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Open AccessArticle

Deriving Field Scale Soil Moisture from Satellite Observations and Ground Measurements in a Hilly Agricultural Region

1
Department of Geodesy and Geoinformation, TU Wien, 1040 Vienna, Austria
2
Institute of Photogrammetry and Remote Sensing, Technische Universität Dresden, 01069 Dresden, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(22), 2596; https://doi.org/10.3390/rs11222596
Received: 3 September 2019 / Revised: 31 October 2019 / Accepted: 4 November 2019 / Published: 6 November 2019
(This article belongs to the Special Issue Remote Sensing of Regional Soil Moisture)
Agricultural and hydrological applications could greatly benefit from soil moisture (SM) information at sub-field resolution and (sub-) daily revisit time. However, current operational satellite missions provide soil moisture information at either lower spatial or temporal resolution. Here, we downscale coarse resolution (25–36 km) satellite SM products with quasi-daily resolution to the field scale (30 m) using the random forest (RF) machine learning algorithm. RF models are trained with remotely sensed SM and ancillary variables on soil texture, topography, and vegetation cover against SM measured in the field. The approach is developed and tested in an agricultural catchment equipped with a high-density network of low-cost SM sensors. Our results show a strong consistency between the downscaled and observed SM spatio-temporal patterns. We found that topography has higher predictive power for downscaling than soil texture, due to the hilly landscape of the study area. Furthermore, including a proxy of vegetation cover results in considerable improvements of the performance. Increasing the training set size leads to significant gain in the model skill and expanding the training set is likely to further enhance the accuracy. When only limited in-situ measurements are available as training data, increasing the number of sensor locations should be favored over expanding the duration of the measurements for improved downscaling performance. In this regard, we show the potential of low-cost sensors as a practical and cost-effective solution for gathering the necessary observations. Overall, our findings highlight the suitability of using ground measurements in conjunction with machine learning to derive high spatially resolved SM maps from coarse-scale satellite products. View Full-Text
Keywords: soil moisture; downscaling; advanced scatterometer (ASCAT); soil moisture active passive (SMAP); random forest; low-cost sensor soil moisture; downscaling; advanced scatterometer (ASCAT); soil moisture active passive (SMAP); random forest; low-cost sensor
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MDPI and ACS Style

Zappa, L.; Forkel, M.; Xaver, A.; Dorigo, W. Deriving Field Scale Soil Moisture from Satellite Observations and Ground Measurements in a Hilly Agricultural Region. Remote Sens. 2019, 11, 2596.

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