Estimation of soil moisture using optical/thermal infrared remote sensing in the Canadian Prairies

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• 作者：Parinaz Rahimzadeh-Bajgiran ; Aaron A. Berg ; Catherine Champagne ; Kenji Omasa
• 关键词：Soil moisture ; Evaporative fraction ; Land surface temperature ; Air temperature ; MODIS
• 刊名：ISPRS Journal of Photogrammetry and Remote Sensing
• 出版年：2013
• 出版时间：September, 2013
• 年：2013
• 卷：83
• 期：Complete
• 页码：94-103
• 全文大小：3013 K

文摘

A new approach to estimate soil moisture (SM) based on evaporative fraction (EF) retrieved from optical/thermal infrared MODIS data is presented for Canadian Prairies in parts of Saskatchewan and Alberta. An EF model using the remotely sensed land surface temperature (Ts)/vegetation index concept was modified by incorporating North American Regional Reanalysis (NAAR) Ta data and used for SM estimation. Two different combinations of temperature and vegetation fraction using the difference between Ts from MODIS Aqua and Terra images and Ta from NARR data (Ts?Ta Aqua-day and Ts?Ta Terra-day, respectively) were proposed and the results were compared with those obtained from a previously improved model (¦¤Ts Aqua-DayNight) as a reference. For the estimation of SM from EF, two empirical models were tested and discussed to find the most appropriate model for converting MODIS-derived EF data to SM values. Estimated SM values were then correlated with in situ SM measurements and their relationships were statistically analyzed. Results indicated statistically significant correlations between SM estimated from all three EF estimation approaches and field measured SM values (R² = 0.42-0.77, p values < 0.04) exhibiting the possibility to estimate SM from remotely sensed EF models. The proposed Ts?Ta MODIS Aqua-day and Terra-day approaches resulted in better estimations of SM (on average higher R² values and similar RMSEs) as compared with the ¦¤Ts reference approach indicating that the concept of incorporating NARR Ta data into Ts/Vegetation index model improved soil moisture estimation accuracy based on evaporative fraction. The accuracies of the predictions were found to be considerably better for intermediate SM values (from 12 to 22 vol/vol % ) with square errors averaging below 11 (vol/vol % )². This indicates that the model needs further improvements to account for extreme soil moisture conditions. The findings of this research can be potentially used to downscale SM estimations obtained from passive microwave remote sensing techniques.