Sourcing Available Data

Gather available data and make decisions on the need for new data. This involves standardising data collection methods from different sources. The data collection approaches will be used to satisfy data needs for for all use cases as well as the next phase of EiA. This stage also involves evaluation of available data for suitability for use. Where data is insufficient in terms of quantity and quality, recommendations will be made for collection of additional data.

Sources available

	Climatic/Meteorology	Source	Platform	Tools
1	Precipitation	GEE	GEE	daily_data_gee.R
2	Solar Net Radiation	GEE/NASA POWER	GEE/NASA POWER	nasapower_download.R
3	Temperature	GEE/NASA POWER	GEE/NASA POWER	nasapower_download.R
4	...	...	...	...
5	...	...	...	...
6	...	...	...	...

	Soil	Source	ID	Platform/Tools
1	N total	iSDA	log.n_tot_ncs_m_30m	isda_download.R
2	Bulk density	iSDA	db_od_m_30m	isda_download.R
3	Phosphorous extractable	iSDA	log.p_mehlich3_m_30m	isda_download.R
4	Bedrock depth	iSDA	bdr_m_30m	isda_download.R
5	Calcium extractable	iSDA	log.ca_mehlich3_m_30m	isda_download.R
6	Carbon organic	iSDA	log.oc_m_30m	isda_download.R
7	Carbon total	iSDA	log.c_tot_m_30m	isda_download.R
8	CEC	iSDA	log.ecec.f_m_30m	isda_download.R
9	Clay content	iSDA	sol_clay_tot_psa_m_30m	isda_download.R

10	N total	SoilGrids	nitrogen	soilgrids250_download.R
11	Bulk density	SoilGrids	bdod	soilgrids250_download.R
12	CEC	SoilGrids	cec	soilgrids250_download.R
13	Soil pH	SoilGrids	phh2o	soilgrids250_download.R
14	Clay content	SoilGrids	clay	soilgrids250_download.R
15	Sand	SoilGrids	sand	soilgrids250_download.R
16	Silt	SoilGrids	silt	soilgrids250_download.R
17	Soil organic carbon content	SoilGrids	soc	soilgrids250_download.R
18	...	SoilGrids	...	...

Depth can be either "0..20cm" or "20..50cm"

	Crop Yield	Source	Platform	Tools
1	?	GARDIAN	CG Labs
2	?	GARDIAN	CG Labs
3	?	GARDIAN	CG Labs
4	...	...	...	...

Examples

NASA POWER:

NASA POWER Provides solar and meteorological data sets from NASA research for support of renewable energy, building energy efficiency and supporting agricultural data needs. Data services are provided through a series of restful Application Programming Interfaces (API) distributing Analysis Ready Data to end users. Making use of the nasapower R package we can access a variety of data in several ways:

Using CG Labs data gathering tools and Fformat your selected NASA POWER data to the desired format (table; vector points; raster stack) using nasapower_json2output.R. Fr example: f_tblR.JSON("POWER_Regional_Daily_20210101_20210110_d2b00515.json", "t2m")
Using the nasaP function facilitating the use of nasapower R package, and alo obtaining data in the desired format (table; vector points; raster stack). For example: nasaP(tr = 0.08333, xmin = 36, ymin = -2, xmax = 39, ymax = 1, sdate = "2021-01-01", edate = "2021-01-10", "T2M", "T10M", "PS", "RH2M")

iSDA Africa Soil:

Most of agronomic decisions depend on available data on soil health or soil characteristics. We have found 2 main data providers: iSDA and SoilGrids

Provide soil characteristics and properties at two standard soil depths using isda_data function, which fetches data from the Cloud Optimized Geotiff (COG) of OpenLand.org sources. Use the ID of the parameter selected from the sources available. Example of total nitrogen at 0 - 20 cm for a region in Kenya. isda_data(par = "log.n_tot_ncs_m_30m", depth = "0..20cm", xmin = 37.0, ymin = -0.9, xmax = 37.2, ymax = -0.7)
Access SoilGrids (?)

Google Earth Engine Catalog

Google Earth Engine's public data catalog includes a variety of standard Earth science datasets. You can import these datasets into your script environment and start analyzing data using Google's computing resources. Results can then be exported and used on premises. Using the rgee R package we can interact with Google Earth Engine APIs and get access to a large variety of spatio-temporal datasets including: CHIRPS, Landsat, and many others. Using daily_data_gee.R and extract_daily_data_gee.R we can export results as a FeatureCollection in GeoJSON format. For example:

Access CHIRPS data for precipitation information between 2018 and 2019 in Malawi: daily.IC(imcol = "UCSB-CHG/CHIRPS/DAILY", band = "precipitation", sdate = "2018-01-01", edate = "2019-12-31", xmin = 34.8145177, ymin = -15.3265231, xmax = 35.3005743, ymax = -14.77034)
Extract that precipitation into an operable table with dates, geometries (coordinates) and the variable of interest (in his example, precipitation). zonalStats(prec, params, xmin = 34.8145177, ymin = -15.3265231, xmax = 35.3005743, ymax = -14.77034)
Export results: ee_table_to_gcs(x, description = "export weather data", bucket = 'your_GCS_bucket', fileNamePrefix = "points_x_", fileFormat = "GeoJSON")$start() ee_monitoring(eeTaskList = T) ...