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Found 10 records similar to Weekly Best-Quality Maximum-NDVI

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 quality control, cloud cover and snow fraction value for each pixel in the Best-Quality Max-NDVI product.

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 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 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 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

AVHRR Pathfinder version 5.3 Level 3C night Sea Surface Temperature (SST) was acquired from NOAA at 4 km spatial resolution. The monthly mean value at all pixels was calculated for individual years, then all years were combined to produce final maps of monthly mean and monthly standard deviation of SST, and the number of occurrences of each pixel over the period of observation. The quality level of all satellite observations was also acquired with this dataset, and used to remove any pixels with a quality level lower than 4. Further, pixels with fewer than two occurrences over the period 1990-2020 were removed from these maps, and set to a NaN value in the tif files.

Last Updated: Mar. 29, 2022
Date Published: Mar. 29, 2021
Organization: Fisheries and Oceans Canada
Formats: PNG HTML ESRI REST GeoTIF
Keywords:  Climatology, Oceans, Climate
Federal

AVHRR Pathfinder version 5.3 Level 3C night Sea Surface Temperature (SST) was acquired from NOAA at 4 km spatial resolution. The monthly mean value at all pixels was calculated for individual years, then all years were combined to produce final maps of monthly mean and monthly standard deviation of SST, and the number of occurrences of valid data at each pixel over the period of observation. The quality level of all satellite observations was also acquired with this dataset, and used to remove any pixels with a quality level lower than 4. Further, pixels with fewer than two occurrences over the period 1981-2010 were removed from these maps, and set to a NaN value in the tif files.

Last Updated: Mar. 29, 2022
Date Published: Mar. 29, 2021
Organization: Fisheries and Oceans Canada
Formats: PNG HTML ESRI REST GeoTIF
Keywords:  Climatology, Climate, Oceans
Federal

MODIS-Aqua night long-wave Sea Surface Temperature (SST) images were acquired from the NASA Ocean Biology Processing Group at processing Level-2 (version 2018), 1-km resolution, spanning the period 2003-01-01 to 2020-12-31. Image pixels were aligned and mapped to a regular grid using the SeaDAS program, retaining all pixels with a quality level of ‘1’ or lower, which is recommended for scientific analysis. The monthly mean value at all pixels was calculated for individual years, and used to produce maps of the monthly climatological mean and standard deviation of SST. Additionally, the number of occurrences of valid data at each pixel over the period of observation were calculated.

Last Updated: Mar. 29, 2022
Date Published: Aug. 4, 2021
Organization: Fisheries and Oceans Canada
Formats: ESRI REST HTML PNG GeoTIF
Keywords:  Climatology, Oceans, Climate
Federal

MODIS-Aqua Chlorophyll-a (Chl-a) was acquired from the NASA Ocean Biology Processing Group at processing Level-2 (version 2018), 1-km resolution, where Chl-a concentration was calculated using the OC3/OCI method. The months of January and December were excluded from this dataset, as data in the winter months at higher latitudes are missing due to low sun angle preventing acquisition. The pixels were aligned on a regular grid using the SeaDAS program, after which the monthly geometric mean value at all pixels was calculated for individual years. Finally, the geometric mean and geometric standard deviation factor of chlorophyll-a were calculated by month from these images.

Last Updated: Mar. 29, 2022
Date Published: Apr. 28, 2021
Organization: Fisheries and Oceans Canada
Formats: ESRI REST HTML PNG GeoTIF
Keywords:  Climatology, Climate, Oceans
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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