This page has only limited features, please log in for full access.

Unclaimed
Aaron A. Berg
Department of Geography, Environment and Geomatics, University of Guelph, Guelph, ON N1G 2W1, Canada

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Canadian geophysical union 2020
Published: 21 June 2021 in Hydrological Processes
Reads 0
Downloads 0

Soil freeze–thaw events have important implications for water resources, flood risk, land productivity, and climate change. A property of these phenomena is the relationship between unfrozen water content and sub-freezing temperature, known as the soil freezing characteristic curve (SFC). It is documented that this relationship exhibits hysteretic behaviour when frozen soil thaws, leading to the definition of the soil thawing characteristic curve (STC). Although explanations have been given for SFC/STC hysteresis, the effect that ‘scale’ – particularly ‘measurement scale’ – may have on these curves has received little attention. The most commonly used measurement scale metric is the ‘support’, which is the spatial (or temporal) unit within which the measured variable is integrated or soil volume sampled. We show (a) measurement support can influence the range and shape of the SFC and (b) hysteresis can be attributed, in part, to the support and location of the measurements comprising the SFC/STC. We simulated lab measured temperature, volumetric water content (VWC), and permittivity from soil samples undergoing freeze–thaw transitions using Hydrus-1D and a modified Dobson permittivity model. To assess the effect of measurement support and location on SFC/STC, we masked the simulated temperature and VWC/permittivity extent to match the instrument's support and location. By creating a detailed simulation of the intra- and inter-support variability associated with the penetration of a freezing front, we demonstrate how measurement support and location can influence the temperature range over which water freezing events are captured. We show it is possible to simulate hysteresis in homogenous media with purely geometric considerations, suggesting that SFC/STC hysteresis may be more of an apparent phenomenon than mechanistically real. Lastly, we develop an understanding of how the location and support of soil temperature and VWC/permittivity measurements influence the temperature range over which water freezing events are captured.

ACS Style

Renato Pardo Lara; Aaron A. Berg; Jon Warland; Gary Parkin. Implications of measurement metrics on soil freezing curves: A simulation of freeze–thaw hysteresis. Hydrological Processes 2021, 35, e14269 .

AMA Style

Renato Pardo Lara, Aaron A. Berg, Jon Warland, Gary Parkin. Implications of measurement metrics on soil freezing curves: A simulation of freeze–thaw hysteresis. Hydrological Processes. 2021; 35 (7):e14269.

Chicago/Turabian Style

Renato Pardo Lara; Aaron A. Berg; Jon Warland; Gary Parkin. 2021. "Implications of measurement metrics on soil freezing curves: A simulation of freeze–thaw hysteresis." Hydrological Processes 35, no. 7: e14269.

Journal article
Published: 05 June 2021 in Remote Sensing
Reads 0
Downloads 0

Surface roughness is an important factor in many soil moisture retrieval models. Therefore, any mischaracterization of surface roughness parameters (root mean square height, RMSH, and correlation length, ʅ) may result in unreliable predictions and soil moisture estimations. In many environments, but particularly in agricultural settings, surface roughness parameters may show different behaviours with respect to the orientation or azimuth. Consequently, the relationship between SAR polarimetric variables and surface roughness parameters may vary depending on measurement orientation. Generally, roughness obtained for many SAR-based studies is estimated using pin profilers that may, or may not, be collected with careful attention to orientation to the satellite look angle. In this study, we characterized surface roughness parameters in multi-azimuth mode using a terrestrial laser scanner (TLS). We characterized the surface roughness parameters in different orientations and then examined the sensitivity between polarimetric variables and surface roughness parameters; further, we compared these results to roughness profiles obtained using traditional pin profilers. The results showed that the polarimetric variables were more sensitive to the surface roughness parameters at higher incidence angles (θ). Moreover, when surface roughness measurements were conducted at the look angle of RADARSAT-2, more significant correlations were observed between polarimetric variables and surface roughness parameters. Our results also indicated that TLS can represent more reliable results than pin profiler in the measurement of the surface roughness parameters.

ACS Style

Zohreh Alijani; John Lindsay; Melanie Chabot; Tracy Rowlandson; Aaron Berg. Sensitivity of C-Band SAR Polarimetric Variables to the Directionality of Surface Roughness Parameters. Remote Sensing 2021, 13, 2210 .

AMA Style

Zohreh Alijani, John Lindsay, Melanie Chabot, Tracy Rowlandson, Aaron Berg. Sensitivity of C-Band SAR Polarimetric Variables to the Directionality of Surface Roughness Parameters. Remote Sensing. 2021; 13 (11):2210.

Chicago/Turabian Style

Zohreh Alijani; John Lindsay; Melanie Chabot; Tracy Rowlandson; Aaron Berg. 2021. "Sensitivity of C-Band SAR Polarimetric Variables to the Directionality of Surface Roughness Parameters." Remote Sensing 13, no. 11: 2210.

Preprint content
Published: 31 May 2021
Reads 0
Downloads 0

The prevalent soil moisture probe algorithms are based on a polynomial function that does not account for the variability in soil organic matter. Users are expected to choose a model before application: either a model for mineral soil or a model for organic soil. Both approaches inevitably suffer from limitations with respect to estimating the volumetric soil water content in soils having a wide range of organic matter content. In this study, we propose a new algorithm based on the idea that the amount of soil organic matter (SOM) is related to major uncertainties in the in-situ soil moisture data obtained using soil probe instruments. To test this theory, we derived a multiphase inversion algorithm from a physically based dielectric mixing model capable of using the SOM amount, performed a selection process from the multiphase model outcomes, and tested whether this new approach improves the accuracy of soil moisture (SM) data probes. The validation of the proposed new soil probe algorithm was performed using both gravimetric and dielectric data from the Soil Moisture Active Passive Validation Experiment in 2012 (SMAPVEX12). The new algorithm is more accurate than the previous soil-probe algorithm, resulting in a slightly improved correlation (0.824 0.848), 12 % lower root mean square error (RMSE; 0.0824 0.0725 cm3·cm−3), and 90 % less bias (−0.0042 0.0004 cm3·cm−3). These results suggest that applying the new dielectric mixing model together with global SOM estimates will result in more reliable soil moisture reference data for weather and climate models and satellite validation.

ACS Style

Chang-Hwan Park; Aaron Berg; Michael H. Cosh; Andreas Colliander; Andreas Behrendt; Hida Manns; Jinkyu Hong; Johan Lee; Volker Wulfmeyer. An inverse dielectric mixing model at 50 MHz that considers soil organic carbon. 2021, 2021, 1 -21.

AMA Style

Chang-Hwan Park, Aaron Berg, Michael H. Cosh, Andreas Colliander, Andreas Behrendt, Hida Manns, Jinkyu Hong, Johan Lee, Volker Wulfmeyer. An inverse dielectric mixing model at 50 MHz that considers soil organic carbon. . 2021; 2021 ():1-21.

Chicago/Turabian Style

Chang-Hwan Park; Aaron Berg; Michael H. Cosh; Andreas Colliander; Andreas Behrendt; Hida Manns; Jinkyu Hong; Johan Lee; Volker Wulfmeyer. 2021. "An inverse dielectric mixing model at 50 MHz that considers soil organic carbon." 2021, no. : 1-21.

Research article
Published: 09 April 2021 in Hydrology and Earth System Sciences
Reads 0
Downloads 0

The interior of western Canada, like many similar cold mid- to high-latitude regions worldwide, is undergoing extensive and rapid climate and environmental change, which may accelerate in the coming decades. Understanding and predicting changes in coupled climate–land–hydrological systems are crucial to society yet limited by lack of understanding of changes in cold-region process responses and interactions, along with their representation in most current-generation land-surface and hydrological models. It is essential to consider the underlying processes and base predictive models on the proper physics, especially under conditions of non-stationarity where the past is no longer a reliable guide to the future and system trajectories can be unexpected. These challenges were forefront in the recently completed Changing Cold Regions Network (CCRN), which assembled and focused a wide range of multi-disciplinary expertise to improve the understanding, diagnosis, and prediction of change over the cold interior of western Canada. CCRN advanced knowledge of fundamental cold-region ecological and hydrological processes through observation and experimentation across a network of highly instrumented research basins and other sites. Significant efforts were made to improve the functionality and process representation, based on this improved understanding, within the fine-scale Cold Regions Hydrological Modelling (CRHM) platform and the large-scale Modélisation Environmentale Communautaire (MEC) – Surface and Hydrology (MESH) model. These models were, and continue to be, applied under past and projected future climates and under current and expected future land and vegetation cover configurations to diagnose historical change and predict possible future hydrological responses. This second of two articles synthesizes the nature and understanding of cold-region processes and Earth system responses to future climate, as advanced by CCRN. These include changing precipitation and moisture feedbacks to the atmosphere; altered snow regimes, changing balance of snowfall and rainfall, and glacier loss; vegetation responses to climate and the loss of ecosystem resilience to wildfire and disturbance; thawing permafrost and its influence on landscapes and hydrology; groundwater storage and cycling and its connections to surface water; and stream and river discharge as influenced by the various drivers of hydrological change. Collective insights, expert elicitation, and model application are used to provide a synthesis of this change over the CCRN region for the late 21st century.

ACS Style

Chris M. DeBeer; Howard S. Wheater; John W. Pomeroy; Alan G. Barr; Jennifer L. Baltzer; Jill F. Johnstone; Merritt R. Turetsky; Ronald E. Stewart; Masaki Hayashi; Garth van der Kamp; Shawn Marshall; Elizabeth Campbell; Philip Marsh; Sean K. Carey; William L. Quinton; Yanping Li; Saman Razavi; Aaron Berg; Jeffrey J. McDonnell; Christopher Spence; Warren D. Helgason; Andrew M. Ireson; T. Andrew Black; Mohamed Elshamy; Fuad Yassin; Bruce Davison; Allan Howard; Julie M. Thériault; Kevin Shook; Michael N. Demuth; Alain Pietroniro. Summary and synthesis of Changing Cold Regions Network (CCRN) research in the interior of western Canada – Part 2: Future change in cryosphere, vegetation, and hydrology. Hydrology and Earth System Sciences 2021, 25, 1849 -1882.

AMA Style

Chris M. DeBeer, Howard S. Wheater, John W. Pomeroy, Alan G. Barr, Jennifer L. Baltzer, Jill F. Johnstone, Merritt R. Turetsky, Ronald E. Stewart, Masaki Hayashi, Garth van der Kamp, Shawn Marshall, Elizabeth Campbell, Philip Marsh, Sean K. Carey, William L. Quinton, Yanping Li, Saman Razavi, Aaron Berg, Jeffrey J. McDonnell, Christopher Spence, Warren D. Helgason, Andrew M. Ireson, T. Andrew Black, Mohamed Elshamy, Fuad Yassin, Bruce Davison, Allan Howard, Julie M. Thériault, Kevin Shook, Michael N. Demuth, Alain Pietroniro. Summary and synthesis of Changing Cold Regions Network (CCRN) research in the interior of western Canada – Part 2: Future change in cryosphere, vegetation, and hydrology. Hydrology and Earth System Sciences. 2021; 25 (4):1849-1882.

Chicago/Turabian Style

Chris M. DeBeer; Howard S. Wheater; John W. Pomeroy; Alan G. Barr; Jennifer L. Baltzer; Jill F. Johnstone; Merritt R. Turetsky; Ronald E. Stewart; Masaki Hayashi; Garth van der Kamp; Shawn Marshall; Elizabeth Campbell; Philip Marsh; Sean K. Carey; William L. Quinton; Yanping Li; Saman Razavi; Aaron Berg; Jeffrey J. McDonnell; Christopher Spence; Warren D. Helgason; Andrew M. Ireson; T. Andrew Black; Mohamed Elshamy; Fuad Yassin; Bruce Davison; Allan Howard; Julie M. Thériault; Kevin Shook; Michael N. Demuth; Alain Pietroniro. 2021. "Summary and synthesis of Changing Cold Regions Network (CCRN) research in the interior of western Canada – Part 2: Future change in cryosphere, vegetation, and hydrology." Hydrology and Earth System Sciences 25, no. 4: 1849-1882.

Research article
Published: 04 March 2021 in Hydrology and Earth System Sciences
Reads 0
Downloads 0

Soil microwave permittivity is a crucial parameter in passive microwave retrieval algorithms but remains a challenging variable to measure. To validate and improve satellite microwave data products, precise and reliable estimations of the relative permittivity (εr=ε/ε0=ε′-jε′′; unitless) of soils are required, particularly for frozen soils. In this study, permittivity measurements were acquired using two different instruments: the newly designed open-ended coaxial probe (OECP) and the conventional Stevens HydraProbe. Both instruments were used to characterize the permittivity of soil samples undergoing several freeze–thaw cycles in a laboratory environment. The measurements were compared to soil permittivity models. The OECP measured frozen (εfrozen′=[3.5; 6.0], εfrozen′′=[0.46; 1.2]) and thawed (εthawed′=[6.5; 22.8], εthawed′′=[1.43; 5.7]) soil microwave permittivity. We also demonstrate that cheaper and widespread soil permittivity probes operating at lower frequencies (i.e., Stevens HydraProbe) can be used to estimate microwave permittivity given proper calibration relative to an L-band (1–2 GHz) probe. This study also highlighted the need to improve dielectric soil models, particularly during freeze–thaw transitions. There are still important discrepancies between in situ and modeled estimates and no current model accounts for the hysteresis effect shown between freezing and thawing processes, which could have a significant impact on freeze–thaw detection from satellites.

ACS Style

Alex Mavrovic; Renato Pardo Lara; Aaron Berg; François Demontoux; Alain Royer; Alexandre Roy. Soil dielectric characterization during freeze–thaw transitions using L-band coaxial and soil moisture probes. Hydrology and Earth System Sciences 2021, 25, 1117 -1131.

AMA Style

Alex Mavrovic, Renato Pardo Lara, Aaron Berg, François Demontoux, Alain Royer, Alexandre Roy. Soil dielectric characterization during freeze–thaw transitions using L-band coaxial and soil moisture probes. Hydrology and Earth System Sciences. 2021; 25 (3):1117-1131.

Chicago/Turabian Style

Alex Mavrovic; Renato Pardo Lara; Aaron Berg; François Demontoux; Alain Royer; Alexandre Roy. 2021. "Soil dielectric characterization during freeze–thaw transitions using L-band coaxial and soil moisture probes." Hydrology and Earth System Sciences 25, no. 3: 1117-1131.

Journal article
Published: 21 January 2021 in Remote Sensing of Environment
Reads 0
Downloads 0

Grasslands are valuable carbon sinks in the effort to mitigate climate change. However, they are not well protected and are consequently being replaced by agricultural systems worldwide. Current monitoring efforts using remote sensing and ground-based methods are insufficient, and accordingly the mapping of grassland to cropland conversions must be improved to better document these changes in the Canadian Prairies. The purpose of this study is to evaluate different structural break methods and remote sensing datasets for their temporal accuracy in detecting grassland conversions in two Alberta study areas from 2010 to 2018. Breaks For Additive Seasonal and Trend (BFAST), BFAST Seasonal and Bayesian Estimator of Abrupt change, Seasonality and Trend (BEAST) methods were applied to evaluate their sensitivity to rangeland and pasture conversions. The best model was BFAST Seasonal, correctly predicting the year of change for 76% of rangelands and 66% of pastures. This demonstrates that seasonal models are effective in detecting interannual changes in vegetation composition amidst background noise from climate and management induced phenological changes. MODIS data outperformed Landsat, outlining the importance of high temporal resolution remote sensing data to successful change detection, even at the expense of higher spatial resolution. Overall, this study demonstrates that structural break methods are effective in identifying grassland to agriculture transitions and may be useful for the operational monitoring of grassland inventories in the future.

ACS Style

Jacob Mardian; Aaron Berg; Bahram Daneshfar. Evaluating the temporal accuracy of grassland to cropland change detection using multitemporal image analysis. Remote Sensing of Environment 2021, 255, 112292 .

AMA Style

Jacob Mardian, Aaron Berg, Bahram Daneshfar. Evaluating the temporal accuracy of grassland to cropland change detection using multitemporal image analysis. Remote Sensing of Environment. 2021; 255 ():112292.

Chicago/Turabian Style

Jacob Mardian; Aaron Berg; Bahram Daneshfar. 2021. "Evaluating the temporal accuracy of grassland to cropland change detection using multitemporal image analysis." Remote Sensing of Environment 255, no. : 112292.

Journal article
Published: 26 October 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Reads 0
Downloads 0

In order to validate its soil moisture products, the NASA Soil Moisture Active Passive (SMAP) mission utilizes sites with permanent networks of in situ soil moisture sensors maintained by independent calibration and validation partners in a variety of ecosystems around the world. Measurements from each core validation site (CVS) are combined in a weighted average to produce an estimate of soil moisture at a 33-km scale that represents the SMAP's radiometerbased retrievals. Since upscaled estimates produced in this manner are dependent on the weighting scheme applied, an independent method of quantifying their biases is needed. Here we present one such method that uses soil moisture measurements taken from a dense, but temporary, network of soil moisture sensors deployed at each CVS to train a Random Forests regression expressing soil moisture in terms of a set of spatial variables. The regression then serves as an independent source of upscaled estimates against which permanent network upscaled estimates can be compared in order to calculate bias statistics. This method, which offers a systematic and unified approach to estimate bias across a variety of validation sites, was applied to estimate biases at four CVSs. The results showed that the magnitude of the uncertainty in the permanent network upscaling bias can sometimes exceed 80% of the upper limit on SMAP's entire allowable unbiased root-mean-square error (ubRMSE). Such large CVS bias uncertainties could make it more difficult to assess biases in soil moisture estimates from SMAP.

ACS Style

Jane Whitcomb; Daniel Clewley; Andreas Colliander; Michael H. Cosh; Jarrett Powers; Matthew Friesen; Heather McNairn; Aaron A. Berg; David D. Bosch; Alisa Coffin; Chandra Holifield Collins; John H. Prueger; Dara Entekhabi; Mahta Moghaddam. Evaluation of SMAP Core Validation Site Representativeness Errors Using Dense Networks of In Situ Sensors and Random Forests. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 6457 -6472.

AMA Style

Jane Whitcomb, Daniel Clewley, Andreas Colliander, Michael H. Cosh, Jarrett Powers, Matthew Friesen, Heather McNairn, Aaron A. Berg, David D. Bosch, Alisa Coffin, Chandra Holifield Collins, John H. Prueger, Dara Entekhabi, Mahta Moghaddam. Evaluation of SMAP Core Validation Site Representativeness Errors Using Dense Networks of In Situ Sensors and Random Forests. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):6457-6472.

Chicago/Turabian Style

Jane Whitcomb; Daniel Clewley; Andreas Colliander; Michael H. Cosh; Jarrett Powers; Matthew Friesen; Heather McNairn; Aaron A. Berg; David D. Bosch; Alisa Coffin; Chandra Holifield Collins; John H. Prueger; Dara Entekhabi; Mahta Moghaddam. 2020. "Evaluation of SMAP Core Validation Site Representativeness Errors Using Dense Networks of In Situ Sensors and Random Forests." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 6457-6472.

Preprint content
Published: 17 October 2020
Reads 0
Downloads 0

The interior of western Canada, like many similar cold mid- to high-latitude regions worldwide, is undergoing extensive and rapid climate and environmental change, which may accelerate in the coming decades. Understanding and predicting changes in coupled climate–land–hydrological systems are crucial to society, yet limited by lack of understanding of changes in cold region process responses and interactions, along with their representation in most current generation land surface and hydrological models. It is essential to consider the underlying processes and base predictive models on the proper physics, especially under conditions of non-stationarity where the past is no longer a reliable guide to the future and system trajectories can be unexpected. These challenges were forefront in the recently completed Changing Cold Regions Network (CCRN), which assembled and focused a wide range of multi-disciplinary expertise to improve the understanding, diagnosis, and prediction of change over the cold interior of western Canada. CCRN advanced knowledge of fundamental cold region ecological and hydrological processes through observation and experimentation across a network of highly instrumented research basins and other sites. Significant efforts were made to improve the functionality and process representation, based on this improved understanding, within the fine-scale Cold Regions Hydrological Modelling (CRHM) platform and the large-scale Modélisation Environmentale Communautaire (MEC) – Surface and Hydrology (MESH) model. These models were, and continue to be, applied under past and projected future climates, and under current and expected future land and vegetation cover configurations to diagnose historical change and predict possible future hydrological responses. This second of two articles synthesizes the nature and understanding of cold region processes and Earth system responses to future climate, as advanced by CCRN. These include changing precipitation and moisture feedbacks to the atmosphere; altered snow regimes, changing balance of snowfall and rainfall, and glacier loss; vegetation responses to climate and the loss of ecosystem resilience to wildfire and disturbance; thawing permafrost and its influence on landscapes and hydrology; groundwater storage and cycling, and its connections to surface water; and stream and river discharge as influenced by the various drivers of hydrological change. Collective insights, expert elicitation, and model application are used to provide a synthesis of this change over the CCRN region for the late-21st century.

ACS Style

Chris M. DeBeer; Howard S. Wheater; John W. Pomeroy; Alan G. Barr; Jennifer L. Baltzer; Jill F. Johnstone; Merritt R. Turetsky; Ronald E. Stewart; Masaki Hayashi; Garth van der Kamp; Shawn Marshall; Elizabeth Campbell; Philip Marsh; Sean K. Carey; William L. Quinton; Yanping Li; Saman Razavi; Aaron Berg; Jeffrey J. McDonnell; Christopher Spence; Warren D. Helgason; Andrew M. Ireson; T. Andrew Black; Bruce Davison; Allan Howard; Julie M. Thériault; Kevin Shook; Alain Pietroniro. Summary and synthesis of Changing Cold Regions Network (CCRN) research in the interior of western Canada – Part 2: Future change in cryosphere, vegetation, and hydrology. 2020, 1 -48.

AMA Style

Chris M. DeBeer, Howard S. Wheater, John W. Pomeroy, Alan G. Barr, Jennifer L. Baltzer, Jill F. Johnstone, Merritt R. Turetsky, Ronald E. Stewart, Masaki Hayashi, Garth van der Kamp, Shawn Marshall, Elizabeth Campbell, Philip Marsh, Sean K. Carey, William L. Quinton, Yanping Li, Saman Razavi, Aaron Berg, Jeffrey J. McDonnell, Christopher Spence, Warren D. Helgason, Andrew M. Ireson, T. Andrew Black, Bruce Davison, Allan Howard, Julie M. Thériault, Kevin Shook, Alain Pietroniro. Summary and synthesis of Changing Cold Regions Network (CCRN) research in the interior of western Canada – Part 2: Future change in cryosphere, vegetation, and hydrology. . 2020; ():1-48.

Chicago/Turabian Style

Chris M. DeBeer; Howard S. Wheater; John W. Pomeroy; Alan G. Barr; Jennifer L. Baltzer; Jill F. Johnstone; Merritt R. Turetsky; Ronald E. Stewart; Masaki Hayashi; Garth van der Kamp; Shawn Marshall; Elizabeth Campbell; Philip Marsh; Sean K. Carey; William L. Quinton; Yanping Li; Saman Razavi; Aaron Berg; Jeffrey J. McDonnell; Christopher Spence; Warren D. Helgason; Andrew M. Ireson; T. Andrew Black; Bruce Davison; Allan Howard; Julie M. Thériault; Kevin Shook; Alain Pietroniro. 2020. "Summary and synthesis of Changing Cold Regions Network (CCRN) research in the interior of western Canada – Part 2: Future change in cryosphere, vegetation, and hydrology." , no. : 1-48.

Journal article
Published: 16 October 2020 in Remote Sensing
Reads 0
Downloads 0

Soil moisture is a key variable used to describe water and energy exchanges at the land surface/atmosphere interface. Therefore, there is widespread interest in the use of soil moisture retrievals from passive microwave satellites. In the assimilation of satellite soil moisture data into land surface models, two approaches are commonly used. In the first approach brightness temperature (TB) data are assimilated, while in the second approach retrieved soil moisture (SM) data from the satellite are assimilated. However, there is not a significant body of literature comparing the differences between these two approaches, and it is not known whether there is any advantage in using a particular approach over the other. In this study, TB and SM L2 retrieval products from the Soil Moisture and Ocean Salinity (SMOS) satellite are assimilated into the Canadian Land Surface Scheme (CLASS), for improved soil moisture estimation over an agricultural region in Saskatchewan. CLASS is the land surface component of the Canadian Earth System Model (CESM), and the Canadian Seasonal and Interannual Prediction System (CanSIPS). Our results indicated that assimilating the SMOS products improved the soil moisture simulation skill of the CLASS. Near surface soil moisture assimilation also resulted in improved forecasts of root zone soil moisture (RZSM) values. Although both techniques resulted in improved forecasts of RZSM, assimilation of TB resulted in the superior estimates.

ACS Style

Manoj Nambiar; Jaison Ambadan; Tracy Rowlandson; Paul Bartlett; Erica Tetlock; Aaron Berg. Comparing the Assimilation of SMOS Brightness Temperatures and Soil Moisture Products on Hydrological Simulation in the Canadian Land Surface Scheme. Remote Sensing 2020, 12, 3405 .

AMA Style

Manoj Nambiar, Jaison Ambadan, Tracy Rowlandson, Paul Bartlett, Erica Tetlock, Aaron Berg. Comparing the Assimilation of SMOS Brightness Temperatures and Soil Moisture Products on Hydrological Simulation in the Canadian Land Surface Scheme. Remote Sensing. 2020; 12 (20):3405.

Chicago/Turabian Style

Manoj Nambiar; Jaison Ambadan; Tracy Rowlandson; Paul Bartlett; Erica Tetlock; Aaron Berg. 2020. "Comparing the Assimilation of SMOS Brightness Temperatures and Soil Moisture Products on Hydrological Simulation in the Canadian Land Surface Scheme." Remote Sensing 12, no. 20: 3405.

Journal article
Published: 10 September 2020 in Remote Sensing
Reads 0
Downloads 0

An accurate radiative transfer model (RTM) is essential for the retrieval of soil moisture (SM) from microwave remote sensing data, such as the passive microwave measurements from the Soil Moisture Active Passive (SMAP) mission. This mission delivers soil moisture products based upon L-band brightness temperature data, via retrieval algorithms for surface and root-zone soil moisture, the latter is retrieved using data assimilation and model support. We found that the RTM based on the tau-omega (?-ω) model can suffer from significant errors over croplands in the simulation of brightness temperature (Tb) (in average between −9.4K and +12.0K for single channel algorithm (SCA); −8K and +9.7K for dual-channel algorithm (DCA)) if the vegetation scattering albedo (omega) is set constant and temporal variations are not considered. In order to reduce this uncertainty, we propose a time-varying parameterization of omega for the widely established zeroth order radiative transfer ?-ω model. The main assumption is that omega can be expressed by a functional relationship between vegetation optical depth (tau) and the Green Vegetation Fraction (GVF). Assuming allometry in the tau-omega relationship, a power-law function was established and it is supported by correlating measurements of tau and GVF. With this relationship, both tau and omega increase during the development of vegetation. The application of the proposed time-varying vegetation scattering albedo results in a consistent improvement for the unbiased root mean square error of 16% for SCA and 15% for DCA. The reduction for positive and negative biases was 45% and 5% for SCA and 26% and 12% for DCA, respectively. This indicates that vegetation dynamics within croplands are better represented by a time-varying single scattering albedo. Based on these results, we anticipate that the time-varying omega within the tau-omega model will help to mitigate potential estimation errors in the current SMAP soil moisture products (SCA and DCA). Furthermore, the improved tau-omega model might serve as a more accurate observation operator for SMAP data assimilation in weather and climate prediction model.

ACS Style

Chang-Hwan Park; Thomas Jagdhuber; Andreas Colliander; Johan Lee; Aaron Berg; Michael Cosh; Seung-Bum Kim; Yoonjae Kim; Volker Wulfmeyer. Parameterization of Vegetation Scattering Albedo in the Tau-Omega Model for Soil Moisture Retrieval on Croplands. Remote Sensing 2020, 12, 2939 .

AMA Style

Chang-Hwan Park, Thomas Jagdhuber, Andreas Colliander, Johan Lee, Aaron Berg, Michael Cosh, Seung-Bum Kim, Yoonjae Kim, Volker Wulfmeyer. Parameterization of Vegetation Scattering Albedo in the Tau-Omega Model for Soil Moisture Retrieval on Croplands. Remote Sensing. 2020; 12 (18):2939.

Chicago/Turabian Style

Chang-Hwan Park; Thomas Jagdhuber; Andreas Colliander; Johan Lee; Aaron Berg; Michael Cosh; Seung-Bum Kim; Yoonjae Kim; Volker Wulfmeyer. 2020. "Parameterization of Vegetation Scattering Albedo in the Tau-Omega Model for Soil Moisture Retrieval on Croplands." Remote Sensing 12, no. 18: 2939.

Journal article
Published: 28 August 2020 in IEEE Geoscience and Remote Sensing Letters
Reads 0
Downloads 0

Ongoing evaluation of the soil moisture active passive (SMAP) soil moisture products has utilized validation networks distributed in several regions around the world. The in situ reference used for validation of the soil moisture retrieval algorithm is associated with measurements from soil moisture probes typically located at 5 cm beneath the soil surface; however, some networks also consider a vertically oriented probe that measures from 0 to 5 cm. In this study, we compare the correlation and unbiased root mean square error (ubRMSE) from the SMAP L2 radiometer soil moisture product when compared to in situ measurements taken at 5 cm (approximately 3.5-6.5 cm) below the surface and measurements taken as an integrated measure from 0 to 5.7 cm. The data were obtained from two SMAP validation networks in Canada: the Kenaston network in Saskatchewan and Carman network situated in Manitoba. At both sites, correlations between the in situ and the SMAP L2 product were consistently higher with vertically oriented probes following rain events. With respect to the ubRMSE, the vertically oriented probes at the Carman site had lower ubRMSE with the SMAP product than the horizontal probes that are currently used for validation activities. In some cases, vertical probe information should be considered in validation approaches when this data is available and could be considered in the design of in situ calibration/validation networks. These results may be useful in design considerations of networks for upcoming soil moisture product validation.

ACS Style

Aaron A. Berg; Jaison Thomas Ambadan; Andreas Colliander; Heather McNairn; Jarrett Powers; Erica Tetlock. The Impact of In Situ Probe Orientation on SMAP Validation Statistics. IEEE Geoscience and Remote Sensing Letters 2020, PP, 1 -5.

AMA Style

Aaron A. Berg, Jaison Thomas Ambadan, Andreas Colliander, Heather McNairn, Jarrett Powers, Erica Tetlock. The Impact of In Situ Probe Orientation on SMAP Validation Statistics. IEEE Geoscience and Remote Sensing Letters. 2020; PP (99):1-5.

Chicago/Turabian Style

Aaron A. Berg; Jaison Thomas Ambadan; Andreas Colliander; Heather McNairn; Jarrett Powers; Erica Tetlock. 2020. "The Impact of In Situ Probe Orientation on SMAP Validation Statistics." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.

Preprint
Published: 30 July 2020
Reads 0
Downloads 0

An accurate radiative transfer model (RTM) is essential for the retrieval of soil moisture (SM) from microwave remote sensing data, such as the passive microwave measurements from the Soil Moisture Active Passive (SMAP) mission. This mission delivers soil moisture products based upon L-band brightness temperature data, via retrieval algorithms for surface and root-zone soil moisture, the latter is retrieved using data assimilation and model support. We found that the RTM based on the tau-omega (?-ω) model, can suffer from significant errors over croplands (in average between -9.4K and + 12.0K for Single Channel Algorithm SCA; -8K and + 9.7K for Dual-Channel Algorithm DCA) if the vegetation scattering albedo (omega) is treated as a constant and the temporal variations are not accounted. In order to reduce this uncertainty, we propose a time-varying parameterization of omega for the widely established zeroth order radiative transfer ?-ω model. The main assumption is that omega can be expressed by a functional relationship between vegetation optical depth (tau) and the Green Vegetation Fraction (GVF). The validation was performed from 14 May to 13 December 2015 over 61 Climate Reference Network sites (SCRN) classified as croplands. The application of the proposed time-varying vegetation scattering albedo results in a consistent improvement for the unbiased root mean square error of 16% for SCA and 15% for DCA. The reduction for positive and negative biases was 45% and 5% for SCA and 26% and 12% for DCA, respectively. This indicates that vegetation dynamics on croplands are better represented by a time-dynamic single scattering albedo.

ACS Style

Chang-Hwan Park; Thomas Jagdhuber; Andreas Colliander; Johan Lee; Aaron Berg; Michael Cosh; Sung-Bum Kim; Yoonjae Kim; Volker Wulfmeyer. Parameterization of Vegetation Scattering Albedo in the Tau-Omega Model for Soil Moisture Retrieval on Croplands. 2020, 1 .

AMA Style

Chang-Hwan Park, Thomas Jagdhuber, Andreas Colliander, Johan Lee, Aaron Berg, Michael Cosh, Sung-Bum Kim, Yoonjae Kim, Volker Wulfmeyer. Parameterization of Vegetation Scattering Albedo in the Tau-Omega Model for Soil Moisture Retrieval on Croplands. . 2020; ():1.

Chicago/Turabian Style

Chang-Hwan Park; Thomas Jagdhuber; Andreas Colliander; Johan Lee; Aaron Berg; Michael Cosh; Sung-Bum Kim; Yoonjae Kim; Volker Wulfmeyer. 2020. "Parameterization of Vegetation Scattering Albedo in the Tau-Omega Model for Soil Moisture Retrieval on Croplands." , no. : 1.

Letter
Published: 21 July 2020 in Remote Sensing
Reads 0
Downloads 0

Land cover management practices, including the adoption of cover crops or retaining crop residue during the non-growing season, has important impacts on soil health. To broadly survey these practices, a number of remotely sensed products are available but issues with cloud cover and access to agriculture fields for validation purposes may limit the collection of data over large regions. In this study, we describe the development of a mobile roadside survey procedure for obtaining ground reference data for the remote sensing of agricultural land use practices. The key objective was to produce a dataset of geo-referenced roadside digital images that can be used in comparison to in-field photos to measure agricultural land use and land cover associated with crop residue and cover cropping in the non-growing season. We found a very high level of correspondence (>90% level of agreement) between the mobile roadside survey to in-field ground verification data. Classification correspondence was carried out with a portion of the county-level census image data against 114 in-field manually categorized sites with a level of agreement of 93%. The few discrepancies were in the differentiation of residue levels between 30–60% and >60%, both of which may be considered as achieving conservation practice standards. The described mobile roadside image capture system has advantages of relatively low cost and insensitivity to cloudy days, which often limits optical remote sensing acquisitions during the study period of interest. We anticipate that this approach can be used to reduce associated field costs for ground surveys while expanding coverage areas and that it may be of interest to industry, academic, and government organizations for more routine surveys of agricultural soil cover during periods of seasonal cloud cover.

ACS Style

Neal Pilger; Aaron Berg; Pamela Joosse. Semi-Automated Roadside Image Data Collection for Characterization of Agricultural Land Management Practices. Remote Sensing 2020, 12, 2342 .

AMA Style

Neal Pilger, Aaron Berg, Pamela Joosse. Semi-Automated Roadside Image Data Collection for Characterization of Agricultural Land Management Practices. Remote Sensing. 2020; 12 (14):2342.

Chicago/Turabian Style

Neal Pilger; Aaron Berg; Pamela Joosse. 2020. "Semi-Automated Roadside Image Data Collection for Characterization of Agricultural Land Management Practices." Remote Sensing 12, no. 14: 2342.

Preprint content
Published: 10 July 2020
Reads 0
Downloads 0

Soil microwave permittivity is a crucial parameter in passive microwave retrieval algorithms but remains a challenging variable to measure. To validate and improve satellite microwave data products, precise and reliable estimations of the relative permittivity (ɛr = ɛ / ɛ0 = ɛ’ - jɛ’’; unitless) of soils are required, particularly for frozen soils. In this study, permittivity measurements were acquired using two different instruments: the newly designed open-ended coaxial probe (OECP) and the conventional Stevens HydraProbe. Both instruments were used to characterize the permittivity of soil samples undergoing several freeze/thaw cycles in a laboratory environment. The measurements were compared to soil permittivity models. We show that the OECP is a suitable device for measuring frozen (ɛ’frozen = [3.5;6.0], ɛ’’frozen = [0.4;1.2]) and thawed (ɛ’thawed = [6.5;22.8], ɛ’’thawed = [1.4;5.7]) soil microwave permittivity. We also demonstrate that cheaper and widespread soil permittivity probes operating at lower frequencies (i.e. Stevens HydraProbe) can be used to estimate microwave permittivity given proper calibration relative to an L-band (1–2 GHz) probe. This study also highlighted the need to improve dielectric soil models, particularly during freeze/thaw transitions. There are still important discrepancies between in situ and modelled estimates and no current model accounts for the hysteresis effect shown between freezing and thawing processes which could have a significant impact on freeze/thaw detection from satellites.

ACS Style

Alex Mavrovic; Renato Pardo Lara; Aaron Berg; François Demontoux; Alain Royer; Alexandre Roy. Soil dielectric characterization at L-band microwave frequencies during freeze-thaw transitions. 2020, 1 -22.

AMA Style

Alex Mavrovic, Renato Pardo Lara, Aaron Berg, François Demontoux, Alain Royer, Alexandre Roy. Soil dielectric characterization at L-band microwave frequencies during freeze-thaw transitions. . 2020; ():1-22.

Chicago/Turabian Style

Alex Mavrovic; Renato Pardo Lara; Aaron Berg; François Demontoux; Alain Royer; Alexandre Roy. 2020. "Soil dielectric characterization at L-band microwave frequencies during freeze-thaw transitions." , no. : 1-22.

Research article
Published: 13 June 2020 in Earth Surface Processes and Landforms
Reads 0
Downloads 0

Thaw slumps in ice‐rich permafrost can retreat tens of metres per summer, driven by the melt of subaerially exposed ground ice. However, some slumps retain an ice‐veneering debris cover as they retreat. A quantitative understanding of the thermal regime and geomorphic evolution of debris‐covered slumps in a warming climate is largely lacking. To characterize the thermal regime, we instrumented four debris‐covered slumps in the Canadian Low Arctic and developed a numerical conduction‐based model. The observed surface temperatures >20° C and steep thermal gradients indicate that debris insulates the ice by shifting the energy balance towards radiative and turbulent losses. After the model was calibrated and validated with field observations, it predicted sub‐debris ice melt to decrease four‐fold from 1.9 to 0.5 mas the thickness of the fine‐grained debris quadruples from 0.1 to 0.4 m. With warming temperatures, melt is predicted to increase most rapidly, in relative terms, for thick (∼0.5–1.0 m) debris covers. The morphology and evolution of the debris‐covered slumps were characterized using field and remote sensing observations, which revealed differences in association with morphology and debris composition. Two low‐angle slumps retreated continually despite their persistent fine‐grained debris covers. The observed elevation losses decreased from ∼1.0 m/yr where debris thickness ∼0.2 mto 0.1 m/yr where thickness ∼1.0 m. Conversely, a steep slump with a coarse‐grained debris veneer underwent short‐lived bursts of retreat, hinting at a complex interplay of positive and negative feedback processes. The insulative protection and behaviour of debris vary significantly with factors such as thickness, grain size and climate: debris thus exerts a fundamental, spatially variable influence on slump trajectories in a warming climate. © 2020 John Wiley & Sons, Ltd.

ACS Style

S. Zwieback; J. Boike; P. Marsh; Aaron Berg. Debris cover on thaw slumps and its insulative role in a warming climate. Earth Surface Processes and Landforms 2020, 45, 2631 -2646.

AMA Style

S. Zwieback, J. Boike, P. Marsh, Aaron Berg. Debris cover on thaw slumps and its insulative role in a warming climate. Earth Surface Processes and Landforms. 2020; 45 (11):2631-2646.

Chicago/Turabian Style

S. Zwieback; J. Boike; P. Marsh; Aaron Berg. 2020. "Debris cover on thaw slumps and its insulative role in a warming climate." Earth Surface Processes and Landforms 45, no. 11: 2631-2646.

Journal article
Published: 12 June 2020 in Agronomy
Reads 0
Downloads 0

The impacts of tillage practices and crop rotations are fundamental factors influencing changes in the soil carbon, and thus the sustainability of agricultural systems. The objective of this study was to compare soil carbon status and temporal changes in topsoil from different 4 year rotations and tillage treatments (i.e., no-till and conventional tillage). Rotation systems were primarily corn and soy-based and included cereal and alfalfa phases along with red clover cover crops. In 2018, soil samples were collected from a silty-loam topsoil (0–15 cm) from the 36 year long-term experiment site in southern Ontario, Canada. Total carbon (TC) contents of each sample were determined in the laboratory using combustion methods and comparisons were made between treatments using current and archived samples (i.e., 20 year and 9 year change, respectively) for selected crop rotations. Overall, TC concentrations were significantly higher for no-till compared with conventional tillage practices, regardless of the crop rotations employed. With regard to crop rotation, the highest TC concentrations were recorded in corn–corn–oats–barley (CCOB) rotations with red clover cover crop in both cereal phases. TC contents were, in descending order, found in corn–corn–alfalfa–alfalfa (CCAA), corn–corn–soybean–winter wheat (CCSW) with 1 year of seeded red clover, and corn–corn–corn–corn (CCCC). The lowest TC concentrations were observed in the corn–corn–soybean–soybean (CCSS) and corn–corn–oats–barley (CCOB) rotations without use of cover crops, and corn–corn–soybean–winter wheat (CCSW). We found that (i) crop rotation varieties that include two consecutive years of soybean had consistently lower TC concentrations compared with the remaining rotations; (ii) TC for all the investigated plots (no-till and/or tilled) increased over the 9 year and 20 year period; (iii) the no-tilled CCOB rotation with 2 years of cover crop showed the highest increase of TC content over the 20 year change period time; and (iv) interestingly, the no-till continuous corn (CCCC) rotation had higher TC than the soybean–soybean–corn–corn (SSCC) and corn–corn–soybean–winter wheat (CCSW). We concluded that conservation tillage (i.e., no-till) and incorporation of a cover crop into crop rotations had a positive effect in the accumulation of TC topsoil concentrations and could be suitable management practices to promote soil fertility and sustainability in our agricultural soils.

ACS Style

Ahmed Laamrani; Paul R. Voroney; Aaron A. Berg; Adam W. Gillespie; Michael March; Bill Deen; Ralph C. Martin. Temporal Change of Soil Carbon on a Long-Term Experimental Site with Variable Crop Rotations and Tillage Systems. Agronomy 2020, 10, 840 .

AMA Style

Ahmed Laamrani, Paul R. Voroney, Aaron A. Berg, Adam W. Gillespie, Michael March, Bill Deen, Ralph C. Martin. Temporal Change of Soil Carbon on a Long-Term Experimental Site with Variable Crop Rotations and Tillage Systems. Agronomy. 2020; 10 (6):840.

Chicago/Turabian Style

Ahmed Laamrani; Paul R. Voroney; Aaron A. Berg; Adam W. Gillespie; Michael March; Bill Deen; Ralph C. Martin. 2020. "Temporal Change of Soil Carbon on a Long-Term Experimental Site with Variable Crop Rotations and Tillage Systems." Agronomy 10, no. 6: 840.

Journal article
Published: 12 May 2020 in Remote Sensing
Reads 0
Downloads 0

Wildfires are a concerning issue in Canada due to their immediate impact on people’s lives, local economy, climate, and environment. Studies have shown that the number of wildfires and affected areas in Canada has increased during recent decades and is a result of a warming and drying climate. Therefore, identifying potential wildfire risk areas is increasingly an important aspect of wildfire management. The purpose of this study is to investigate if remotely sensed soil moisture products from the Soil Moisture and Ocean Salinity (SMOS) satellite can be used to identify potential wildfire risk areas for better wildfire management. We used the National Fire Database (NFDB) fire points and polygons to group the wildfires according to ecozone classifications, as well as to analyze the SMOS soil moisture data over the wildfire areas, between 2010–2017, across fourteen ecozones in Canada. Timeseries of 3-day, 5-day, and 7-day soil moisture anomalies prior to the onset of each wildfire occurrence were examined over the ecozones individually. Overall, the results suggest, despite the coarse-resolution, SMOS soil moisture products are potentially useful in identifying soil moisture anomalies where wildfire hot-spots may occur.

ACS Style

Jaison Thomas Ambadan; Matilda Oja; Ze’Ev Gedalof; Aaron A. Berg. Satellite-Observed Soil Moisture as an Indicator of Wildfire Risk. Remote Sensing 2020, 12, 1543 .

AMA Style

Jaison Thomas Ambadan, Matilda Oja, Ze’Ev Gedalof, Aaron A. Berg. Satellite-Observed Soil Moisture as an Indicator of Wildfire Risk. Remote Sensing. 2020; 12 (10):1543.

Chicago/Turabian Style

Jaison Thomas Ambadan; Matilda Oja; Ze’Ev Gedalof; Aaron A. Berg. 2020. "Satellite-Observed Soil Moisture as an Indicator of Wildfire Risk." Remote Sensing 12, no. 10: 1543.

Journal article
Published: 29 April 2020 in Water Resources Research
Reads 0
Downloads 0

We present a method to characterize soil moisture freeze‐thaw events and freezing/melting point depression using permittivity and temperature measurements, readily available from in situ sources. In cold regions soil freeze‐thaw processes play a critical role in the surface energy and water balance, with implications ranging from agricultural yields to natural disasters. Although monitoring of the soil moisture phase state is of critical importance, there is an inability to interpret soil moisture instrumentation in frozen conditions. To address this gap, we investigated the freeze‐thaw response of a widely used soil moisture probe, the HydraProbe (HP), in the laboratory. Soil freezing curves (SFC) and soil thawing curves (STC) were identified using the relationship between soil permittivity and temperature. The permittivity SFC/STC were fit using a logistic growth model to estimate the freezing/melting point depression (Tf/m) and its spread (s). Laboratory results showed the fitting routine requires permittivity changes greater than 3.8 to provide robust estimates and suggested a temperature bias is inherent in horizontally placed HPs. We tested the method using field measurements collected over the last seven years from Environment and Climate Change Canada and University of Guelph's Kenaston Soil Moisture Network in Saskatchewan, Canada. By dividing the time series into freeze‐thaw events and then into individual transitions the permittivity SFC/STC were identified. The freezing and melting point depression for the network was estimated as Tf/m = − 0.35 ± 0.2,with Tf = − 0.41 ± 0.22 °C and Tm = − 0.29 ± 0.16 °C respectively.

ACS Style

R. Pardo Lara; A. A. Berg; J. Warland; Erica Tetlock. In Situ Estimates of Freezing/Melting Point Depression in Agricultural Soils Using Permittivity and Temperature Measurements. Water Resources Research 2020, 56, 1 .

AMA Style

R. Pardo Lara, A. A. Berg, J. Warland, Erica Tetlock. In Situ Estimates of Freezing/Melting Point Depression in Agricultural Soils Using Permittivity and Temperature Measurements. Water Resources Research. 2020; 56 (5):1.

Chicago/Turabian Style

R. Pardo Lara; A. A. Berg; J. Warland; Erica Tetlock. 2020. "In Situ Estimates of Freezing/Melting Point Depression in Agricultural Soils Using Permittivity and Temperature Measurements." Water Resources Research 56, no. 5: 1.

Journal article
Published: 28 April 2020 in Remote Sensing
Reads 0
Downloads 0

Growing cover or winter crops and retaining crop residue on agricultural lands are considered beneficial management practices to address soil health and water quality. Remote sensing is a valuable tool to assess and map crop residue cover and cover crops. The objective of this study is to evaluate the performance of linear spectral unmixing for estimating soil cover in the non-growing season (November–May) over the Canadian Lake Erie Basin using seasonal multitemporal satellite imagery. Soil cover ground measurements and multispectral Landsat-8 imagery were acquired for two areas throughout the 2015–2016 non-growing season. Vertical soil cover photos were collected from up to 40 residue and 30 cover crop fields for each area (e.g., Elgin and Essex sites) when harvest, cloud, and snow conditions permitted. Images and data were reviewed and compiled to represent a complete coverage of the basin for three time periods (post-harvest, pre-planting, and post-planting). The correlations between field measured and satellite imagery estimated soil covers (e.g., residue and green) were evaluated by coefficient of determination (R2) and root mean square error (RMSE). Overall, spectral unmixing of satellite imagery is well suited for estimating soil cover in the non-growing season. Spectral unmixing using three-endmembers (i.e., corn residue-soil-green cover; soybean residue-soil-green cover) showed higher correlations with field measured soil cover than spectral unmixing using two- or four-endmembers. For the nine non-growing season images analyzed, the residue and green cover fractions derived from linear spectral unmixing using corn residue-soil-green cover endmembers were highly correlated with the field-measured data (mean R2 of 0.70 and 0.86, respectively). The results of this study support the use of remote sensing and spectral unmixing techniques for monitoring performance metrics for government initiatives, such as the Canada-Ontario Lake Erie Action Plan, and as input for sustainability indicators that both require knowledge about non-growing season land management over a large area.

ACS Style

Ahmed Laamrani; Pamela Joosse; Heather McNairn; Aaron Berg; Jennifer Hagerman; Kathryn Powell; Mark Berry. Assessing Soil Cover Levels during the Non-Growing Season Using Multitemporal Satellite Imagery and Spectral Unmixing Techniques. Remote Sensing 2020, 12, 1397 .

AMA Style

Ahmed Laamrani, Pamela Joosse, Heather McNairn, Aaron Berg, Jennifer Hagerman, Kathryn Powell, Mark Berry. Assessing Soil Cover Levels during the Non-Growing Season Using Multitemporal Satellite Imagery and Spectral Unmixing Techniques. Remote Sensing. 2020; 12 (9):1397.

Chicago/Turabian Style

Ahmed Laamrani; Pamela Joosse; Heather McNairn; Aaron Berg; Jennifer Hagerman; Kathryn Powell; Mark Berry. 2020. "Assessing Soil Cover Levels during the Non-Growing Season Using Multitemporal Satellite Imagery and Spectral Unmixing Techniques." Remote Sensing 12, no. 9: 1397.

Journal article
Published: 25 April 2020 in Journal of Hydrology
Reads 0
Downloads 0

A trapezoidal boundary location algorithm (TBLA) for the dry/wet boundaries of the land surface temperature (Tm)-fractional vegetation coverage (fc) trapezoidal framework is derived. The new model, PCALEP, for Pixel Component Arranging Comparing and Layered Energy Partition, uses newly-developed TBLA and combines field-measured values of the extreme dry/wet points, to estimate the theoretical temperatures of the Tm-fc trapezoid and calculate surface energy fluxes. The automatic and high-precision location algorithm for dry/wet boundaries greatly reduces the uncertainty caused by the extreme pixels selection. The results show PCALEP simulates overall accuracy of root-mean-square-error (RMSE) and Bias for latent heat flux (LE) of 36.3 W/m2 and −21.3 W/m2, in Yueyang site and 39.7 W/m2 and −27.7 W/m2 in XiShuangBanNa (XSBN) site in three test days of 2015. PCALEP provides higher accuracy estimation of surface energy fluxes compared with several two-source models. The model simulation of T/ET (ratio of vegetation transpiration to evapotranspiration) in the study area was 0.69. Sensitivity analyses show that the greatest impacts on estimation of LE results are fc, Tm and solar radiation (So), followed by albedo-related parameters, with the least impact from meteorological parameters such as friction wind speed (u*). The TBLA proposed in the paper can be applied to all trapezoidal framework boundary location (such as Tm-fc, αm (Albdeo) - fc, etc.).

ACS Style

Han Chen; Jinhui Jeanne Huang; Aaron Berg; Edward McBean. Development of a trapezoidal framework-based model (PCALEP) for partition of land evapotranspiration. Journal of Hydrology 2020, 589, 124994 .

AMA Style

Han Chen, Jinhui Jeanne Huang, Aaron Berg, Edward McBean. Development of a trapezoidal framework-based model (PCALEP) for partition of land evapotranspiration. Journal of Hydrology. 2020; 589 ():124994.

Chicago/Turabian Style

Han Chen; Jinhui Jeanne Huang; Aaron Berg; Edward McBean. 2020. "Development of a trapezoidal framework-based model (PCALEP) for partition of land evapotranspiration." Journal of Hydrology 589, no. : 124994.