# GROW Soil Moisture Maps

_Empowering growers with soil moisture data models_

Published: 2019-09-16

Live project: [→ Interactive Site](http://soilmoisturemaps.growobservatory.org)

The GROW Observatory is a European-wide project engaging thousands of growers, scientists and others passionate about the land. We developed the Dynamic Soil Moisture Map as a demonstration of an information service which could be used by farmers, scientists and policy makers for applications in agriculture and climate forecasting. These maps use GROW sensor data combined with satellite data to generate a continuous estimation of water content over a terrain.

## Key visuals

- GROW Soil Moisture Maps menu with a green Europe globe ringed by nine sensor-cluster sites and their sensor counts
- Tard, Hungary site view with a 3D soil moisture model over aerial imagery and moisture, light and temperature charts
- Close-up of the gridded blue 3D soil moisture surface floating over green aerial farmland
- Numbered help overlay explaining sensors, moisture model and time-series charts of the Soil Moisture Maps interface
- Mezőzombor, Hungary with sensor MIS_DK_D4E1 selected and its moisture line highlighted at 39.1% in the charts
- Detail of the moisture model with a selected sensor highlighted in yellow among moisture and light data columns
- Luxembourg site view with a 3D soil moisture model over aerial imagery and moisture, light and temperature charts
- Algarve, Portugal site view with a 3D soil moisture model over aerial imagery and moisture, light and temperature charts
- Vienna, Austria site view with a 3D soil moisture model over aerial imagery and moisture, light and temperature charts
- GROW Soil Moisture Maps intro screen with project description text over a faded Europe globe of sensor sites

## Screencast of the interface

## How it works

![GROW Soil Moisture Maps menu: green Europe globe ringed by nine sensor-cluster sites with sensor counts]()

Local farmers and growers can install Parrot Flower Power sensors on their fields. These sensors continously collect data on light, moisture and temperature conditions. 

![Algarve site view: 3D soil moisture model over aerial map with moisture, light and temperature time-series charts]()

Once the user has selected a site of the exemplary GROW sites featured in the visualization, the sensor locations are shown on the map at the bottom. The map can be rotated by dragging with the mouse.

![Mezőzombor site with sensor MIS_DK_D4E1 selected, its moisture line highlighted at 39.1% in the time-series charts]()

The time series charts on the right show the development of light, moisture and temperature readings. Click to select a sensor — it will be highlighted in the map and the graphs on the left.

![Close-up of the gridded 3D soil moisture model rising above the point sensor columns for moisture and light]()

Based on this raw information from the sensors, a continuous moisture model is calculated for the whole surrounding area. The blue section above the sensors shows continuous soil moisture values across the area of land. The vertical axis of the blue section represents the moisture level. The "gridded carpet" look of the model is in contrast to the spiky columns of the sensor data visualization — in visual correspondance to the underlying process of continuous extrapolation from point-wise measurements.

## Technical background

The web application was implemented using [react](https://reactjs.org/), [mobx](https://github.com/mobxjs/mobx), [d3](https://d3js.org/), [mapbox-gl](https://github.com/mapbox/mapbox-gl-js) and [three.js](https://threejs.org/). The moisture model information is stored in [geoTIFF](https://en.wikipedia.org/wiki/GeoTIFF) files, while are loaded at runtime and transformed into 3D geometry using three.js.

## Credits

The GROW Observatory [Soil Moisture Maps](https://web.archive.org/web/20191016194202/http://soilmoisturemaps.growobservatory.org/) have been developed by the [GROW Observatory](http://growobservatory.org/)

Lead partner: [FutureEverything](http://futureeverything.org/)

Data visualisation: Moritz Stefaner ([Truth & Beauty](http://truth-and-beauty.net/)) with [Yuri Vishnevsky](https://yuri.is/)

Algorithms: [IIASA](http://www.iiasa.ac.at/) and [University of Miskolc](http://www.uni-miskolc.hu/en)

Data provided by: GROW community

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[View on truth-and-beauty.net](https://truth-and-beauty.net/projects/grow)