HiResAlp_MobileCampaigns: HiResAlp Mobile Field Campaign
In JBrenn/SoilMoisturePattern: Analyse Soil Moiture Pattern

Description Usage Format Details Source References Examples

Data frame containing field data gathered manually within the HiResAlp project 2013-2015. Data on volumetric soil moisture content are combined with topographic, vegetation and land use characteristics derived from different sources. Climatic drivers (preceding precipitation, air temperature, relative humidity, and incoming solar radiation) are measured in representative locations within the LTER site Matsch/Mazia. Land use and soil classifications were gained during intensive field investigations. Remote sensing products, like MODIS vegetation indices, accomplish the data set. The data set is a basis for better understanding of inter-dependencies between soil moisture, topography and atmosperic conditions/weather, land use and soil variability. It serves to investigate soil moisture variablility and stability in time and space. The campaigns aim to validate remote sensing products coming from satellites (e.g. RADARSAT2 or Sentinel images), accomplish high resolution NDVI and thermal maps (drone products) and, moreover, can be useful for calibrating hydrological models or data assimilation approaches (e.g. combining ground sensed data, EO observations and modeling).

1	data("HiResAlp_MobileCampaigns")

A data frame with 2443 observations on the following 58 variables.

ID: integer, ID of observation
CampaignType: Faktor, Topology of HiResAlp campaign, RADAR for campaigns considered for EO data calibration/validation (in MUNTATSCHINIG or MATSCH valley, in both regions if not indicated), DRONE for campaigns to accomplish drone field campaign

Datetime variables

date: Factor, Date of observation - format: yyyy-mm-dd
time: character, Time of observation - format: HH:MM:SS
year: integer, Year of observation - format: yyyy
month: integer, Month of observation
day: integer, Day of observation
DOY: integer, Day Of Year
season: character, Season ("DJF","MAM","JJA","SON")
vegPeriod: integer, Vegetation period: 1 - Apr. 1st to Nov. 1st, no Vegetation period: 0

Geographic location of measurement

longitude: numeric, geographical position longitude in degree
latitude: numeric, geographical position latitude in degree
utmx: numeric, geographical position x in meter
utmy: numeric, geographical position y in meter

Data measured with WET sensor

Sensor: character, name of sensor used
SoilMoisture_mean: numeric, Volumetric Soil Moisture (SMC) Mean Value (raw data); for "WET-2 Sensor, Delta-T" aggregation according to sampling strategy of HiResAlp Campaigns; for "HydroSense II, Campbell" aggregation of measurment which are very close (rounding of utm xy data)
SoilMoisture_sd: Volumetric Soil Moisture Standard Deviation (raw data)
SoilTemperature_mean: Soil Temperature Mean Value in degree Celsius; only available for "WET-2 Sensor, Delta-T"
SoilTemperature_sd: Soil Temperature Standard Deviation
Permittivity_mean: Soil Permittivity (Eb) Mean Value in mS/m; measure variable by sensor, used to derive SMC
Permittivity_sd: Soil Permittivity Standard Deviation
SoilMoisture_mean_Processed: numeric, Volumetric Soil Moisture Mean Value (processed data); for "WET-2 Sensor, Delta-T" SMC is calculated by the formula SMC = (sqrt(Eb) - a0) / a1, where the parameters a0 and a1 are soil specific paramters and have been estimated from the "HydroSense II, Campbell" data

Measurement location meta data

Landuse: Factor, Land Use Class derived from field campaign; Classes: 1-irr. Meadow; 2- non irr. Meadow; 3- addandoned Meadow; 4- Pasture; 5- Other; 6- Other: Shrubs; 7- Other: Forest; missing values were filled with Information derived from former vegetation survey in Matsch/Mazia valley - see below.
SoilCoverage: Factor, Soil Coverage Class derived from field campaign [percent]; Classes: 1- Bare Soil, 2- 0.25, 3- 0.50; 4- 0.75; 5- 1
VegetationHeight: Factor, Vegetation Height Class dervived from field campaign [cm]; Classes: 1- >10cm 2- [10cm;25cm]; 3- [25cm;50cm]; 4- [50cm;75cm]; 5- >75cm
Notes: character, Note taken during field campaign

Topographic and hydrological variables derived from digital elevation model

elevation: numeric, Elevation in m a.s.l. from DEM (resolution: 2.5m)
slope: numeric, Slope in degree derived from DEM (resolution: 2.5m)
aspect: numeric, Aspectin degree derived from DEM (resolution: 2.5m)
curvProfile: numeric, profile Curvature derived from DEM (resolution: 2.5m)
curvHorizon: numeric, horizon Curvature derived from DEM (resolution: 2.5m)
elevation: numeric, Elevation in m a.s.l. from DEM (resolution: 2.5m)
slope: numeric, Slope in degree derived from DEM (resolution: 2.5m)
aspect: numeric, Aspectin degree derived from DEM (resolution: 2.5m)
curvProfile: numeric, profile Curvature derived from DEM (resolution: 2.5m)
curvHorizon: numeric, horizon Curvature derived from DEM (resolution: 2.5m)
flowAccum: numeric, Flow Accumulation derived from DEM (resolution: 2.5m), calculation with SAGA GIS: Multiple Flow Direction based on Maximum Downslope Gradient (Qin et al. 2011)
TWI: numeric, Topographic Wetness Index derived from Slope and flowAccum (resolution: 2.5m), calculation with SAGA GIS: ln(a/tan(beta)), where a is the area of the hillslope per unit contour length that drains through any point, and tan(beta) is the local surface topographic slope (delta vertical) / (delta horizontal).

Information derived from vegetation survey 2015 in Matsch/Mazia valley by Michaela Plaikner

StrukturCL: integer, Structure type class
StruturTyp: Factor, Structure Type label
NutzungCL: integer, current Land Use class
AktuelleNutzung: Factor, current Land Use label
VegTypCL: integer, Vegetation type class
VegLab3: Factor, Vegetation Type Label 3; very specific e.g. "Goldhaferwiese"
VegLab2: Factor, Vegetation Type Label 2; e.g. "Sekundaere Rasengesellschaften"
VegLab1: Factor, Vegetation Type Label 1; general e.g. "FETTWEIDEN UND FETTRASEN"
TrittID: integer, Damage by treads (cattle, sheep, ...) class
Trittschaeden: Factor, Damage by treads label
VegShapeID: numeric, ID of shapes in vegetation map; useful for extracting additional information

Information derived from former vegetation survey in Matsch/Mazia valley

landuseID: numeric, Land Use class
landuseLabel: Factor, Land Use type label

Information derived from soil survey in Matsch/Mazia valley in 2011

soilID: numeric, Soil class
soilID: Factor, Soil type
soilID: Factor, Texture class
soilID: Factor, Soil type in GEOtop model

Information on atmospheric condition / weather (base stations P2 - 1500 m a.s.l and B3 - 2000 m a.s.l.)

Temp: Air Temperature in degC, mean value for day of observation
RH: Relative Humidity in percent, mean value for day of observation
SolarRadiation: Incoming Solar Radiation in W/m2, mean value for day of observation
antecedentRain1_B3: Rainfall Amount in mm, 24 hours before measurement, station B3
antecedentRain3_B3: Rainfall Amount in mm, 3 days before measurement, station B3
antecedentRain5_B3: Rainfall Amount in mm, 5 days before measurement, station B3
antecedentRain10_B3: Rainfall Amount in mm, 10 days before measurement, station B3
antecedentRain10_B3: Rainfall Amount in mm, 40 days before measurement, station B3
antecedentRain1_P2: Rainfall Amount in mm, 24 hours before measurement, station P2
antecedentRain3_P2: Rainfall Amount in mm, 3 days before measurement, station P2
antecedentRain5_P2: Rainfall Amount in mm, 5 days before measurement, station P2
antecedentRain10_P2: Rainfall Amount in mm, 10 days before measurement, station P2
antecedentRain10_P2: Rainfall Amount in mm, 40 days before measurement, station P2

Remote Sensing Data (e.g. MODIS, RADARSAT)

radarsat_smc: RADARSAT soil moisture product
MODIS.start.date: Date of first MODIS product retrieved
MODIS.end.date: Date of last MODIS product retrieved
MODIS.NDVI.mean: numeric, MODIS Normalized Diverence (NDVI) mean values, EO map resolution 250m, [0;1]
MODIS_EVI: numeric, MODIS EVI mean value, extension of NDVI, EO map resolution 250m, [0;1]

MODIS data are point wise extracted using the functionalities of MODIStools. In specific the functions MODISSubsets and MODISsummary were used for a time period of 14 days araound the observation day. Data sources see below.

MODIS Vegetation Index Products (NDVI and EVI): MOD13Q1 (16-Day L3 Global 250m) https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod13q1

MODIS Evapotranspiration (ET and LE): MOD16A2 (8-day Global 1km) ftp://ftp.ntsg.umt.edu/pub/MODIS/NTSG_Products/MOD16/

Qin et al. (2011): An approach to computing topographic wetness index based on maximum downslope gradient., Precision Agriculture, 12(1), 32-43. http://doi.org/10.1007/s11119-009-9152-y

Tuck et al. (2014). MODISTools - downloading and processing MODIS remotely sensed data in R. Ecology and Evolution, 4(24), 4658<e2><80><93>4668. http://doi.org/10.1002/ece3.1273