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. 2019 Nov 5:2:37.
doi: 10.3389/fdata.2019.00037. eCollection 2019.

A New Optical Remote Sensing Technique for High-Resolution Mapping of Soil Moisture

Ebrahim Babaeian  1 , Paheding Sidike  2 , Maria S Newcomb  3 , Maitiniyazi Maimaitijiang  4 , Scott A White  1 , Jeffrey Demieville  3 , Richard W Ward  3 , Morteza Sadeghi  5 , David S LeBauer  6 , Scott B Jones  7 , Vasit Sagan  4 , Markus Tuller  1
Affiliations

A New Optical Remote Sensing Technique for High-Resolution Mapping of Soil Moisture

Ebrahim Babaeian et al. Front Big Data. .

Abstract

The recently developed OPtical TRApezoid Model (OPTRAM) has been successfully applied for watershed scale soil moisture (SM) estimation based on remotely sensed shortwave infrared (SWIR) transformed reflectance (TRSWIR) and the normalized difference vegetation index (NDVI). This study is aimed at the evaluation of OPTRAM for field scale precision agriculture applications using ultrahigh spatial resolution optical observations obtained with one of the world's largest field robotic phenotyping scanners located in Maricopa, Arizona. We replaced NDVI with the soil adjusted vegetation index (SAVI), which has been shown to be more accurate for cropped agricultural fields that transition from bare soil to dense vegetation cover. The OPTRAM was parameterized based on the trapezoidal geometry of the pixel distribution within the TRSWIR-SAVI space, from which wet- and dry-edge parameters were determined. The accuracy of the resultant SM estimates is evaluated based on a comparison with ground reference measurements obtained with Time Domain Reflectometry (TDR) sensors deployed to monitor surface, near-surface and root zone SM. The obtained results indicate an SM estimation error between 0.045 and 0.057 cm3 cm-3 for the near-surface and root zone, respectively. The high resolution SM maps clearly capture the spatial SM variability at the sensor locations. These findings and the presented framework can be applied in conjunction with Unmanned Aerial System (UAS) observations to assist with farm scale precision irrigation management to improve water use efficiency of cropping systems and conserve water in water-limited regions of the world.

Keywords: OPTRAM; TERRA-REF; high-resolution; precision irrigation; remote sensing; soil moisture.

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Figures

Figure 1
Figure 1
Conceptual sketch of the TRSWIR-SAVI feature space.
Figure 2
Figure 2
Aerial view of the TERRA-REF field with marked locations (black squares) of the TDR sensor nests and the East-West swats (yellow rectangles) captured by the Vis-NIR and SWIR cameras (a), the TERRA-REF steel gantry with suspended instrument box (b), close up of the instrument box with various imaging systems and sensors (c), and the True TDR-315 sensors installed in duplicate in 2, 10, and 50 cm depths at the three sensor nest locations (d).
Figure 3
Figure 3
Reference panels exhibiting various spectral reflectances used for radiometric calibration of Vis-NIR and SWIR observations.
Figure 4
Figure 4
Pixel distribution within the integrated TRSWIR-SAVI trapezoidal space for the 2018 sorghum experiment. The blue and red solid lines represent the manually fitted wet- and dry-edges, respectively. The black, green, red, yellow, and blue point clouds correspond to the observations from Sept. 15, Sept. 28, Oct. 9, Oct. 18, and Oct. 28, respectively.
Figure 5
Figure 5
OPTRAM SM estimates compared with the reference TDR measurements at the three sensor nest locations.
Figure 6
Figure 6
An example for the applicability of the OPTRAM to estimate moisture variations when parameterized with ultrahigh resolution Vis-NIR and SWIR observations. Scenarios for wet and dry conditions are shown.
See this image and copyright information in PMC

References

    1. Babaeian E., Sadeghi M., Franz T. E., Jones S., Tuller M. (2018). Mapping soil moisture with the OPtical TRApezoid Model (OPTRAM) based on long-term MODIS observations. Remote Sens. Environ. 211, 425–440. 10.1016/j.rse.2018.04.029 - DOI
    1. Babaeian E., Sadeghi M., Jones S. B., Montzka C., Vereecken H., Tuller M. (2019). Ground, proximal and satellite remote sensing of soil moisture. Rev. Geophys. 57, 530–616. 10.1029/2018RG000618 - DOI
    1. Burnette M., Willis C., Kooper R., Maloney J. D., Ward R., Shakoor N., et al. . (2018). TERRA-REF data processing infrastructure, in Proceedings of the Practice and Experience on Advanced Research Computing (Pittsburgh, PA: ). 10.1145/3219104.3219152 - DOI
    1. Effati M., Bahrami H.-A., Gohardoust M. R., Babaeian E., Tuller M. (2019). Application of satellite remote sensing for estimation of dust emission probability in the Urmia Lake Basin in Iran. Soil Sci. Soc. Am. J. 83, 993–1002. 10.2136/sssaj2019.01.0018 - DOI
    1. Entekhabi D., Njoku E. G., O'Neill P. E., Kellogg K. H., Crow W. T., Edelstein W. N., et al. . (2010). The soil moisture active passive (SMAP) mission. Proc. IEEE. 98, 704–716. 10.1109/JPROC.2010.2043918 - DOI