At any one time, over two thousand human-made satellites are circling our planet. They help us communicate, locate, observe, and gather data. As agricultural scientists try to predict crop conditions in these climate-complex times, they are increasingly turning to “remote sensing” – gathering information from a distance. In particular, satellites have been used to measure soil moisture in order to predict and prepare for flood, droughts and other risks.
But hydrologists can be hesitant to accept remote sensing approaches, viewing them as complex and fickle, or offering data (such as rainfall estimates) that they feel are too easily impacted by external factors and usually not locally accurate enough to use effectively. They worry satellite data contains a range of biases that make it hard to accurately estimate moisture from space.
To overcome these barriers, researchers have developed a new process – an algorithm – that merges open data from a number of satellites. The researchers hope this process can add help policy-makers to help farmers.
Why measure soil moisture?
Accurately monitoring soil moisture has a huge range of applications. It can be the basis for weather prediction including rainfall estimation and flood forecasting. It can potentially predict droughts, landslides and soil erosion. If measurements find soil that is increasingly wet, a flood may be imminent. Alternatively, a soil that’s drying over time could mean drought conditions are on their way. And since soil moisture greatly impacts plant growth, particularly in dry climates, monitoring it can also be used to predict crop yields, loss or failure.
But that's not all. Seeking out effective, cost efficient ways to monitor soil moisture can help us slow the spread of plant and animal diseases – and sometimes even human diseases. As well, this data can help us better understand and manage soil’s role in climate change. So better understanding and monitoring soil moisture, and changes to it, could bring significant agricultural and safety benefits.
But how can we best measure moisture?
Some researchers advocate relying more on data collected from on-site ground observation stations. Such stations are accurate and easy to administer. Stations can be equipped with physical sensors, or researchers can even take soil samples and test their moisture levels. But these ground observation techniques are… well… grounded. They cover one particular area, limiting the amount of data that can be gathered and compared. They are useful at small scales, but expensive for larger geographic areas or over long time periods.
Soil moisture can also be estimated using a variety of remote sensing methods, in this case using satellites that generate openly available data. Satellites can measure radiation, which allows scientists to estimate moisture levels. Soil naturally gives off microwaves, and when the wetness of a soil changes, these signals change. Remote sensing can also track weather patterns, atmospheric conditions, wind speed and other factors that influence oncoming conditions.
But those same weather conditions can also lead to data errors from some types of remote sensing. As can the position of the satellite as the earth rotates. Even rough topography or differing soil cover, such as vegetation or snow, can impact readings. So what can be done to overcome these challenges?
Math to the rescue
The algorithm’s developers believe it may hold the key to overcoming data accuracy concerns by allowing researchers to compare soil moisture results from different satellites. More sources of data mean less likelihood of bias. This may allow us to narrow the gap between satellite and ground data, helping scientists, farmers and policy-makers make better decisions, faster.
The algorithm – which is essentially a calculation process that helps researchers integrate several sources of data into one final set of numbers – was initially developed by IRPI. But a team from the CGIAR Research Program on Water, Land and Ecosystems (WLE), led by the International Water Management Institute (IWMI), has been focusing on turning the calculations into real world results.
“We’re looking at the potential for this tool to be a cheaper and more accurate option for governments and other decision makers to predict climatic, land and agricultural events,” says Giriraj Amarnath, the WLE project lead. “We hope this can lead to better policies helping communities cope with floods, droughts and other dangers. Ultimately, it could help farmers reduce crop loss or failure.”
How can this be used?
Pulling together these drought and flood prediction and measurement tools greatly improves the geographical reach, speed (almost real-time) and efficiency of soil moisture monitoring. This can be part of a toolbox to support climate smart and resilient agriculture. In turn, it may mean a more food secure future and more reliable income for farmers.
If the algorithm can be widely applied, it may change the way surface water data is collected and used across the world. And it may help us better predict and plan for events that could seriously impact crops, livestock and human safety.
Working in collaboration with researchers from the Research Institute for Geo-Hydrological Protection, National Research Council, Italy, the WLE team developed the South Asia Drought Monitoring System (SADMS), using the algorithm and applying it to five satellite-derived daily rainfall estimates. The tests, conducted in India and Italy, found that the data merged together using the algorithm outperformed data from individual satellites. It also corresponded quite closely to data gathered at ground level. This success offers interesting new opportunities to more accurately and reliably predict drought conditions in many parts of the world.
WLE has already been working with the Indian Council of Agricultural Research (ICAR), using this approach, to provide drought monitoring data to state officials responsible for predicting and putting in place measures to prevent crop failures and losses. The research team are now exploring the potential to trial these same tools in Pakistan and in the USA.
The team is also considering providing the algorithm through Google Earth Engine (GEE), which would allow anyone with a basic computer and software to assess their local flood and drought risks. “This could give decision-makers – even farmers – around the world a freely accessible tool for flood and drought prediction,” says Amarnath. “It’s bringing science to those who might not have the resources to get the right data for decisions.”
Helping us prepare
If the challenges of remote sensing are overcome it has a lot to offer. Predicting the likelihood of drought or floods, they could not only help farmers, but also plan for migration and disaster response. This means satellite data can help decision-makers transition from mere crisis response to longer-term preparedness.
Satellites may orbit our planet unseen from earth, and may seem so far in the distance that we have trouble imagining how they can impact our daily choices. But this work may help bring remote data down to ground level – where it can help farmers, regions and countries avoid some of the worst risks of drought, flood and crop failure.
Thrive blog is a space for independent thought and aims to stimulate discussion among sustainable agriculture researchers and the public. Blogs are facilitated by the CGIAR Research Program on Water, Land and Ecosystems (WLE) but reflect the opinions and information of the authors only and not necessarily those of WLE and its donors or partners. WLE and partners are supported by CGIAR Fund Donors, including: ACIAR, DFID, DGIS, SDC, and others.