Wie viele Forscher haben schon einmal schmerzlich feststellen müssen, dass es gar nicht so einfach ist, gute Messdaten zu erhalten. Häufig werden Sensoren im Feld installiert, dann – nach einiger Zeit – kommt man zurück und hofft auf genaue Aufzeichnungen. Anstelle dessen erlebt man unliebsame Überraschungen: gelöste Sensorverbindungen, angenagte Sensorkabel oder eine ungenügende Menge an aufgezeichneten Daten. Setzen Sie deshalb von Anfang an auf Qualitätssensoren, planen Sie im Voraus, überprüfen Sie die Messungen, beschäftigen Sie sich mit den Standortbedingungen, schützen Sie Ihre Sensoren und vieles mehr.
Der folgende englischsprachige Artikel macht Sie mit allen Eventualitäten vertraut und beschreibt ganz konkret, worauf Sie achten müssen, um eine erfolgreiche Feldmessung durchzuführen.
Schwerpunkte dieses Artikels:
- Dateninterpretation uvm.
Laden Sie sich außerdem unser Arbeitsblatt herunter und machen Sie sich damit Ihre Arbeit nochmal leichter.
Every researcher’s goal is to obtain usable field data for the entire duration of a study. A good data set is one a scientist can use to draw conclusions or learn something about the behavior of environmental factors in a particular application. However, as many researchers have painfully discovered, getting good data is not as simple as installing sensors, leaving them in the field, and returning to find an accurate record. Those who don’t plan ahead, check the data often, and troubleshoot regularly often come back to find unpleasant surprises such as unplugged data logger cables, sensor cables damaged by rodents, or worse: that they don’t have enough data to interpret their results. Fortunately, most data collection mishaps are avoidable with quality equipment, some careful forethought, and a small amount of preparation.
Make no mistake, it will cost you
Below are some common mistakes people make when designing a study that cost them time and money and may prevent their data from being usable.
- Site characterization: Not enough is known about the site, its variability, or other influential environmental factors that guide data interpretation
- Sensor location: Sensors are installed in a location that doesn’t address the goals of the study (i.e., in soils, both the geographic location of the sensors and the location in the soil profile must be applicable to the research question)
- Sensor installation: Sensors are not installed correctly, causing inaccurate readings
- Data collection: Sensors and logger are not protected, and data are not checked regularly to maintain a continuous and accurate data record
- Data dissemination: Data cannot be understood or replicated by other scientists
When designing a study, use the following best practices to simplify data collection and avoid oversights that keep data from being usable and ultimately, publishable.
Pre-installation prep saves time and money
Setting up sensors in the lab before going to the field helps a researcher understand how their sensors work. For instance, scientists can take soil sensor readings in different soil types, which will give them a solid understanding of what soil moisture values to expect in different scenarios. Figuring out the sensors before going to the field helps researchers understand correct installation, how long an installation might take, and it allows them to diagnose problems, such as a sensor that might be reading incorrectly. During this time, they can work out what tools and equipment they might need for the installation. Having a dedicated installation toolbox filled with important tools such as zip ties, pliers, markers, flashlights, and batteries can save hours of trips back and forth to the site.
If a researcher is using a data logger that needs programming, they should learn the programming language two weeks in advance to ensure they understand how to write programs for the logger. Even a plug-and-play, cellular data logger such as the EM60G will need pre-installation prep work, such as making sure the research site is in range of a cell tower.
Planning is paramount
Researchers should make a site plan with a map and remember that an installation usually takes twice as long as they think it will. Having a site plan significantly reduces human error, especially when pressed for time. When arriving at the research site, scientists can install according to the plan and record adjustments to the map as they go. This step saves significant time in the future if they, or other colleagues, have to find and dig up a problematic sensor. Having a backup plan for things that might go wrong is also important. For instance, what if a soil is too rocky at a certain depth? Or what will happen if a humidity sensor can’t be installed at 2 meters? Researchers need to think about what to do if their original plan is not going to work because often, they won’t be able to return to the site for weeks or months.
Site selection can make or break a study
Before selecting a site, scientists should clearly define their goals for gathering data. They need to know what they’re going to do with the data, so the data can answer the correct questions. Once goals are understood, then a researcher can begin to understand where to put their sensors.
The most influential issue a researcher will face in determining where to put their sensors is variability. For instance, scientists studying the soil will need to understand variability factors such as slope, aspect, vegetation type, depth, soil type, and soil density. If they are studying a canopy, they will need to understand the heterogeneity of the plant cover and deploy accordingly. If a researcher is comparing data, he/she will need to be consistent with sensor placement. This means that above-ground heights or below-ground depths should be consistent site-to-site. There’s no way to monitor every source of variability, so researchers should monitor the sources that are the most important. For a more in-depth look at variability, read “Soil moisture sensors: How many do you need?”
Site selection should also be practical. Researchers will need to look at the data as often as possible (we recommend at least once a month) to be sure everything is working correctly, thus the data logger needs to be accessible. Cellular data loggers make accessing data much easier, especially at remote sites. Uploading data to the cloud means scientists can access, share, and troubleshoot data every day from the comfort of their office.
Also, when choosing a data logger location, try to avoid long wire runs which can cause voltage potential gradients if lightning strikes. Choose a location where the sensors will be easy to plug in, and zip tie extra cable to the post for strain relief so the cables won’t get pulled out of the logger. Unplugged sensors or broken connections can be catastrophic to a study.
More metadata—more insight
The more metadata researchers record at a research site, the better they will understand their data, and the more time they will save in the long run. Some data loggers such as the EM60G automatically record important metadata, such as GPS location, barometric pressure, and sensor serial number. In addition, ancillary measurements such as soil temperature or microclimate monitoring, can be another source of metadata. An all-in-one weather station such as the ATMOS 41 automatically records weather events and can be an important way to benchmark or ground truth soil moisture or other data.
To document site information not automatically recorded by field instrumentation, many scientists find it practical to create a shared site-characterization worksheet that they can use to inform additional colleagues working at the site. Metadata that will be critical to future data insight and publication are: soil type, soil density, types of cover, measurement interval, raw data and type of calibration used, notes about an irrigation system (if present), which sensors are installed at which depth, notes on why the site was picked, events that might affect your data gathering such as a harvest, or any other information which may be hard to recall when analyzing the data. This information will be important when it’s time to publish, and putting it in a shared, cloud-based location will save headaches.