کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4525224 | 1625613 | 2016 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Using machine learning to produce near surface soil moisture estimates from deeper in situ records at U.S. Climate Reference Network (USCRN) locations: Analysis and applications to AMSR-E satellite validation
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موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
فرآیندهای سطح زمین
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Surface soil moisture is a critical parameter for understanding the energy flux at the land atmosphere boundary. Weather modeling, climate prediction, and remote sensing validation are some of the applications for surface soil moisture information. The most common in situ measurement for these purposes are sensors that are installed at depths of approximately 5Â cm. There are however, sensor technologies and network designs that do not provide an estimate at this depth. If soil moisture estimates at deeper depths could be extrapolated to the near surface, in situ networks providing estimates at other depths would see their values enhanced. Soil moisture sensors from the U.S. Climate Reference Network (USCRN) were used to generate models of 5Â cm soil moisture, with 10Â cm soil moisture measurements and antecedent precipitation as inputs, via machine learning techniques. Validation was conducted with the available, in situ, 5Â cm resources. It was shown that a 5Â cm estimate, which was extrapolated from a 10Â cm sensor and antecedent local precipitation, produced a root-mean-squared-error (RMSE) of 0.0215Â m3/m3. Next, these machine-learning-generated 5Â cm estimates were also compared to AMSR-E estimates at these locations. These results were then compared with the performance of the actual in situ readings against the AMSR-E data. The machine learning estimates at 5Â cm produced an RMSE of approximately 0.03Â m3/m3 when an optimized gain and offset were applied. This is necessary considering the performance of AMSR-E in locations characterized by high vegetation water contents, which are present across North Carolina. Lastly, the application of this extrapolation technique is applied to the ECONet in North Carolina, which provides a 10Â cm depth measurement as its shallowest soil moisture estimate. A raw RMSE of 0.028Â m3/m3 was achieved, and with a linear gain and offset applied at each ECONet site, an RMSE of 0.013Â m3/m3 was possible.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Advances in Water Resources - Volume 98, December 2016, Pages 122-131
Journal: Advances in Water Resources - Volume 98, December 2016, Pages 122-131
نویسندگان
Evan J. Coopersmith, Michael H. Cosh, Jesse E. Bell, Ryan Boyles,