Introduction to the smapr package
Maxwell B. Joseph
2023-03-06
Source:vignettes/smapr-intro.Rmd
smapr-intro.RmdThis vignette outlines a basic use scenario for smapr. We will acquire and process NASA (Soil Moisture Active-Passive) SMAP data, and generate some simple visualizations.
SMAP data products
Multiple SMAP data products are provided by the NSIDC, and these products vary in the amount of processing. Currently, smapr primarily supports level 3 and level 4 data products, which represent global daily composite and global three hourly modeled data products, respectively. NSIDC provides documentation for all SMAP data products on their website, and we provide a summary of data products supported by smapr below.
| Dataset id | Description | Resolution |
|---|---|---|
| SPL2SMAP_S | SMAP/Sentinel-1 Radiometer/Radar Soil Moisture | 3 km |
| SPL3FTA | Radar Northern Hemisphere Daily Freeze/Thaw State | 3 km |
| SPL3SMA | Radar Global Daily Soil Moisture | 3 km |
| SPL3SMP | Radiometer Global Soil Moisture | 36 km |
| SPL3SMAP | Radar/Radiometer Global Soil Moisture | 9 km |
| SPL4SMAU | Surface/Rootzone Soil Moisture Analysis Update | 9 km |
| SPL4SMGP | Surface/Rootzone Soil Moisture Geophysical Data | 9 km |
| SPL4SMLM | Surface/Rootzone Soil Moisture Land Model Constants | 9 km |
| SPL4CMDL | Carbon Net Ecosystem Exchange | 9 km |
This vignette uses the level 4 SPL4SMAU (Surface/Rootzone Soil Moisture Analysis Update) data product.
Preparing to access SMAP data
NASA requires a username and password from their Earthdata portal to access SMAP data. You can get these credentials here: https://earthdata.nasa.gov/
Once you have your credentials, you can use the
set_smap_credentials function to set them for use by the
smapr package:
set_smap_credentials("myusername", "mypassword")This function saves your credentials for later use unless you use the
argument save = FALSE.
Finding data
To find out which SMAP data are available, we’ll use the
find_smap function, which takes a data set ID, date(s) to
search, and a dataset version.
available_data <- find_smap(id = 'SPL4SMAU', dates = '2018-06-01', version = 5)This returns a data frame, where every row is one data file that is available on NASA’s servers.
str(available_data)
#> 'data.frame': 8 obs. of 3 variables:
#> $ name: chr "SMAP_L4_SM_aup_20180601T030000_Vv5030_001" "SMAP_L4_SM_aup_20180601T060000_Vv5030_001" "SMAP_L4_SM_aup_20180601T090000_Vv5030_001" "SMAP_L4_SM_aup_20180601T120000_Vv5030_001" ...
#> $ date: Date, format: "2018-06-01" "2018-06-01" "2018-06-01" "2018-06-01" ...
#> $ dir : chr "SPL4SMAU.005/2018.06.01/" "SPL4SMAU.005/2018.06.01/" "SPL4SMAU.005/2018.06.01/" "SPL4SMAU.005/2018.06.01/" ...Downloading data
To download the data, we can use download_smap. Note
that this may take a while, depending on the number of files being
downloaded, and the speed of your internet connection. Because we’re
downloading multiple files, we will use the verbose = FALSE
argument to avoid printing excessive output to the console.
local_files <- download_smap(available_data, overwrite = FALSE, verbose = FALSE)Each file corresponds to different times as indicated by the file names:
local_files$name[1:2]
#> [1] "SMAP_L4_SM_aup_20180601T030000_Vv5030_001" "SMAP_L4_SM_aup_20180601T060000_Vv5030_001"Exploring data
Each file that we downloaded is an HDF5 file with multiple datasets
bundled together. To list all of the data in a file we can use
list_smap. By default, if we give list_smap a
data frame of local files, it will return a list of data frames. Because
all of these data files are of the same data product, using
list_smap on one file (e.g., the first) will tell us what’s
available in all of the files:
list_smap(local_files[1, ])
#> $SMAP_L4_SM_aup_20180601T030000_Vv5030_001
#> name group otype dclass dim
#> 1 y . H5I_DATASET H5T_FLOAT 1624
#> 2 Forecast_Data . H5I_GROUP <NA> <NA>
#> 3 sm_surface_forecast Forecast_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 4 tb_v_forecast Forecast_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 5 surface_temp_forecast Forecast_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 6 tb_v_forecast_ensstd Forecast_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 7 soil_temp_layer1_forecast Forecast_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 8 sm_profile_forecast Forecast_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 9 tb_h_forecast Forecast_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 10 sm_rootzone_forecast Forecast_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 11 tb_h_forecast_ensstd Forecast_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 12 time . H5I_DATASET H5T_FLOAT 1
#> 13 EASE2_global_projection . H5I_DATASET H5T_STRING 1
#> 14 Analysis_Data . H5I_GROUP <NA> <NA>
#> 15 sm_surface_analysis_ensstd Analysis_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 16 surface_temp_analysis_ensstd Analysis_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 17 sm_rootzone_analysis_ensstd Analysis_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 18 sm_surface_analysis Analysis_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 19 sm_profile_analysis_ensstd Analysis_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 20 sm_rootzone_analysis Analysis_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 21 sm_profile_analysis Analysis_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 22 soil_temp_layer1_analysis Analysis_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 23 soil_temp_layer1_analysis_ensstd Analysis_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 24 surface_temp_analysis Analysis_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 25 cell_lat . H5I_DATASET H5T_FLOAT 3856 x 1624
#> 26 cell_row . H5I_DATASET H5T_INTEGER 3856 x 1624
#> 27 cell_lon . H5I_DATASET H5T_FLOAT 3856 x 1624
#> 28 Metadata . H5I_GROUP <NA> <NA>
#> 29 Source Metadata H5I_GROUP <NA> <NA>
#> 30 L1C_TB Metadata/Source H5I_GROUP <NA> <NA>
#> 31 AcquisitionInformation Metadata H5I_GROUP <NA> <NA>
#> 32 platform Metadata/AcquisitionInformation H5I_GROUP <NA> <NA>
#> 33 platformDocument Metadata/AcquisitionInformation H5I_GROUP <NA> <NA>
#> 34 radarDocument Metadata/AcquisitionInformation H5I_GROUP <NA> <NA>
#> 35 radar Metadata/AcquisitionInformation H5I_GROUP <NA> <NA>
#> 36 radiometerDocument Metadata/AcquisitionInformation H5I_GROUP <NA> <NA>
#> 37 radiometer Metadata/AcquisitionInformation H5I_GROUP <NA> <NA>
#> 38 DataQuality Metadata H5I_GROUP <NA> <NA>
#> 39 TBH Metadata/DataQuality H5I_GROUP <NA> <NA>
#> 40 DomainConsistency Metadata/DataQuality/TBH H5I_GROUP <NA> <NA>
#> 41 CompletenessOmission Metadata/DataQuality/TBH H5I_GROUP <NA> <NA>
#> 42 TBV Metadata/DataQuality H5I_GROUP <NA> <NA>
#> 43 DomainConsistency Metadata/DataQuality/TBV H5I_GROUP <NA> <NA>
#> 44 CompletenessOmission Metadata/DataQuality/TBV H5I_GROUP <NA> <NA>
#> 45 SeriesIdentification Metadata H5I_GROUP <NA> <NA>
#> 46 DatasetIdentification Metadata H5I_GROUP <NA> <NA>
#> 47 Extent Metadata H5I_GROUP <NA> <NA>
#> 48 CRID Metadata H5I_GROUP <NA> <NA>
#> 49 AUP Metadata/CRID H5I_GROUP <NA> <NA>
#> 50 Root Metadata/CRID H5I_GROUP <NA> <NA>
#> 51 Config Metadata H5I_GROUP <NA> <NA>
#> 52 GridSpatialRepresentation Metadata H5I_GROUP <NA> <NA>
#> 53 Latitude Metadata/GridSpatialRepresentation H5I_GROUP <NA> <NA>
#> 54 Longitude Metadata/GridSpatialRepresentation H5I_GROUP <NA> <NA>
#> 55 ProcessStep Metadata H5I_GROUP <NA> <NA>
#> 56 cell_column . H5I_DATASET H5T_INTEGER 3856 x 1624
#> 57 Observations_Data . H5I_GROUP <NA> <NA>
#> 58 tb_v_obs Observations_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 59 tb_v_obs_time_sec Observations_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 60 tb_h_obs Observations_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 61 tb_h_orbit_flag Observations_Data H5I_DATASET H5T_INTEGER 3856 x 1624
#> 62 tb_h_resolution_flag Observations_Data H5I_DATASET H5T_INTEGER 3856 x 1624
#> 63 tb_h_obs_errstd Observations_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 64 tb_v_orbit_flag Observations_Data H5I_DATASET H5T_INTEGER 3856 x 1624
#> 65 tb_v_resolution_flag Observations_Data H5I_DATASET H5T_INTEGER 3856 x 1624
#> 66 tb_v_obs_assim Observations_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 67 tb_h_obs_assim Observations_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 68 tb_h_obs_time_sec Observations_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 69 tb_v_obs_errstd Observations_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 70 x . H5I_DATASET H5T_FLOAT 3856To dig deeper, we can use the all argument to
list_smap:
list_smap(local_files[1, ], all = TRUE)
#> $SMAP_L4_SM_aup_20180601T030000_Vv5030_001
#> name group otype dclass dim
#> 1 y . H5I_DATASET H5T_FLOAT 1624
#> 2 Forecast_Data . H5I_GROUP <NA> <NA>
#> 3 sm_surface_forecast Forecast_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 4 tb_v_forecast Forecast_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 5 surface_temp_forecast Forecast_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 6 tb_v_forecast_ensstd Forecast_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 7 soil_temp_layer1_forecast Forecast_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 8 sm_profile_forecast Forecast_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 9 tb_h_forecast Forecast_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 10 sm_rootzone_forecast Forecast_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 11 tb_h_forecast_ensstd Forecast_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 12 time . H5I_DATASET H5T_FLOAT 1
#> 13 EASE2_global_projection . H5I_DATASET H5T_STRING 1
#> 14 Analysis_Data . H5I_GROUP <NA> <NA>
#> 15 sm_surface_analysis_ensstd Analysis_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 16 surface_temp_analysis_ensstd Analysis_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 17 sm_rootzone_analysis_ensstd Analysis_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 18 sm_surface_analysis Analysis_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 19 sm_profile_analysis_ensstd Analysis_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 20 sm_rootzone_analysis Analysis_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 21 sm_profile_analysis Analysis_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 22 soil_temp_layer1_analysis Analysis_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 23 soil_temp_layer1_analysis_ensstd Analysis_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 24 surface_temp_analysis Analysis_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 25 cell_lat . H5I_DATASET H5T_FLOAT 3856 x 1624
#> 26 cell_row . H5I_DATASET H5T_INTEGER 3856 x 1624
#> 27 cell_lon . H5I_DATASET H5T_FLOAT 3856 x 1624
#> 28 Metadata . H5I_GROUP <NA> <NA>
#> 29 Source Metadata H5I_GROUP <NA> <NA>
#> 30 L1C_TB Metadata/Source H5I_GROUP <NA> <NA>
#> 31 AcquisitionInformation Metadata H5I_GROUP <NA> <NA>
#> 32 platform Metadata/AcquisitionInformation H5I_GROUP <NA> <NA>
#> 33 platformDocument Metadata/AcquisitionInformation H5I_GROUP <NA> <NA>
#> 34 radarDocument Metadata/AcquisitionInformation H5I_GROUP <NA> <NA>
#> 35 radar Metadata/AcquisitionInformation H5I_GROUP <NA> <NA>
#> 36 radiometerDocument Metadata/AcquisitionInformation H5I_GROUP <NA> <NA>
#> 37 radiometer Metadata/AcquisitionInformation H5I_GROUP <NA> <NA>
#> 38 DataQuality Metadata H5I_GROUP <NA> <NA>
#> 39 TBH Metadata/DataQuality H5I_GROUP <NA> <NA>
#> 40 DomainConsistency Metadata/DataQuality/TBH H5I_GROUP <NA> <NA>
#> 41 CompletenessOmission Metadata/DataQuality/TBH H5I_GROUP <NA> <NA>
#> 42 TBV Metadata/DataQuality H5I_GROUP <NA> <NA>
#> 43 DomainConsistency Metadata/DataQuality/TBV H5I_GROUP <NA> <NA>
#> 44 CompletenessOmission Metadata/DataQuality/TBV H5I_GROUP <NA> <NA>
#> 45 SeriesIdentification Metadata H5I_GROUP <NA> <NA>
#> 46 DatasetIdentification Metadata H5I_GROUP <NA> <NA>
#> 47 Extent Metadata H5I_GROUP <NA> <NA>
#> 48 CRID Metadata H5I_GROUP <NA> <NA>
#> 49 AUP Metadata/CRID H5I_GROUP <NA> <NA>
#> 50 Root Metadata/CRID H5I_GROUP <NA> <NA>
#> 51 Config Metadata H5I_GROUP <NA> <NA>
#> 52 GridSpatialRepresentation Metadata H5I_GROUP <NA> <NA>
#> 53 Latitude Metadata/GridSpatialRepresentation H5I_GROUP <NA> <NA>
#> 54 Longitude Metadata/GridSpatialRepresentation H5I_GROUP <NA> <NA>
#> 55 ProcessStep Metadata H5I_GROUP <NA> <NA>
#> 56 cell_column . H5I_DATASET H5T_INTEGER 3856 x 1624
#> 57 Observations_Data . H5I_GROUP <NA> <NA>
#> 58 tb_v_obs Observations_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 59 tb_v_obs_time_sec Observations_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 60 tb_h_obs Observations_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 61 tb_h_orbit_flag Observations_Data H5I_DATASET H5T_INTEGER 3856 x 1624
#> 62 tb_h_resolution_flag Observations_Data H5I_DATASET H5T_INTEGER 3856 x 1624
#> 63 tb_h_obs_errstd Observations_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 64 tb_v_orbit_flag Observations_Data H5I_DATASET H5T_INTEGER 3856 x 1624
#> 65 tb_v_resolution_flag Observations_Data H5I_DATASET H5T_INTEGER 3856 x 1624
#> 66 tb_v_obs_assim Observations_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 67 tb_h_obs_assim Observations_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 68 tb_h_obs_time_sec Observations_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 69 tb_v_obs_errstd Observations_Data H5I_DATASET H5T_FLOAT 3856 x 1624
#> 70 x . H5I_DATASET H5T_FLOAT 3856Looking at this output, we can conclude that the file contains
multiple arrays (notice the dim column). These arrays
correspond to things like estimated root zone soil moisture
(/Analysis_Data/sm_rootzone_analysis), estimated surface
soil moisture (/Analysis_Data/sm_surface_analysis), and
estimated surface temperature
(/Analysis_Data/surface_temp_analysis). See https://nsidc.org/data/smap/spl4sm/data-fields#sm_surface_analysis
for more detailed information on what these datasets represent and how
they were generated.
Extracting data
The datasets that we are interested in are spatial grids. The
smapr package can extract these data into
raster objects with the extract_smap function,
which takes a dataset name as an argument. These names are paths that
can be generated from the output of list_smap. For example,
if we want to get rootzone soil moisture, we can see a dataset with name
sm_rootzone_analysis in group /Analysis_Data,
so that the path to the dataset is
/Analysis_Data/sm_rootzone_analysis:
sm_raster <- extract_smap(local_files, '/Analysis_Data/sm_rootzone_analysis')This will extract all of the data in the data frame
local_files, generating a terra SpatRaster object with one
layer per file:
sm_raster
#> class : SpatRaster
#> dimensions : 1624, 3856, 8 (nrow, ncol, nlyr)
#> resolution : 8984.982, 8205.308 (x, y)
#> extent : -17367530, 17278561, -6010879, 7314541 (xmin, xmax, ymin, ymax)
#> coord. ref. : +proj=cea +lat_ts=30 +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs
#> source : tmp.tif
#> names : SMAP_~0_001, SMAP_~0_001, SMAP_~0_001, SMAP_~0_001, SMAP_~0_001, SMAP_~0_001, ...
#> min values : 0.0065597, 0.00659073, 0.006612024, 0.006668247, 0.006737789, 0.006590234, ...
#> max values : 0.8000000, 0.80000001, 0.800000012, 0.800000012, 0.800000012, 0.800000012, ...We can visualize each layer:
plot(sm_raster)
Cropping, masking, and summarization can then proceed using the terra R package.
For example, to get mean soil moisture values across layers,
useterra::app():
Comparing surface and soil moisture
Our SPL4SMAU data have estimated surface and rootzone soil moisture layers. If we want to compare these values, we can load the surface soil moisture data, compute the mean value over layers as we did for the rootzone soil moisture raster, and generate a scatterplot.
surface_raster <- extract_smap(local_files,
name = '/Analysis_Data/sm_surface_analysis')
mean_surface_sm <- app(surface_raster, fun = mean)
# compare values
plot(values(mean_sm), values(mean_surface_sm), col = 'dodgerblue', cex = .1,
xlab = 'Rootzone soil moisture', ylab = 'Surface soil moisture', bty = 'n')
abline(0, 1, lty = 2)