The comma-delimited fields in this dataset provide values for the remotely sensed variables analyzed for landscape blocks described in the paper, "Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate’s effects on wetland surface water, amphibians, and landscape conditions," by Sadinski et al. (submitted). The field labeled “BlockSite” links the records in this file with a set of boundaries in a shapefile called “Study_Block_Boundaries.shp” The records represent weekly measurements of normalized difference vegetation index (BlockNDVI) values and total evapotranspiration (BlockETmm), as well as the annual snow-off date (BlockDOYsnowfree) for the study blocks from January through August from 2008 to 2012.

This comma-delimited dataset provides values for the remotely sensed status of snow on/off analyzed for field study sites described in the paper, "Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate’s effects on wetland surface water, amphibians, and landscape conditions," by Sadinski et al. (submitted). These data provide an indication of snow presence at the spatial resolution of a 500-m square cell for each eight-day interval beginning in January and ending at the start July of each year from 2008-2012. The source for the data was the MOD10A2 snow product from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor. We extracted data for the cells associated with 35 field study sites for which we subsequently determined the timing of spring snow-free conditions. The data field labeled "BlockSite" links these values geospatially to a data field of the same name in an ESRI shapefile titled "Study_Block_Boundaries.shp" that delineates study blocks containing the field sites. Refer to the paper by Sadinski et al. (submitted) for details of the analyses performed.

This comma-delimited dataset provides values for the remotely sensed status of snow on/off analyzed for field study sites described in the paper, "Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate’s effects on wetland surface water, amphibians, and landscape conditions," by Sadinski et al. (submitted). These data provide an indication of snow presence at the spatial resolution of a 500-m square cell for each eight-day interval beginning in January and ending at the start July of each year from 2008-2012. The source for the data was the MOD10A2 snow product from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor. We extracted data for the cells associated with 35 field study sites for which we subsequently determined the timing of spring snow-free conditions. The data field labeled "BlockSite" links these values geospatially to a data field of the same name in an ESRI shapefile titled "Study_Block_Boundaries.shp" that delineates study blocks containing the field sites. Refer to the paper by Sadinski et al. (submitted) for details of the analyses performed.

This file includes satellite sensor-derived data on evapotranspiration, vegetation response (normalized difference vegetation index), and snow and weather-station data used to derive weekly growing degree units and total precipitation used in analyses described in the paper, "Challenges in complementing data from ground-based sensors with satellite-derived products to measure ecological changes in relation to climate – lessons from temperate wetland-upland landscapes."

This file includes satellite sensor-derived data on evapotranspiration, vegetation response (normalized difference vegetation index), and snow and weather-station data used to derive weekly growing degree units and total precipitation used in analyses described in the paper, "Challenges in complementing data from ground-based sensors with satellite-derived products to measure ecological changes in relation to climate – lessons from temperate wetland-upland landscapes."

This data release consists of four datasets that were used for evaluating winter drawdown patterns in 166 Massachusetts lakes greater than 0.3 km2 surface area. The first dataset (“Water area and level.csv”) provides water area and water level time series data of 166 lakes from 2016 to 2021. Water area and water level time-series data were derived from European Space Agency’s Sentinel 1 synthetic aperture radar satellite sensor using the JavaScript code in Google Earth Engine platform. Details of this code were described in the software release (https://doi.org/10.5066/P9ZA5I1U). The second dataset (“Water area interpolated.csv”) is the linearly-interpolated daily water area time series data of the 166 lakes from the first dataset that were used in winter drawdown classification model as input files. The third dataset (“Winter drawdown classification.csv”) is the winter drawdown classification model derived binary classification (1 for winter drawdown and 0 for non-winter drawdown) of 166 lakes for 5 years (2016–2021). The fourth dataset (“Winter drawdown metrics_2016.csv”, “Winter drawdown metrics_2017.csv”, “Winter drawdown metrics_2018.csv”, (“Winter drawdown metrics_2019.csv”, and “Winter drawdown metrics_2020.csv”) are the winter drawdown metrics such as timing, duration, and magnitude of drawdown derived for the winter drawdown lakes from the water area time series (second dataset) for 5 years. The codes used for the classification model and drawdown metrics are also available in the software release (https://doi.org/10.5066/P9ZA5I1U).

This data release consists of four datasets that were used for evaluating winter drawdown patterns in 166 Massachusetts lakes greater than 0.3 km2 surface area. The first dataset (“Water area and level.csv”) provides water area and water level time series data of 166 lakes from 2016 to 2021. Water area and water level time-series data were derived from European Space Agency’s Sentinel 1 synthetic aperture radar satellite sensor using the JavaScript code in Google Earth Engine platform. Details of this code were described in the software release (https://doi.org/10.5066/P9ZA5I1U). The second dataset (“Water area interpolated.csv”) is the linearly-interpolated daily water area time series data of the 166 lakes from the first dataset that were used in winter drawdown classification model as input files. The third dataset (“Winter drawdown classification.csv”) is the winter drawdown classification model derived binary classification (1 for winter drawdown and 0 for non-winter drawdown) of 166 lakes for 5 years (2016–2021). The fourth dataset (“Winter drawdown metrics_2016.csv”, “Winter drawdown metrics_2017.csv”, “Winter drawdown metrics_2018.csv”, (“Winter drawdown metrics_2019.csv”, and “Winter drawdown metrics_2020.csv”) are the winter drawdown metrics such as timing, duration, and magnitude of drawdown derived for the winter drawdown lakes from the water area time series (second dataset) for 5 years. The codes used for the classification model and drawdown metrics are also available in the software release (https://doi.org/10.5066/P9ZA5I1U).

These data were collected as part of the Stormwater Action Monitoring (SAM) program's Puget Small Streams (PSS) status and trends program. The data presented here were collected from 29 sites during water year 2020 for water temperature, air temperature, and sensor depth. Sensor depth was used as a surrogate for water depth and represents the arbitrary depth of water above the sensor location in the water column. Data was collected at 15-minute intervals from October 2019 until September 2020 using Hobo U20L-04 sensors that simultaneously measured temperature and pressure. All data were analyzed and approved following USGS continuous data processing procedures. Sensor depth data was used as a surrogate for stream stage in order to calculate three flow metrics: (1) the fraction of time that flow exceeded the annual mean (TQmean), (2) the Richards-Baker Index of flashiness (RBI), and (3) the annual tally of wet-season day-to-day flow reversals following methods outlined in Booth and Konrad (2017). In addition to these flow metrics, continuous temperature data was used to calculate the 7-day average daily maximum (7DADmax) temperature at each monitoring site. Included in this data release are the final approved continuous data, the input and output files used to calculate the metrics in R, and two html files showing the R code and process used to make these calculations. Booth, D. and C.P. Konrad. 2017. Hydrologic metrics for status-and trend monitoring in urban and urbanizing watersheds. Hydrological processes 31: 4507-4519. https://doi.org/10.1002/hyp.11369 Note: Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government

The polygons in this shapefile accompany the paper, “Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate’s effects on wetland surface water, amphibians, and landscape conditions,” by Sadinski et al. (submitted). The paper describes relations between climate dynamics and key ecological conditions and processes on wetland-upland landscapes in > 30 sites distributed across four study areas in the midwestern United States. The variables studied included both ground- and satellite-based measures. The polygons in this shapefile pertain to the latter and provide the boundaries of 4-km2 blocks that subsume the field sites and provide a landscape perspective for the timing and duration of snow, evapotranspiration, and photosynthetic activity from 2008 to 2012 from January through August. The data for these variables are provided in a separate tabular file (xxx insert reference link xxx) that links to this shapefile by a shared field called BlockSite. Citation: Sadinski, W., Gallant, A.L., Roth, M., Brown, J., Senay, G., Brininger, W., and Stoker, J., Submitted 2017, Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate’s effects on wetland surface water, amphibians, and landscape conditions: PLOS ONE.

The polygons in this shapefile accompany the paper, “Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate’s effects on wetland surface water, amphibians, and landscape conditions,” by Sadinski et al. (submitted). The paper describes relations between climate dynamics and key ecological conditions and processes on wetland-upland landscapes in > 30 sites distributed across four study areas in the midwestern United States. The variables studied included both ground- and satellite-based measures. The polygons in this shapefile pertain to the latter and provide the boundaries of 4-km2 blocks that subsume the field sites and provide a landscape perspective for the timing and duration of snow, evapotranspiration, and photosynthetic activity from 2008 to 2012 from January through August. The data for these variables are provided in a separate tabular file (xxx insert reference link xxx) that links to this shapefile by a shared field called BlockSite. Citation: Sadinski, W., Gallant, A.L., Roth, M., Brown, J., Senay, G., Brininger, W., and Stoker, J., Submitted 2017, Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate’s effects on wetland surface water, amphibians, and landscape conditions: PLOS ONE.