Type of Submission
Poster
Proposal
The process of soil respiration results in the release of CO2 from the soil. A large body of research has found that variables such as soil moisture, plant cover and density, and soil quality are all strong predictors of soil respiration. At the same time remote sensing has been shown to make strong predictions of these variables based on surface reflectance. Currently there is a scale mismatch between soil respiration measurements and remote sensing techniques that estimate respiration rates from sub-meter points or 0.1 ha areas, respectively. In this study we set out to assess as to whether this scale discrepancy may be overcome by using examining as to whether imagery from a drone-based multi-spectral sensor is correlated with soil respiration. Further we assess whether there was a significant difference (a = 0.05) between adjacent plant communities. We found that soil respiration was statistically different (p = 0.002) between the lawn (= 4.33 µmole m-2 s-1) compared to the prairie (= 2.01 µmole m-2 s-1). Our correlation analysis revealed several strong relationships between soil CO2 respiration rates and the drone imagery. Specifically, we found r-values of -0.40 and -0.49 between soil CO2 efflux and Red 650 and Red 668 bands. We plan to continue to assess these data to offer guidance for future analysis into the agricultural applications of drone based imagery.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Can We Estimate Soil Respiration from the Sky?
The process of soil respiration results in the release of CO2 from the soil. A large body of research has found that variables such as soil moisture, plant cover and density, and soil quality are all strong predictors of soil respiration. At the same time remote sensing has been shown to make strong predictions of these variables based on surface reflectance. Currently there is a scale mismatch between soil respiration measurements and remote sensing techniques that estimate respiration rates from sub-meter points or 0.1 ha areas, respectively. In this study we set out to assess as to whether this scale discrepancy may be overcome by using examining as to whether imagery from a drone-based multi-spectral sensor is correlated with soil respiration. Further we assess whether there was a significant difference (a = 0.05) between adjacent plant communities. We found that soil respiration was statistically different (p = 0.002) between the lawn (= 4.33 µmole m-2 s-1) compared to the prairie (= 2.01 µmole m-2 s-1). Our correlation analysis revealed several strong relationships between soil CO2 respiration rates and the drone imagery. Specifically, we found r-values of -0.40 and -0.49 between soil CO2 efflux and Red 650 and Red 668 bands. We plan to continue to assess these data to offer guidance for future analysis into the agricultural applications of drone based imagery.
