during cloudy days. Use of reference ET as a scaling flux yielded higher bias than the solar radiation method, although resulting errors showed similar lack of seasonal dependence. Finally, the commonly used evaporative fraction method yielded satisfactory results only in summer months, July and August, and tended to underestimate the observations in the fall/winter seasons from November to January at the flux sites studied. In general, the proposed methodology clearly showed the added value of an intercomparison of different upscaling methods under scenarios that account for the uncertainty in eddy covariance flux measurements due to closure errors.

scholarly journals Upscaling of evapotranspiration fluxes from instantaneous to daytime scales for thermal remote sensing applications

2013 ◽  
Vol 10 (6) ◽  
pp. 7325-7350 ◽  
Author(s):  
C. Cammalleri ◽  
M. C. Anderson ◽  
W. P. Kustas
Keyword(s):  
Remote Sensing ◽  
Solar Radiation ◽  
Eddy Covariance ◽  
Time Of Day ◽  
Surface Solar Radiation ◽  
Evaluation Approach ◽  
Sensing Applications ◽  
Wide Range ◽  
Systematic Biases ◽  
Sky Conditions

Abstract. Four upscaling methods for estimating daytime evapotranspiration (ET) from single time-of-day snapshots, as commonly retrieved using remote sensing, were compared. These methods are based on the assumption of self-preservation of the ratio between ET and a given reference variable over the daytime hours. The analysis was performed using eddy covariance data collected at 12 AmeriFlux towers, sampling a fairly wide range in climatic and land cover conditions. The choice of energy budget closure method significantly impacted performance using different scaling methodologies. Therefore, a statistical evaluation approach was adopted to better account for the inherent uncertainty in ET fluxes using eddy covariance technique. Overall, this approach suggests that at-surface solar radiation is the most robust reference variable amongst those tested, due to high accuracy of upscaled fluxes and absence of systematic biases. Top-of-atmosphere irradiance was also tested and proved to be reliable under near clear-sky conditions, but tended to overestimate the observed daytime ET during cloudy days. Use of reference ET as a scaling flux did not perform as well as the solar radiation method, but similarly had errors with little seasonal dependency. Finally, the commonly-used evaporative fraction method yielded satisfactory results only in summer months, July and August, and tended to underestimate the observations in the fall/winter seasons from November to January at the flux sites studied.

scholarly journals Impact of the Revisit of Thermal Infrared Remote Sensing Observations on Evapotranspiration Uncertainty—A Sensitivity Study Using AmeriFlux Data

2019 ◽  
Vol 11 (5) ◽  
pp. 573 ◽  
Author(s):  
Pierre Guillevic ◽  
Albert Olioso ◽  
Simon Hook ◽  
Joshua Fisher ◽  
Jean-Pierre Lagouarde ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Solar Radiation ◽  
Thermal Infrared ◽  
Sensitivity Study ◽  
Time Of Day ◽  
Surface Solar Radiation ◽  
Thermal Infrared Remote Sensing ◽  
Temporal Sampling ◽  
Infrared Remote Sensing ◽  
The Impact

Thermal infrared remote sensing observations have been widely used to provide useful information on surface energy and water stress for estimating evapotranspiration (ET). However, the revisit time of current high spatial resolution (<100 m) thermal infrared remote sensing systems, sixteen days for Landsat for example, can be insufficient to reliably derive ET information for water resources management. We used in situ ET measurements from multiple Ameriflux sites to (1) evaluate different scaling methods that are commonly used to derive daytime ET estimates from time-of-day observations; and (2) quantify the impact of different revisit times on ET estimates at monthly and seasonal time scales. The scaling method based on a constant evaporative ratio between ET and the top-of-atmosphere solar radiation provided slightly better results than methods using the available energy, the surface solar radiation or the potential ET as scaling reference fluxes. On average, revisit time periods of 2, 4, 8 and 16 days resulted in ET uncertainties of 0.37, 0.55, 0.73 and 0.90 mm per day in summer, which represented 13%, 19%, 23% and 31% of the monthly average ET calculated using the one-day revisit dataset. The capability of a system to capture rapid changes in ET was significantly reduced for return periods higher than eight days. The impact of the revisit on ET depended mainly on the land cover type and seasonal climate, and was higher over areas with high ET. We did not observe significant and systematic differences between the impacts of the revisit on monthly ET estimates that are based on morning or afternoon observations. We found that four-day revisit scenarios provided a significant improvement in temporal sampling to monitor surface ET reducing by around 40% the uncertainty of ET products derived from a 16-day revisit system, such as Landsat for instance.

scholarly journals Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges

2019 ◽  
Vol 11 (3) ◽  
pp. 230 ◽  
Author(s):  
Tien Pham ◽  
Naoto Yokoya ◽  
Dieu Bui ◽  
Kunihiko Yoshino ◽  
Daniel Friess
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Mangrove Ecosystem ◽  
Carbon Stocks ◽  
Machine Learning Techniques ◽  
Species Structure ◽  
Mangrove Species ◽  
Mitigation And Adaptation ◽  
Sensing Applications ◽  
Wide Range

The mangrove ecosystem plays a vital role in the global carbon cycle, by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, mangroves have been lost worldwide, resulting in substantial carbon stock losses. Additionally, some aspects of the mangrove ecosystem remain poorly characterized compared to other forest ecosystems due to practical difficulties in measuring and monitoring mangrove biomass and their carbon stocks. Without a quantitative method for effectively monitoring biophysical parameters and carbon stocks in mangroves, robust policies and actions for sustainably conserving mangroves in the context of climate change mitigation and adaptation are more difficult. In this context, remote sensing provides an important tool for monitoring mangroves and identifying attributes such as species, biomass, and carbon stocks. A wide range of studies is based on optical imagery (aerial photography, multispectral, and hyperspectral) and synthetic aperture radar (SAR) data. Remote sensing approaches have been proven effective for mapping mangrove species, estimating their biomass, and assessing changes in their extent. This review provides an overview of the techniques that are currently being used to map various attributes of mangroves, summarizes the studies that have been undertaken since 2010 on a variety of remote sensing applications for monitoring mangroves, and addresses the limitations of these studies. We see several key future directions for the potential use of remote sensing techniques combined with machine learning techniques for mapping mangrove areas and species, and evaluating their biomass and carbon stocks.

scholarly journals Frequency-Agile FFT Spectrometer for Microwave Remote Sensing Applications

Atmosphere ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 490
Author(s):  
Jonas Hagen ◽  
Andres Luder ◽  
Axel Murk ◽  
Niklaus Kämpfer
Keyword(s):  
Remote Sensing ◽  
Software Defined Radio ◽  
Low Cost ◽  
Microwave Remote Sensing ◽  
Local Oscillator ◽  
High Spectral Resolution ◽  
Large Bandwidth ◽  
Sensing Applications ◽  
Atmospheric Remote Sensing ◽  
Systematic Biases

We report on a Fast Fourier Transform Spectrometer (FFTS) that provides larger bandwidth by fast local oscillator switching of the base-band converter. We demonstrate that this frequency scanning technique is suited for atmospheric remote sensing and conduct measurements of atmospheric ozone using the WIRA-C (WInd RAdiometer for Campaigns) Doppler wind radiometer. The comparison of our measurements to an adjusted atmospheric and instrumental model exposes no systematic biases due to the switching procedure in the measured spectra. It further shows that the combination of high spectral resolution with large bandwidth yields good measurement response to stratospheric and mesospheric ozone from approximately a 20 km to 70 km altitude with a resolution of 7 km in the lower stratosphere to 20 km in the mesosphere. We conclude that low-cost, low-power software-defined radio hardware designed for communications applications is very well suited for a variety of spectroscopic applications, including ozone monitoring. This allows the design of low-cost, multi-purpose instruments for atmospheric remote sensing and thus has a direct impact on future radiometer developments and their adoption in remote sensing campaigns and networks.

Broad-band Viscoelastic Rotational Vibration Control for Remote Sensing Applications

2005 ◽  
Vol 11 (11) ◽  
pp. 1339-1356 ◽  
Author(s):  
Adam L. Webster ◽  
William H. Semke
Keyword(s):  
Remote Sensing ◽  
Vibration Isolation ◽  
Imaging System ◽  
Broad Band ◽  
Space Station ◽  
Single Element ◽  
Sensing Applications ◽  
Wide Range ◽  
Rotational Vibration ◽  
Remote Sensing Applications

The ability to eliminate, or effectively control, vibration in remote sensing applications is critical. Any perturbations of an imaging system are greatly magnified over the hundreds of kilometers from the orbiting space platform to the Earth's surface. Space platforms, such as the International Space Station, are not as predictable or stable as many other spacecraft. Therefore, an effective vibration isolation and/or absorber system is needed that operates over a wide range of excitation frequencies. A passive system is also preferred to reduce the resources required, as well as to provide a reliable and self-contained system. To accomplish these goals, a vibration amplitude limiting system has been developed that uses both vibration isolation and absorber components. Viscoelastic structural elements that act as both a spring and a damper in a single element are implemented in the design. This configuration also demonstrates a favorable frequencydependent response and produces a system with improved dynamic behavior compared to conventional spring and damper designs. This rotation limiting vibration system has been designed and analyzed for use in digital remote sensing imaging. The transmissibility and the ground jitter associated with the system are determined. A summary of these results will be presented along with a comparison to a more conventional vibration isolation/absorber system.

scholarly journals Remote Sensing by using Unsupervised Algorithm

2021 ◽  
Author(s):  
Afshan Saleem
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Unsupervised Learning ◽  
Hyperspectral Image ◽  
Relevant Information ◽  
Spectral Signature ◽  
Sensing Applications ◽  
Hyper Spectral ◽  
Wide Range ◽  
Remote Sensing Applications

Hyper-spectral images contain a wide range of bands or wavelength due to which they are rich in information. These images are taken by specialized sensors and then investigated through various supervised or unsupervised learning algorithms. Data that is acquired by hyperspectral image contain plenty of information hence it can be used in applications where materials can be analyzed keenly, even the smallest difference can be detected on the basis of spectral signature i.e. remote sensing applications. In order to retrieve information about the concerned area, the image has to be grouped in different segments and can be analyzed conveniently. In this way, only concerned portions of the image can be studied that have relevant information and the rest that do not have any information can be discarded. Image segmentation can be done to assort all pixels in groups. Many methods can be used for this purpose but in this paper, we discussed k means clustering to assort data in AVIRIS cuprite, AVIRIS Muffet and Rosis Pavia in order to calculate the number of regions in each image and retrieved information of 1st, 10th and100th band. Clustering has been done easily and efficiently as k means algorithm is the easiest approach to retrieve information.

scholarly journals Application of Remote Sensing Methods to Monitor Coastal Zones

2020 ◽  
Vol 8 (6) ◽  
pp. 391 ◽  
Author(s):  
Luis Pedro Almeida ◽  
Rafael Almar
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Original Research ◽  
Coastal Zones ◽  
Special Issue ◽  
Research Papers ◽  
Sensing Applications ◽  
Wide Range ◽  
Remote Sensing Applications ◽  
Remote Sensing Methods

In this Special Issue “Application of Remote Sensing Methods to Monitor Coastal Zones” nine original research papers were published, with topics covering a wide range of ranging of remote sensing applications including coastal topography, bathymetry, land cover, and nearshore hydrodynamics [...]

scholarly journals Hybrid Inversion Algorithms for Retrieval of Absorption Subcomponents from Ocean Colour Remote Sensing Reflectance

2021 ◽  
Vol 13 (9) ◽  
pp. 1726
Author(s):  
Srinivas Kolluru ◽  
Surya Prakash Tiwari ◽  
Shirishkumar S. Gedam
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Pure Water ◽  
Decomposition Algorithms ◽  
Sensing Applications ◽  
Remote Sensing Reflectance ◽  
Wide Range ◽  
Water Constituents ◽  
Ocean Colour Remote Sensing ◽  
Constraints Model

Semi-analytical algorithms (SAAs) invert spectral remote sensing reflectance (Rrs(λ), sr−1) to Inherent Optical Properties (IOPs) of an aquatic medium (λ is the wavelength). Existing SAAs implement different methodologies with a range of spectral IOP models and inversion methods producing concentrations of non-water constituents. Absorption spectrum decomposition algorithms (ADAs) are a set of algorithms developed to partition anw(λ), m−1 (i.e., the light absorption coefficient without pure water absorption), into absorption subcomponents of phytoplankton (aph(λ), m−1) and coloured detrital matter (adg(λ), m−1). Despite significant developments in ADAs, their applicability to remote sensing applications is rarely studied. The present study formulates hybrid inversion approaches that combine SAAs and ADAs to derive absorption subcomponents from Rrs(λ) and explores potential alternatives to operational SAAs. Using Rrs(λ) and concurrent absorption subcomponents from four datasets covering a wide range of optical properties, three operational SAAs, i.e., Garver–Siegel–Maritorena (GSM), Quasi-Analytical Algorithm (QAA), Generalized Inherent Optical Property (GIOP) model are evaluated in deriving anw(λ) from Rrs(λ). Among these three models, QAA and GIOP models derived anw(λ) with lower errors. Among six distinctive ADAs tested in the study, the Generalized Stacked Constraints Model (GSCM) and Zhang’s model-derived absorption subcomponents achieved lower average spectral mean absolute percentage errors (MAPE) in the range of 8–38%. Four hybrid models, GIOPGSCM, GIOPZhang, QAAGSCM and QAAZhang, formulated using the SAAs and ADAs, are compared for their absorption subcomponent retrieval performance from Rrs(λ). GIOPGSCM and GIOPZhang models derived absorption subcomponents have lower errors than GIOP and QAA. Potential uncertainties associated with datasets and dependency of algorithm performance on datasets were discussed.

scholarly journals Review of Surface Solar Radiation Projections in Bias-Corrected Euro-Cordex Regional Climate Models

2018 ◽  
Vol 60 (1) ◽  
pp. 3-13
Author(s):  
Blanka Bartók
Keyword(s):  
Solar Radiation ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Scale ◽  
Regional Climate Models ◽  
Surface Radiation ◽  
Climate Feedback ◽  
Surface Solar Radiation ◽  
Wide Range ◽  
Seasonal Time Scale

AbstractRegional climate models (RCMs) are used in a wide range of climate applications as they can provide high resolution (up to 10 to 20 km or less) and multi-decadal simulations of the climate system describing climate feedback mechanisms acting at the regional scale. However due to different forcing data and physics parametrisations regional climate models might produce different results. This study aims to achieve a state-of-the-art knowledge of bias-corrected surface solar radiation projections coming from 11 EURO-CORDEX regional climate models. First a comparison against 63 GEBA observations is elaborated indicating a general overestimation of surface solar radiation (SSR) in the RCMs by 6.12 W/m2 (4.4%). Next changes in surface radiation between the period of 2031-2060 and 1971-2000 are presented on annual and seasonal time scale. The model projections indicate robust increase in SSR mainly in the western part of the Mediterranean region, while the northern part of the continent is characterised by decreases in SSR till the middle of this century. The study emphasis the need of an overall validation of different climate models before introducing them in impact studies in order to have an overview regarding the uncertainties.

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