Volume and uncertainty estimates of on-farm reservoirs using surface reflectance and LiDAR data

Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land

Atmospheric Measurement Techniques ◽

10.5194/amt-11-1529-2018 ◽

2018 ◽

Vol 11 (3) ◽

pp. 1529-1547 ◽

Cited By ~ 7

Author(s):

Antti Lipponen ◽

Tero Mielonen ◽

Mikko R. A. Pitkänen ◽

Robert C. Levy ◽

Virginia R. Sawyer ◽

...

Keyword(s):

Computation Time ◽

Retrieval Algorithm ◽

Surface Reflectance ◽

Aerosol Retrieval ◽

Correlation Models ◽

Modis Aod ◽

Uncertainty Estimates ◽

Average Computation Time ◽

Moderate Resolution Imaging Spectroradiometer ◽

Aerosol Parameters

Abstract. We have developed a Bayesian aerosol retrieval (BAR) algorithm for the retrieval of aerosol optical depth (AOD) over land from the Moderate Resolution Imaging Spectroradiometer (MODIS). In the BAR algorithm, we simultaneously retrieve all dark land pixels in a granule, utilize spatial correlation models for the unknown aerosol parameters, use a statistical prior model for the surface reflectance, and take into account the uncertainties due to fixed aerosol models. The retrieved parameters are total AOD at 0.55 µm, fine-mode fraction (FMF), and surface reflectances at four different wavelengths (0.47, 0.55, 0.64, and 2.1 µm). The accuracy of the new algorithm is evaluated by comparing the AOD retrievals to Aerosol Robotic Network (AERONET) AOD. The results show that the BAR significantly improves the accuracy of AOD retrievals over the operational Dark Target (DT) algorithm. A reduction of about 29 % in the AOD root mean square error and decrease of about 80 % in the median bias of AOD were found globally when the BAR was used instead of the DT algorithm. Furthermore, the fraction of AOD retrievals inside the ±(0.05+15%) expected error envelope increased from 55 to 76 %. In addition to retrieving the values of AOD, FMF, and surface reflectance, the BAR also gives pixel-level posterior uncertainty estimates for the retrieved parameters. The BAR algorithm always results in physical, non-negative AOD values, and the average computation time for a single granule was less than a minute on a modern personal computer.

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Bayesian Dark Target Algorithm for MODIS AOD retrieval over land

10.5194/amt-2017-359 ◽

2017 ◽

Cited By ~ 1

Author(s):

Antti Lipponen ◽

Tero Mielonen ◽

Mikko R. A. Pitkänen ◽

Robert C. Levy ◽

Virginia R. Sawyer ◽

...

Keyword(s):

Computation Time ◽

Surface Reflectance ◽

Correlation Models ◽

Modis Aod ◽

Uncertainty Estimates ◽

Average Computation Time ◽

Moderate Resolution ◽

Fine Mode ◽

Moderate Resolution Imaging Spectroradiometer ◽

Aerosol Parameters

Abstract. We have developed a Bayesian Dark Target (BDT) algorithm for the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrieval over land. In the BDT algorithm, we simultaneously retrieve all pixels in a granule, utilize spatial correlation models for the unknown aerosol parameters, use a statistical prior model for the surface reflectance, and take into account the uncertainties due to fixed aerosol models. The retrieved parameters are total AOD at 550 nm, fine-mode fraction (FMF), and surface reflectances at four different wavelengths (466, 550, 644, and 2100 nm). The accuracy of the new algorithm is evaluated by comparing the AOD retrievals to Aerosol Robotic Network (AERONET) AOD. The results show that the BDT significantly improves the accuracy of AOD retrievals over the operational Dark Target (DT) algorithm. A reduction of about 29 % in the AOD root mean square error and decrease of about 80 % in the median bias of AOD were found globally when the BDT was used instead of the DT algorithm. Furthermore, the fraction of AOD retrievals inside the ±(0.05 + 15 %) expected error envelope increased from 55 % to 76 %. In addition to retrieving the values of AOD, FMF and surface reflectance, the BDT also gives pixel-level posterior uncertainty estimates for the retrieved parameters. The BDT algorithm always results in physical, non-negative AOD values, and the average computation time for a single granule was less than a minute on a modern personal computer.

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Uncertainty Estimates and the Interpretation of Climate Change Forecasts

PsycEXTRA Dataset ◽

10.1037/e502412013-601 ◽

2012 ◽

Author(s):

Jared E. Leclerc ◽

Susan Joslyn

Keyword(s):

Climate Change ◽

Uncertainty Estimates

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On-farm udder health monitoring

Tierärztliche Praxis Ausgabe G Großtiere / Nutztiere ◽

10.1055/s-0038-1624622 ◽

2011 ◽

Vol 39 (02) ◽

pp. 95-100

Author(s):

J. C. van Veersen ◽

O. Sampimon ◽

R. G. Olde Riekerink ◽

T. J. G. Lam

Keyword(s):

Management Practices ◽

Health Management ◽

Action Plan ◽

Real Life ◽

Health Data ◽

Control Points ◽

Udder Health ◽

Critical Control Points ◽

On Farm ◽

Clinical Problems

SummaryIn this article an on-farm monitoring approach on udder health is presented. Monitoring of udder health consists of regular collection and analysis of data and of the regular evaluation of management practices. The ultimate goal is to manage critical control points in udder health management, such as hygiene, body condition, teat ends and treatments, in such a way that results (udder health parameters) are always optimal. Mastitis, however, is a multifactorial disease, and in real life it is not possible to fully prevent all mastitis problems. Therefore udder health data are also monitored with the goal to pick up deviations before they lead to (clinical) problems. By quantifying udder health data and management, a farm is approached as a business, with much attention for efficiency, thought over processes, clear agreements and goals, and including evaluation of processes and results. The whole approach starts with setting SMART (Specific, Measurable, Acceptable, Realistic, Time-bound) goals, followed by an action plan to realize these goals.

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2012–2016 On-Farm Evaluation of Fungicide Programs for Peanut Disease Control in Hamilton County, Florida

EDIS ◽

10.32473/edis-pp334-2017 ◽

2017 ◽

Vol 2017 (4) ◽

Author(s):

Keith W. Wynn ◽

Nicholas S. Dufault ◽

Rebecca L. Barocco

Keyword(s):

Plant Pathology ◽

Disease Control ◽

Leaf Spot ◽

Fungal Disease ◽

White Mold ◽

Stem Rot ◽

Late Leaf Spot ◽

Fact Sheet ◽

On Farm ◽

Hamilton County

This ten-page fact sheet includes a summary of various fungicide spray programs for fungal disease control of early leaf spot, late leaf spot, and white mold/stem rot of peanut in 2012-2016 on-farm trials in Hamilton County. Written by K.W. Wynn, N.S. Dufault, and R.L. Barocco and published by the Plant Pathology Department.http://edis.ifas.ufl.edu/pp334

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Florida Agritourism Building and Fire Codes

EDIS ◽

10.32473/edis-wc349-2020 ◽

2020 ◽

Vol 2020 (1) ◽

Author(s):

Mary Beth Henry ◽

Kathryn A Stofer

Keyword(s):

Agricultural Education ◽

Local Governments ◽

Regulatory Authority ◽

Building Codes ◽

Bona Fide ◽

Agricultural Operations ◽

On Farm ◽

Fire Codes

Agritourism marries Florida’s two largest industries, tourism and agriculture, to provide an on-farm recreational experience for consumers. Although Florida trails many other states in the number of agritourism operations, the number of Florida farms offering recreational experiences more than doubled from 2007 to 2012. This new 4-page document describes building codes relevant to Florida agritourism operations. Written by Mary Beth Henry and Kathryn A. Stofer, and published by the UF/IFAS Department of Agricultural Education and Communication.https://edis.ifas.ufl.edu/wc349 A companion document, Florida’s Agritourism Laws, EDIS publication AEC623, Florida’s Agritourism Laws, http://edis.ifas.ufl.edu/wc285, discusses Florida Statutes related to definitions, liability protections, and limits to regulatory authority of local governments over bona fide agricultural operations engaged in agritourism.

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