An Evaluation of M5 Model Tree vs. Artificial Neural Network for Estimating Mean Air Temperature as Based on Land Surface Temperature Data by MODIS-Terra Sensor
Author(s):
Abstract:
The use of satellite data in an estimation of air temperature (Ta) near the earth’s surface has turned into an effective way for a large area of high spatial and temporal resolution. Throughout the present study، Artificial Neural Network (ANN) as well as M5 model tree were employed to estimate Ta in Khuzestan Province (South West of Iran)، using satellite remotely sensed land surface temperature (Ts) data acquired through the MODIS-Terra sensor. The input variables for the models consisted of the daytime and nighttime MODIS Ts as well as extraterrestrial solar radiation. A total of 365 images of MOD11A1 Ts product for the year 2007، covering the area under study were collected from the Land Processes Distributed Active Archive Center (LP DAAC). The results indicated that coefficient of determination (R2) for both models exceeded 0. 96. However، ANN model estimations of air temperature were more accurate than RMSE with the respective R2 values of 1. 7 and 0. 97 oC.
Keywords:
Language:
Persian
Published:
Iranian Journal of Soil and Water Research, Volume:45 Issue: 4, 2015
Pages:
423 to 433
magiran.com/p1363584
دانلود و مطالعه متن این مقاله با یکی از روشهای زیر امکان پذیر است:
اشتراک شخصی
با عضویت و پرداخت آنلاین حق اشتراک یکساله به مبلغ 1,390,000ريال میتوانید 70 عنوان مطلب دانلود کنید!
اشتراک سازمانی
به کتابخانه دانشگاه یا محل کار خود پیشنهاد کنید تا اشتراک سازمانی این پایگاه را برای دسترسی نامحدود همه کاربران به متن مطالب تهیه نمایند!
توجه!
- حق عضویت دریافتی صرف حمایت از نشریات عضو و نگهداری، تکمیل و توسعه مگیران میشود.
- پرداخت حق اشتراک و دانلود مقالات اجازه بازنشر آن در سایر رسانههای چاپی و دیجیتال را به کاربر نمیدهد.
In order to view content subscription is required
Personal subscription
Subscribe magiran.com for 70 € euros via PayPal and download 70 articles during a year.
Organization subscription
Please contact us to subscribe your university or library for unlimited access!