Person:
Militino, Ana F.

Loading...
Profile Picture

Email Address

Birth Date

Research Projects

Organizational Units

Job Title

Last Name

Militino

First Name

Ana F.

person.page.departamento

Estadística, Informática y Matemáticas

person.page.instituteName

InaMat2. Instituto de Investigación en Materiales Avanzados y Matemáticas

ORCID

0000-0002-0631-3919

person.page.upna

220

Name

Search Results

Now showing 1 - 2 of 2
  • PublicationOpen Access
    Improving the quality of satellite imagery based on ground-truth data from rain gauge stations
    (MDPI, 2018) Militino, Ana F.; Ugarte Martínez, María Dolores; Pérez Goya, Unai; Estatistika eta Ikerketa Operatiboa; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística e Investigación Operativa; Gobierno de Navarra / Nafarroako Gobernua
    Multitemporal imagery is by and large geometrically and radiometrically accurate, but the residual noise arising from removal clouds and other atmospheric and electronic effects can produce outliers that must be mitigated to properly exploit the remote sensing information. In this study, we show how ground-truth data from rain gauge stations can improve the quality of satellite imagery. To this end, a simulation study is conducted wherein different sizes of outlier outbreaks are spread and randomly introduced in the normalized difference vegetation index (NDVI) and the day and night land surface temperature (LST) of composite images from Navarre (Spain) between 2011 and 2015. To remove outliers, a new method called thin-plate splines with covariates (TpsWc) is proposed. This method consists of smoothing the median anomalies with a thin-plate spline model, whereby transformed ground-truth data are the external covariates of the model. The performance of the proposed method is measured with the square root of the mean square error (RMSE), calculated as the root of the pixel-by-pixel mean square differences between the original data and the predicted data with the TpsWc model and with a state-space model with and without covariates. The study shows that the use of ground-truth data reduces the RMSE in both the TpsWc model and the state-space model used for comparison purposes. The new method successfully removes the abnormal data while preserving the phenology of the raw data. The RMSE reduction percentage varies according to the derived variables (NDVI or LST), but reductions of up to 20% are achieved with the new proposal.
  • PublicationOpen Access
    Interpolation of the mean anomalies for cloud filling in land surface temperature and normalized difference vegetation index
    (IEEE, 2019) Militino, Ana F.; Ugarte Martínez, María Dolores; Pérez Goya, Unai; Genton, Marc G.; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística, Informática y Matemáticas
    When monitoring time series of remote sensing data, it is advisable to fill gaps, i.e., missing or distorted data, caused by atmospheric effects or technical failures. In this paper, a new method for filling these gaps called interpolation of the mean anomalies (IMA) is proposed and compared with some competitors. The method consists of: 1) defining a neighborhood for the target image from previous and subsequent images across previous and subsequent years; 2) computing the mean target image of the neighborhood; 3) estimating the anomalies in the target image by subtracting the mean image from the target image; 4) filtering the anomalies; 5) averaging the anomalies over a predefined window; 6) interpolating the averaged anomalies; and 7) adding the interpolated anomalies to the mean image. To assess the performance of the IMA method, both a real example and a simulation study are conducted with a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) TERRA and MODIS AQUA images captured over the region of Navarre (Spain) from 2011 to 2013. We analyze the land surface temperature (LST) day and night, and the normalized difference vegetation index (NDVI). In the simulation study, seven sizes of artificial clouds are randomly introduced to each image in the studied time series. The square root of the mean-squared prediction error (RMSE) between the original and the filled data is chosen as an indicator of the goodness of fit. The results show that the IMA method outperforms Timesat, Hants, and Gapfill (GF) in filling small, moderate, and big cloud gaps in both the day and night LST and NDVI data, reaching RMSE reductions of up to 23%.