Rotimi, Y.O.; DeMarco, J.; Crossley, P.; Chapman, T.; Breckheimer, I.K. Geospatial Modeling of Soil Moisture on a Restored Wet Meadow. Preprints2024, 2024010819. https://doi.org/10.20944/preprints202401.0819.v1
APA Style
Rotimi, Y.O., DeMarco, J., Crossley, P., Chapman, T., & Breckheimer, I.K. (2024). Geospatial Modeling of Soil Moisture on a Restored Wet Meadow. Preprints. https://doi.org/10.20944/preprints202401.0819.v1
Chicago/Turabian Style
Rotimi, Y.O., Teresa Chapman and Ian K. Breckheimer. 2024 "Geospatial Modeling of Soil Moisture on a Restored Wet Meadow" Preprints. https://doi.org/10.20944/preprints202401.0819.v1
Abstract
Remotely sensed data acquired by multispectral sensors can be used to monitor soil moisture (SM) across a larger land area than in situ monitoring alone. Although there have been wide-ranging applications of remote sensing tools in SM estimation on many ecosystems, there is a limited understanding of their accuracy in restored wetlands. The objective of this study was to examine the potential of remotely sensed data from Landsat-9, Sentinel-1A SAR, and Blackswift E2 Uncrewed Aircraft Systems (UAS) in predicting SM in a restored wetland complex in the Gunnison Basin of Colorado. We tested two response variables, gravimetric SM and volumetric SM, to determine which indicator of SM was better predicted with remotely sensed data. We also tested the accuracy of remotely sensed data in predicting SM at different soil depths. Overall, satellite and UAS indices predicted gravimetric SM better than volumetric SM. The Normalized Difference Red Edge (NDRE) index from Blackswift E2 UAS had the best prediction of GSM at the depth of 0 to 5 cm (R2 = 0.86, RMSE = 7.41). Results from the study provide information on the accuracy of remotely sensed data for SM monitoring on restored wetlands.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
