کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
10224021 1701070 2018 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An advanced error correction methodology for merging in-situ observed and model-based soil moisture
ترجمه فارسی عنوان
یک روش اصلاح خطای پیشرفته برای ادغام رطوبت خاک مشاهده شده و مدل مبتنی بر مدل
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات فرآیندهای سطح زمین
چکیده انگلیسی
In order to obtain an improved soil moisture (SM) dataset at large scale, an advanced SM merging methodology based on error correction methods was constructed to merge the model-based and in-situ observed SM data. The SM datasets in a 0-40 cm soil layer were derived from 10 km × 10 km Variable Infiltration Capacity (VIC) model and 797 in-situ stations, respectively. The merging methodology was conducted grid by grid, and mainly included two parts: bias correction and random error correction. Firstly, the bias correction was performed for the VIC simulations by applying the Cumulative Distribution Function (CDF) matching approach combined with the kriging technique. Secondly, the random error of the VIC simulations was corrected using an Optimal Interpolation (OI) technique based on a spatio-temporal correlation function which was proposed and constructed in this study. Through validations against in-situ observations, the merged SM was evaluated, and the merging errors in each step were analyzed and discussed. The results showed that the merged SM product was improved compared to the original SM data, both temporally and spatially. The SM merging methodology is effective and reliable in combining the accurate but sparse in-situ observations and the continuous VIC simulations. In addition, the spatial mismatch impact on the representativeness of in-situ stations was limited, and the merging errors were mainly produced in the CDF estimation process. The random error information in the spatial dimension exhibited a bigger impact on the random error correction comparing to that in the temporal dimension. This study provided strong encouragement for the efficient use of in-situ SM observations and provided valuable methods for combining multi-sources SM datasets.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Hydrology - Volume 566, November 2018, Pages 150-163
نویسندگان
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