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Rzeszow | Take Flight: Making the Most of NASA’s Airborne Data

Heat it up!

High-Level Project Summary

There are many problems to be solved in the world, and there is also plenty of data from ongoing research. Appropriate of them brings valuable information. We want to use them to educate what changes are taking place in the climate and how it affects our daily lives. Satellite, aerial and field data allow us to monitor the condition of agricultural land and predict what conditions will be for future crops. As part of our solution, we offer agricultural support to indicate which areas may be mismanaged or badly cultivated. We have also prepared a statistical analysis of how changes in climatic conditions affect local areas.

Link to Final Project

https://www.figma.com/proto/CQkeDm2Px6KFoxYBoubpsh/demo2?page-id=0%3A1&node-id=0%3A1&viewport=1249%2C911%2C0.55&scaling=scale-down

Link to Project "Demo"

https://supercluster.cfolks.pl/wp-content/uploads/2022/10/HeatItUp.pdf

Detailed Project Description

The project provides an analysis of the impact of climate change on the environment, changing water levels, agriculture and food prices. Field data such as soil organic matter content, carbon density, nitrogen density and phosphorus density were used. They came from the Delta X project, carried out by NASA. Since these are field data, they are not carried out with great frequency. We used publicly available satellite data, which allow us to cover large areas and provide additional information on the condition of the soil and the crops grown. For this purpose, satellite imagery from the Sentinel-2 satellite collection was used thanks to the Creodias EO Finder service. 


Indicators such as the Normalized Difference Vegetation Index and the Normalized Difference Moisture Index were calculated as part of the Agricultural Land Condition Monitoring System project. Maps were generated with an illustration of the summed agricultural area condition index. This index for 2021 takes into account the data collected during the Delta X program. Workflows and tools were developed to automate the processes necessary to generate the aforementioned maps. They allow precise identification of the needs of individual plots for additional fertilization and irrigation. The maps generated and presented illustrate visible differences in the quality and condition of crops on specific fields thanks to the high spatial resolution of satellite imagery from the Sentinel constellation (10mx10m). The use of such a system makes it possible to optimize the use of fertilizers and water thus reducing costs and negative environmental impact.


In addition to the study for 2021, soil condition analyses were made for farmland in 2017 and 2019, allowing us to observe how conditions are changing on farmland in the state of Louisiana in the Mississipi River Delta. In parallel with the processing of satellite data, statistical analysis was performed for agriculture-related indicators. 


In addition, they were compared with statistical data from the fields of agriculture and climate change. We want to show in a simple way that optimizing agriculture is important. We are most interested in convincing less developed regions, where precision agriculture is not yet common, of these concepts. Through education, we encourage them to take an interest in the calculated indicators. 

Space Agency Data

A dataset from the 5-year Delta-X project was used. These were field surveys at six study sites in the Atchafalaya and Mississippi River basins.

Soil properties studied and used in the project include:







  • organic matter content,
  • total carbon density,
  • nitrogen density,
  • Phosphorus density.


These are the parameters we used to complement with satellite Sentinel 2 data for the purpose of agriculture planning and monitoring system. To make the visualization and use of the data possible, we used Inverse Distance Weighting Interpolation. These data were used to determine NDMI and NDVI indicators.


NDMI (Normalized Difference Moisture Index) - is used to detect moisture levels in vegetation using a combination of near-infrared (NIR) and short-wave infrared (SWIR) spectral bands.


NDVI (Normalized Difference Vegetation Index) - is used to quantify vegetation by measuring the difference between near-infrared (which vegetation strongly reflects) and red light (which vegetation absorbs).


The data mentioned above let us to assess the quality of crops in the area of our interest. Combining the data with these gathered throughout Delta-X program gives us more information on how to act, irrigate and fertilize the fields that really need help, but not those that are doing great anyway.


Schematic of data and how it was used.


On top of that we've used NASA Climate data (https://climate.nasa.gov/) and United States Department of Agriculture data (https://quickstats.nass.usda.gov/) to corelate climate changes with our results.

Hackathon Journey

As young people, we are concerned about the state of the environment, so we decided to choose a challenge that involves educating about the impact of climate change on everyday life. During the challenge, our team learned even more about the ongoing climate change caused by mismanagement in agriculture. We created an app that shows the direct impact of climate change in an accessible way, such as the effect of temperature on the price of everyday products and increased resource consumption. In addition, we have gone off the charts, allowing the processed data to be used directly in agriculture. Our indicators will enable you to assess the state of your crops and see that you can increase them and reduce costs by optimizing them. We also focused on the link between agriculture and climate because agriculture is very underdeveloped in our region. We hope that our application will demonstrate the possibilities associated with using available satellite, aerial, and meteo data.


The work was divided into three teams. The first dealt with analyzing and processing satellite data, the second analyzed possible scientific studies on climate change, agriculture, and possible actions, while the third dealt with developing a model of the correlation between climate change and daily life, analyzing product prices, among other things.

One of the most difficult tasks was finding and interpreting statistical data. They varied considerably in availability between periods, plus no one on the team had prior experience working with such data. Another problem was that the quality of data from the DeltaX project also varied over the duration of the measurements.


The presence of mentors was very helpful. The guidance given by specialists in both science and business helped us prepare the best possible product, for which we would like to thank them sincerely.

References

RESOURCES:





Soil Conditions Indexes:





Temperature data:





Fertilization data:





Agricultural data





Publications





GRAPHICS:





SOFTWARE:





  • QGIS 3.26.1 ‘Buenos Aires’ Open Source,
  • PyCharm (Python IDE),
  • PyQGIS (Python plugins development kit).
  • Excel
  • Matlab

Tags

airborne, EO, satellite, agriculture, crops, climate change

Take Flight: Making the Most of NASA’s Airborne Data

NASA’s airborne campaigns collect high-resolution data to solve specific scientific problems, but these data can be used to study additional topics, especially when combined with other types of data. Your challenge is to create an app that will utilize the data from one of five selected airborne campaigns to educate the public about a problem associated with Earth’s changing climate.