Browsing by Subject "MODIS"

Now showing 1 - 2 of 2
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    Estimation of Actual Evapotranspiration using TDTM model and MODIS derived variables
    (Taylor & Francis LTD, 2022-01-10) Ruiz-Álvarez, Marcos; Gomariz-Castillo, Francisco; Alonso-Sarria, Francisco; López Ballesteros, Ana; Geografía
    Abstract: The objective of this paper is to contribute to improve ETa estimation in semiarid environments by proposing two variations to the TDMT model. These variations are based on the use of MODIS products from TERRA or AQUA satellites and on the use of NDVI instead of EVI, to estimate the fraction of vegetation cover. The proposed changes were validated with the original methodology for the 2012-2014 period with data obtained from two flux towers (ES-LJu and ES-Amo). The best results were obtained when using the alternative methodology (RMSE of 0.64-0.67 mm in ES-Lju and of 0.97-1.02 mm in ES-Amo). Additionally, a correction of the temporal systematic errors of the model based on Random Forest is proposed. With this correction, RMSE of 0.31-0.35 mm in ES-Lju and 0.30-0.34 mm in ES-Amo were obtained. The spatial distribution obtained with this corrected model is the most consistent with the characteristics of the study area
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    Open Access
    Interpolation of Instantaneous Air Temperature Using Geographical and MODIS Derived Variables with Machine Learning Techniques
    (MDPI, 2019-08-31) Ruiz Álvarez, Marcos; Alonso-Sarria, Francisco; Gomariz Castillo, Francisco; Geografía
    Several methods have been tried to estimate air temperature using satellite imagery. In this paper, the results of two machine learning algorithms, Support Vector Machines and Random Forest, are compared with Multiple Linear Regression and Ordinary kriging. Several geographic, remote sensing and time variables are used as predictors. The validation is carried out using two different approaches, a leave-one-out cross validation in the spatial domain and a spatio-temporal k-block cross-validation, and four different statistics on a daily basis, allowing the use of ANOVA to compare the results. The main conclusion is that Random Forest produces the best results (R2 = 0.888 ± 0.026, Root mean square error = 3.01 ± 0.325 using k-block cross-validation). Regression methods (Support Vector Machine, Random Forest and Multiple Linear Regression) are calibrated with MODIS data and several predictors easily calculated from a Digital Elevation Model. The most important variables in the Random Forest model were satellite temperature, potential irradiation and cdayt, a cosine transformation of the julian day.