The GROW Observatory recently launched soil moisture maps developed using sensor data collected.
If you’re reading this article and you are not a member of the GROW Observatory, or you don’t grow in one of the areas covered by our soil moisture maps — no problem. You can still benefit from growers who have been collecting and submitting data, as GROW is making these data available from all participants.
Soil is a very complex system with dynamic properties. Soil is alive and soil conditions change through space and time. The periodic dataset that underpins the soil moisture maps follows the weekly changes in soil moisture. This means that the homogenous areas where soil functions are similar can be identified. For example, zones with dry, average or moist periods provide a spectrum of growing conditions from risky to convenient.
Relief (the difference between the highest and lowest elevations) is influenced by soil formation, and the spatial distribution of the primary soil characteristics is guided by topography, This is reflected in soil moisture values. Low lying areas where soil is deeper and water filtrates from the nearby slopes, are areas with puddles, stagnating water and thus high soil moisture values. Slopes and plateaus dry up quicker due to water run-off and soil is typically shallower. These processes are driven by soil texture, which influences the soil’s capacity to hold or drain water.
The volumetric percentage of soil moisture characterising an area hints at the soil’s physical properties. For the same topography, high values, like 50–60 v/v% soil moisture suggest clay-based soils. These soils keep moisture for longer periods and are suitable for growing in dry years. Below 30–35 v/v% indicates sandy soils with little pore space, so sandy soils do not hold water well and are often freely draining. this means they may be expected to have lower soil moisture values, and may not stagnate in wet periods.
For more from GROW Observatory on soil moisture see here
Land cover and land use influence soil conditions. Using long term soil moisture data allows us to follow the effects of land management interventions, or different land uses on soil moisture. Land management mostly affects soil structure, especially how the mineral and organic components of the soil aggregate. The shape and size of soil aggregates influences soil porosity. Big cracks between soil aggregates drain water to the deep soil , and within soil aggregates there are small pores holding water and providing niche space for soil life to inhabit. Excessive tillage or over grazing compacts soil, and destroys the aggregation, resulting in an imbalanced water regime.
All soil moisture measurements provided by the GROW Citizens’ Observatory are gathered and processed. Flower Power sensors deployed by GROW participants provide soil moisture data for the upper 10 centimetre of soil. These measurements characterise only the immediate surroundings of the sensor, and the value is only valid and accurate for the exact location. These collected data are called point data. Point data can be interpolated with geostatistical methods, to generate continuous maps of the soil moisture conditions for a specific time.
Flower Power sensor measurements at the Hungarian pilot site for the date of 2018.12.21. (University of Miskolc)
GROW’s soil moisture maps generated from point data are the visual interpretation of data collected by participants and above all, the continuous extension of point data for the wider geographic area. Above is a map that provides estimates of soil moisture for areas where no measurements have been made. The quality of the estimated data depends on:
- the number and distribution of the sensors
- the representability of the sensor locations
- the quality and interpretability of associated explanatory variables
Examples of the environmental data for the Hungarian pilot site: elevation, slope, flow accumulation, soil map (University of Miskolc)
We call these maps ‘explanatory variables’ because they show the environmental conditions where sensors are deployed. Spatial variability of the soil characteristics, like soil moisture, is dependent upon the soil type, soil texture, human impact (land use), vegetation (land cover) and relief. Freely available data exist for the whole of Europe. GROW Observatory’s science team uses these data sources to estimate soil moisture for areas where no sensors are deployed.
For more from GROW Observatory on soil type and soil texture see here:
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The extrapolated soil moisture map for the Hungarian pilot site (University of Miskolc)
GROW Observatory aims to ground truth (validate) with the Flower Power sensor and to elaborate soil moisture raster maps (i.e. pixilated, un-manipulatable) based on the data generated by the citizens’ observatory. Ground truthing needs measurements from the surface layer of the soil as Sentinel-1’s radar only perceives soil moisture to 5–10 centimetres depth. The upper 10 cm is the most influenced, disturbed top layer of the soil. Every crack, root or even ant hill effects the measured soil moisture value. It is difficult to reliably describe the spatial variability of soil moisture with the currently available environmental data. So the created soil moisture maps are more generalised and do not have the same accuracy as the point measurements.
However the advantage of continuous raster data is that it shows the spatial variability of soil conditions and gives insights to growers who want to explore the periodic soil moisture maps provided by the GROW Observatory.
Periodic soil moisture maps provided by GROW Observatory
Although there are thousands of participants providing data to the GROW Citizen Observatory, the soil moisture maps still have limitations. The network needs more observations to make reliable soil moisture maps for more geographic areas. And this is where YOU can get involved! Join the GROW Citizen Observatory at: www.growobservatory.org to help us generate more accurate soil moisture maps. And check out here where you can find all the ways to get involved in the GROW Observatory, including via the Edible Plants Database.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 690199