Which measurements can be taken to calculate the water balance in the nursery?

In the framework of the project work around the tree nursery in Schinznach, the water balance is to be measured and assessed. Therefore, certain values must be known, fortunately we already know some of the parameters of the water cycle. For example, the exchange with groundwater can be neglected, as only a certain quantity is taken and only something seeps away in the pond. Furthermore, there is no inflow in the form of a stream. The water for irrigation is taken from the groundwater and then preheated in the pond so that the plants do not get a shock. Therefore, it is known how much water comes into the system "tree nursery". What is not known is how much evaporates and how much remains in the canopy of leaves. According to that the following research is based on the question: Which remote sensing methods and indices are the easiest and most effective for measuring evapotranspiration?

NDVI (normalized difference vegetation index) is one of the most commonly used applications for UAS(Xue & Su, 2017). NDVI is easy to use as there are very light and high-resolution cameras UAS can be equipped with. It is defined as:

Because of the high reflectance of chlorophyll in the near infrared range this index is used to detect the plants greenness and vegetation cover in certain areas. One disadvantage of the NDVI is, that it doesn’t increase anymore above a certain biomass level, which is why more sensitive VI’s where developed like the EVI (enhanced vegetation index)(Xue & Su, 2017).

Another Index is the NDWI (normalized difference water index), it can measure the vegetation liquid water from space. It uses two narrow channels near 0.86 µm and 1.24 µm because these channels sense similar through vegetation canopy. NDWI is defined with the following formula:

 NDWI is a measure of liquid water molecules in the vegetation that interacted with solar radiation(Gao, 1996). Therefore it can be used to measure water stress and evaporation not only in the vegetation canopy but also in the soil in low depth(Nebo, Cesar & Richard, 2014). The disadvantage is that many UAS are not equipped with the necessary sensors and it can therefore not be used easily.

There are many more indices which build on the concept of NDVI and improve it in terms of error susceptibility and erroneous measurements, but they are all also a little more complicated and elaborate to process. Therefore, I recommend the NDVI as an index to measure the water balance in Schinznach. With the NDVI the vegetation cover can be measured relatively easily. If you now know which kind of species the vegetation consists of, you can find out how much this plant transpires and calculate the transpiration rate. Since NDVI is sensitive to the effects of soil brightness, soil colour, and leaf canopy shadow it needs to be calibrated with ground data(Glenn, Huete, Nagler & Nelson, 2008). To measure the evaporation, it would be useful to see if it is possible to get satellite data for the region using NDWI. Collecting our own data may turn out to be difficult because the necessary sensors may not be available.

Sighted Literature:

Gao, B. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58 (3), 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3

Glenn, E. P., Huete, A. R., Nagler, P. L. & Nelson, S. G. (2008). Relationship Between Remotely-sensed Vegetation Indices, Canopy Attributes and Plant Physiological Processes: What Vegetation Indices Can and Cannot Tell Us About the Landscape. Sensors (Basel, Switzerland), 8 (4), 2136–2160.

Nebo, J., Cesar, G. & Richard, B. (2014, April). Validation of remotely-sensed evapotranspiration and NDWI  using ground measurements at Riverlands, South Africa.

Xue, J. & Su, B. (2017). Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications. Journal of Sensors. https://doi.org/10.1155/2017/1353691

Potential of unmanned aircraft systems in precision agriculture

Unmanned aircraft Systems (UAS) are supposed to bring big advantages for the usage in farm-scaled applications. These applications are expected to help achieving a sustainable agriculture. The UAS provide the characteristics of data acquisition demanded by the farmers, for example small pixel size or coverage on demand even if there are clouds. Furthermore, it offers a quick delivery of information and that while being cheaper. In all these points satellite based systems or manned aircrafts, mostly used by governmental organisations, failed before.

UAS make precision agriculture achievable, because by being provided exact soil data, farmers are able to calculate accurately where and how much fertilizer is needed. Today, fertilizer is in fact applied in an amount suitable only for areas with the highest yield potential. UAS can also be used to measure crop growth by sending Near Infrared radiation (NIR) to the ground and detecting the reflected radiation, an advantage, although remote sensing is not essential. There are many other systems available, but the adoption of these technologies is slower than estimated and it’s depending a great deal on the farmer and the size of the farm. Only a small part of the farmers will be able to use UAS, particularly the ones already using variable rate technologies. And the question whether the technology will result in economic and environmental benefit is yet to be answered.

UAS can not only be used for measuring crop growth, but also for taking high resolution images, to detect spatial variability of water stress and to manage irrigation more effectively. Water stress can be measured with thermal sensors, which are still quite expensive. Methods calculating the amount of water used for irrigation include spectral vegetation indices and plant canopy temperature measurements. Another advantage of UAS are the smaller ground sample distances. Imagery taken form UAS close to the ground can be used to identify the soil between rows of crop, using object-based image analysis. Also, germination rates can be determined with pixel counting, using imagery with even smaller ground sample distances, between 0.5 and 2.5 cm. Furthermore, accurate crop height can be calculated by the difference between the digital surface and the actual ground elevation model delivered from orthomosaic images. 

A big problem of measuring soil and plant properties with spectral reflectance is the second-to-second variation of atmospheric transmittance. It can be solved with up-looking sensors to measure the variation in incident light levels directly. These measurements can be used to calibrate cameras and multispectral sensors instantly.

Remote sensing with UAS has three niches in precision agriculture: scouting for problems, monitoring to prevent yield losses and planning crop management operations. Using nutrient management as an example, ‘scouting’ checks to see whether a specific area has a nutrient deficiency, ‘monitoring’ systematically searches for areas that may need more nutrients, and ‘planning’ determines the economically optimal fertilization rates. But regarding the costs of processing data by professionals only the ‘scouting’ may be economically reasonable. UAS have a big potential to detect weed occurrence, disease outbreaks and insect infestations due to the small ground sample distance. However, the accuracy of identification is still not high and therefore the best treatment can’t easily be determined(Hunt & Daughtry, 2018). 

In summary it can be said that the potential of remote sensing with UAS in precision agriculture is high, but not yet in every aspect competitive. Nevertheless, with more automatized data analysis and advanced processing the advantages of UAS could outrun the negative aspects.

Sighted literature:

Hunt, E. R. J. & Daughtry, C. S. T. (2018). What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture? International Journal of Remote Sensing, 39 (15–16), 5345–5376. https://doi.org/10.1080/01431161.2017.1410300