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Found 10 records similar to The Canadian Ag-Land Monitoring System (CALMS) - Quality Control, Cloud and Snow Flags

Federal

Each pixel value corresponds to the actual number (count) of valid Best-quality Max-NDVI values used to calculate the mean weekly values for that pixel. Since 2020, the maximum number of possible observations used to create the Mean Best-Quality Max-NDVI for the 2000-2014 period is n=20. However, because data quality varies both temporally and geographically (e.g. cloud cover and snow cover in spring; cloud near large water bodies all year), the actual number (count) of observations used to create baselines can vary significantly for any given week and year.

Last Updated: Jul. 19, 2021
Date Published: Nov. 1, 2012
Organization: Agriculture and Agri-Food Canada
Formats: PDF GeoTIF
Keywords:  Agriculture, Crops, Climate, Remote sensing
Federal

Each pixel value corresponds to the difference (anomaly) between the mean “Best-Quality” Max-NDVI of the week specified (e.g. Week 18, 2000-2014) and the “Best-Quality” Max-NDVI of the same week in a specific year (e.g. Week 18, 2015). Max-NDVI anomalies < 0 indicate where weekly Max-NDVI is lower than normal.

Last Updated: Jul. 27, 2021
Date Published: Nov. 1, 2012
Organization: Agriculture and Agri-Food Canada
Formats: WMS PDF HTML ESRI REST GeoTIF
Keywords:  Climate, Agriculture, Crops, Remote sensing
Federal

Each pixel value corresponds to the mean historical “Best-quality” Max-NDVI value for a given week, as calculated from the previous 20 years in the MODIS historical record (i.e. does not include data from the current year). These data are also often referred to as “weekly baselines” or “weekly normals”.

Last Updated: Jul. 19, 2021
Date Published: Nov. 1, 2012
Organization: Agriculture and Agri-Food Canada
Formats: PDF GeoTIF
Keywords:  Crops, Climate, Agriculture, Remote sensing
Federal

Each pixel value corresponds to the best quality maximum NDVI recorded within that pixel over the week specified. Poor quality pixel observations are removed from this product. Observations whose quality is degraded by snow cover, shadow, cloud, aerosols, and/or low sensor zenith angles are removed (and are assigned a value of “missing data”). In addition, negative Max-NDVI values, occurring where R reflectance > NIR reflectance, are considered non-vegetated and assigned a value of 0.

Last Updated: Jul. 19, 2021
Date Published: Nov. 1, 2012
Organization: Agriculture and Agri-Food Canada
Formats: PDF GeoTIF
Keywords:  Climate, Crops, Agriculture, Remote sensing
Federal

Each pixel value corresponds to the day-of-week (1-7) from which the Weekly Best-Quality NDVI retrieval is obtained (1 = Monday, 7 = Sunday).

Last Updated: Jul. 19, 2021
Date Published: Nov. 1, 2012
Organization: Agriculture and Agri-Food Canada
Formats: PDF GeoTIF
Keywords:  Agriculture, Crops, Climate, Remote sensing
Federal

AAFC’s Canadian Ag-Land Monitoring System (CALMS), operational since 2009, was developed by AAFC’s Earth Observation Service (EOS) to deliver weekly NDVI-based maps of crop condition in near-real-time. The CALMS uses data collected by the Moderate Resolution Imaging Spectro-radiometer (MODIS), a sensor mounted onboard NASA’s Terra satellite that has been acquiring data since February 2000. The state-of-the-art radiometric, spectral and spatial resolutions of MODIS Terra make it particularly well-suited for large-scale vegetation mapping and assessment. Crop condition (NDVI) maps are generated weekly by AAFC throughout Canada’s growing season, the period defined as the six-month period stretching from the start of Julian week 12 (end of March) to the end of Julian week 44 (late October).

Last Updated: Jul. 19, 2021
Date Published: Nov. 1, 2012
Organization: Agriculture and Agri-Food Canada
Formats: PDF GeoTIF
Keywords:  Climate, Crops, Agriculture, Remote sensing
Federal

The 2020 AAFC Land Use is a culmination and curated metaanalysis of several high-quality spatial datasets produced between 1990 and 2021 using a variety of methods by teams of researchers as techniques and capabilities have evolved. The information from the input datasets was consolidated and embedded within each 30m x 30m pixel to create consolidated pixel histories, resulting in thousands of unique combinations of evidence ready for careful consideration. Informed by many sources of high-quality evidence and visual observation of imagery in Google Earth, we apply an incremental strategy to develop a coherent best current understanding of what has happened in each pixel through the time series.

Last Updated: Aug. 25, 2022
Date Published: Jan. 25, 2018
Organization: Agriculture and Agri-Food Canada
Formats: PDF GeoTIF
Keywords:  Crops, Satellites, Agriculture, Farmlands
Federal

The 2005 AAFC Land Use is a culmination and curated metaanalysis of several high-quality spatial datasets produced between 1990 and 2021 using a variety of methods by teams of researchers as techniques and capabilities have evolved. The information from the input datasets was consolidated and embedded within each 30m x 30m pixel to create consolidated pixel histories, resulting in thousands of unique combinations of evidence ready for careful consideration. Informed by many sources of high-quality evidence and visual observation of imagery in Google Earth, we apply an incremental strategy to develop a coherent best current understanding of what has happened in each pixel through the time series.

Last Updated: Oct. 13, 2021
Date Published: Jan. 25, 2018
Organization: Agriculture and Agri-Food Canada
Formats: WMS PDF ESRI REST GeoTIF
Keywords:  Agriculture, Satellites, Crops, Farmlands
Federal

The 2010 AAFC Land Use is a culmination and curated metaanalysis of several high-quality spatial datasets produced between 1990 and 2021 using a variety of methods by teams of researchers as techniques and capabilities have evolved. The information from the input datasets was consolidated and embedded within each 30m x 30m pixel to create consolidated pixel histories, resulting in thousands of unique combinations of evidence ready for careful consideration. Informed by many sources of high-quality evidence and visual observation of imagery in Google Earth, we apply an incremental strategy to develop a coherent best current understanding of what has happened in each pixel through the time series.

Last Updated: Oct. 13, 2021
Date Published: Jan. 25, 2018
Organization: Agriculture and Agri-Food Canada
Formats: WMS PDF ESRI REST GeoTIF
Keywords:  Satellites, Crops, Agriculture, Farmlands
Federal

The 2015 AAFC Land Use is a culmination and curated metaanalysis of several high-quality spatial datasets produced between 1990 and 2021 using a variety of methods by teams of researchers as techniques and capabilities have evolved. The information from the input datasets was consolidated and embedded within each 30m x 30m pixel to create consolidated pixel histories, resulting in thousands of unique combinations of evidence ready for careful consideration. Informed by many sources of high-quality evidence and visual observation of imagery in Google Earth, we apply an incremental strategy to develop a coherent best current understanding of what has happened in each pixel through the time series.

Last Updated: Oct. 13, 2021
Date Published: Jan. 25, 2018
Organization: Agriculture and Agri-Food Canada
Formats: WMS PDF ESRI REST GeoTIF
Keywords:  Satellites, Crops, Agriculture, Farmlands
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