Aaron Berg
YOU?
Author Swipe
View article: Challenges and Limitations in Comparing Satellite Microwave Vegetation Optical Depth (VOD) Against in-Situ Tree Hydraulics in the Canadian Boreal Forest
Challenges and Limitations in Comparing Satellite Microwave Vegetation Optical Depth (VOD) Against in-Situ Tree Hydraulics in the Canadian Boreal Forest Open
Microwave remote sensing can be used to estimate vegetation optical depth (VOD), a measure of the attenuation of microwave radiation by vegetation. VOD is tightly linked to vegetation properties such as water content and above-ground bioma…
View article: Satellite-Based Extension of the Soil Freezing Curve Paradigm: Detecting Extrinsic Freeze/Thaw Thresholds with SMAP in Mid-Latitudinal Agricultural Fields
Satellite-Based Extension of the Soil Freezing Curve Paradigm: Detecting Extrinsic Freeze/Thaw Thresholds with SMAP in Mid-Latitudinal Agricultural Fields Open
We present a novel method for surface freeze/thaw (F/T) classification based on L-band brightness temperature (TB), as measured by the Soil Moisture Active Passive (SMAP) mission, combined with thermodynamic temperature estimates, whether …
View article: Automated Semantic Segmentation of Arctic Surface Water Features with Very-High Resolution Satellite X-Band Radar Imagery and U-Net Deep Learning
Automated Semantic Segmentation of Arctic Surface Water Features with Very-High Resolution Satellite X-Band Radar Imagery and U-Net Deep Learning Open
Repeatable methods capable of quantifying Arctic surface water extent at high resolutions are important, but still require development. Here, we present a study using very-high resolution (VHR) X-band Synthetic Aperture Radar (SAR) imagery…
View article: Improving Seasonally Frozen Ground Monitoring Using Soil Freezing Characteristic Curve in Permittivity–Temperature Space
Improving Seasonally Frozen Ground Monitoring Using Soil Freezing Characteristic Curve in Permittivity–Temperature Space Open
Frozen ground, a key indicator of climate change, profoundly influences ecological, hydrological, and carbon flux processes in cold regions. However, traditional monitoring methods, which rely on a binary 0 °C soil temperature threshold, f…
View article: The Predictive Skill of a Remote Sensing-Based Machine Learning Model for Ice Wedge and Visible Ground Ice Identification in Western Arctic Canada
The Predictive Skill of a Remote Sensing-Based Machine Learning Model for Ice Wedge and Visible Ground Ice Identification in Western Arctic Canada Open
Fine-scale maps of ground ice and related surface features are critical for permafrost-related modelling and management. However, such maps are lacking across almost the entire Arctic. Machine learning provides the potential to automate re…
View article: Impact of L-band Vegetation Optical Depth Temporal Variation on Soil Moisture Retrieval
Impact of L-band Vegetation Optical Depth Temporal Variation on Soil Moisture Retrieval Open
Forests are one of the most essential components of the Earth system. They account for a large part of the total global photosynthetic activity, store a significant amount of the total carbon, and provide a habitat for countless species. A…
View article: Post-hoc Evaluation of Sample Size in a Regional Digital Soil Mapping Project
Post-hoc Evaluation of Sample Size in a Regional Digital Soil Mapping Project Open
The transition from conventional soil mapping (CSM) to digital soil mapping (DSM) not only affects the final map products, but it also affects the concepts of scale, resolution, and sampling intensity. This is critical because in the CSM a…
View article: Assessment of L-Band Passive Microwave Soil Moisture Retrievals Against In Situ Measurements in the North American Boreal Biome
Assessment of L-Band Passive Microwave Soil Moisture Retrievals Against In Situ Measurements in the North American Boreal Biome Open
International audience
View article: Soil Moisture Active/Passive Validation Experiment Within the Canadian Boreal Forest in 2022 (SMAPVEX22-Boreal)
Soil Moisture Active/Passive Validation Experiment Within the Canadian Boreal Forest in 2022 (SMAPVEX22-Boreal) Open
The soil moisture active passive (SMAP) validation experiment in the boreal forest took place near Candle Lake, Saskatchewan within an area previously studied as a part of the experiments conducted at the Boreal Ecosystem Research and Moni…
View article: SMAP Validation Experiment 2019–2022 (SMAPVEX19–22): Field Campaign to Improve Soil Moisture and Vegetation Optical Depth Retrievals in Temperate Forests
SMAP Validation Experiment 2019–2022 (SMAPVEX19–22): Field Campaign to Improve Soil Moisture and Vegetation Optical Depth Retrievals in Temperate Forests Open
Satellite-based retrieval of forest soil moisture (SM) and vegetation optical depth (VOD) are two long-standing unresolved issues hindering advances in hydrology, ecology, and Earth system science. A key obstacle is the lack of adequate re…
View article: Evaluating soil moisture retrieval in Arctic and sub-Arctic environments using passive microwave satellite data
Evaluating soil moisture retrieval in Arctic and sub-Arctic environments using passive microwave satellite data Open
Soil Moisture (SM) is a key parameter in northern Arctic and sub-Arctic (A-SA) environments that are highly vulnerable to climate change. We evaluated six SM satellite passive microwave datasets using thirteen ground-based SM stations acro…
View article: From drought hazard to risk: A spring wheat vulnerability assessment in the Canadian Prairies
From drought hazard to risk: A spring wheat vulnerability assessment in the Canadian Prairies Open
The Canadian drought monitor (CDM) provides monthly assessments of drought severity but does not provide guidance on the potential agricultural impacts. This study extends the CDM to a comprehensive risk assessment framework by assessing t…
View article: Sample Size Optimization for Digital Soil Mapping: An Empirical Example
Sample Size Optimization for Digital Soil Mapping: An Empirical Example Open
In the evolving field of digital soil mapping (DSM), the determination of sample size remains a pivotal challenge, particularly for large-scale regional projects. We introduced the Jensen-Shannon Divergence (DJS), a novel tool recently app…
View article: Evaluating the Topographic Factors for Land Suitability Mapping of Specialty Crops in Southern Ontario
Evaluating the Topographic Factors for Land Suitability Mapping of Specialty Crops in Southern Ontario Open
Climate change research identifies risks to agriculture that will impact agricultural land suitability. To mitigate these impacts, agricultural growing regions will need to adapt, diversify, or shift in location. Various machine learning a…
View article: An efficient soil moisture sampling scheme for the improvement of remotely sensed soil moisture validation over an agricultural field
An efficient soil moisture sampling scheme for the improvement of remotely sensed soil moisture validation over an agricultural field Open
An efficient and robust soil moisture (SM) sampling scheme that can capture the spatial variability of SM is required for the accurate calibration and validation of satellite-based SM retrievals. Often, this process requires numerous sampl…
View article: Leveraging google earth engine cloud computing for large-scale arctic wetland mapping
Leveraging google earth engine cloud computing for large-scale arctic wetland mapping Open
Climate-driven permafrost degradation and an intensification of the hydrological cycle are rapidly altering the intricate ecohydrological processes of Arctic wetlands, threatening their long-term carbon sequestration capabilities. Addressi…
View article: Understanding the Drivers of Drought Onset and Intensification in the Canadian Prairies: Insights from Explainable Artificial Intelligence (XAI)
Understanding the Drivers of Drought Onset and Intensification in the Canadian Prairies: Insights from Explainable Artificial Intelligence (XAI) Open
Recent advances in artificial intelligence (AI) and explainable AI (XAI) have created opportunities to better predict and understand drought processes. This study uses a machine learning approach for understanding the drivers of drought se…
View article: A Machine Learning Framework for Predicting and Understanding the Canadian Drought Monitor
A Machine Learning Framework for Predicting and Understanding the Canadian Drought Monitor Open
Drought is a costly natural disaster that impacts economies and ecosystems worldwide, so monitoring drought and communicating its impacts to individuals, communities, industry, and governments is important for mitigation, adaptation, and d…
View article: Divergence metrics for determining optimal training sample size in digital soil mapping
Divergence metrics for determining optimal training sample size in digital soil mapping Open
Digital soil mapping (DSM) typically requires three common ingredients: georeferenced samples, environmental covariates, and a model. Of the three, sample design, or the selection of sample size and locations, has received considerably les…
View article: Supplementary Table from Sensitive Detection of Mono- and Polyclonal ESR1 Mutations in Primary Tumors, Metastatic Lesions, and Cell-Free DNA of Breast Cancer Patients
Supplementary Table from Sensitive Detection of Mono- and Polyclonal ESR1 Mutations in Primary Tumors, Metastatic Lesions, and Cell-Free DNA of Breast Cancer Patients Open
Table S1. Sequence of targeted amplification primers. Table S2. Sequence of ddPCR primer and probes. Table S3. Cellularity and location of bone metastases.
View article: Supplementary Table from Sensitive Detection of Mono- and Polyclonal ESR1 Mutations in Primary Tumors, Metastatic Lesions, and Cell-Free DNA of Breast Cancer Patients
Supplementary Table from Sensitive Detection of Mono- and Polyclonal ESR1 Mutations in Primary Tumors, Metastatic Lesions, and Cell-Free DNA of Breast Cancer Patients Open
Table S1. Sequence of targeted amplification primers. Table S2. Sequence of ddPCR primer and probes. Table S3. Cellularity and location of bone metastases.
View article: Data from Sensitive Detection of Mono- and Polyclonal ESR1 Mutations in Primary Tumors, Metastatic Lesions, and Cell-Free DNA of Breast Cancer Patients
Data from Sensitive Detection of Mono- and Polyclonal ESR1 Mutations in Primary Tumors, Metastatic Lesions, and Cell-Free DNA of Breast Cancer Patients Open
Purpose: Given the clinical relevance of ESR1 mutations as potential drivers of resistance to endocrine therapy, this study used sensitive detection methods to determine the frequency of ESR1 mutations in primary and metastatic breast canc…
View article: Supplementary Figures from Sensitive Detection of Mono- and Polyclonal ESR1 Mutations in Primary Tumors, Metastatic Lesions, and Cell-Free DNA of Breast Cancer Patients
Supplementary Figures from Sensitive Detection of Mono- and Polyclonal ESR1 Mutations in Primary Tumors, Metastatic Lesions, and Cell-Free DNA of Breast Cancer Patients Open
Supplementary Figure S1A. Pre-amplification preserves mutant allele frequency and maintains sensitivity of ESR1-D538G mutation detection by ddPCR. Supplementary Figure S1B. Pre-amplification preserves mutant allele frequency and maintains …
View article: Data from Sensitive Detection of Mono- and Polyclonal ESR1 Mutations in Primary Tumors, Metastatic Lesions, and Cell-Free DNA of Breast Cancer Patients
Data from Sensitive Detection of Mono- and Polyclonal ESR1 Mutations in Primary Tumors, Metastatic Lesions, and Cell-Free DNA of Breast Cancer Patients Open
Purpose: Given the clinical relevance of ESR1 mutations as potential drivers of resistance to endocrine therapy, this study used sensitive detection methods to determine the frequency of ESR1 mutations in primary and metastatic breast canc…