Al Proyecto Cátedra CONCYTEC: “Teledetección en Desertificación y Sequía” (R.P.N° 328-2011-P) por la subvención de una beca para el estudio de maestría y desarrollo de la tesis.

Contribuye con información climática de la humedad atmosférica en el Perú y áreas vecinas, con el fin de caracterizar la climatología de la humedad atmosférica y su relación con la sequía en la Costa Peruana. En la investigación se utiliza datos imágenes del sensor MODIS a bordo de los satélites TERRA y AQUA. El estudio considera el territorio entre 0- y 1000-msnm. El indicador de la humedad atmosférica es el vapor de agua, según la WMO. Este parámetro es evaluado en base de la columna de vapor de agua precipitable (PWV: siglas en inglés) en la atmósfera, estimado de los datos producto diarios de Nivel 2 PWV (MOD05) en la banda infrarrojo cercano (NIR) para el periodo 2000-2013 del MODIS/TERRA y 2002-2008 del MODIS/AQUA. Otros datos utilizados son: la temperatura de la superficie del mar (TSM), el índice de vegetación de diferencia normalizada (NDVI) y un modelo de ele...

The interannual variability of lake surface water temperature (LSWT) was investigated using MODIS images for winter 2001-2014 at Lake Titicaca 15°45'00''S - 69°25'00''O. We use the MOD11A2 Earth Surface Temperature (LST) product, which we will rename as LSWT. Remote sensing LST data was obtained from the MODIS sensor onboard the Terra satellite as an 8-day composite, with a spatial resolution of 1 km. During the austral winter (June, July, and August), the product MOD06L2 cloud fraction showed the clear sky over Lake Titicaca in winter. The LSWT distribution was analyzed by means of images, time series and the coefficient of variation (Cv). Two regions of Titicaca were considered, Lake Mayor (LM) and Lake Menor (Lm). It was discovered that the coefficient of variation of LSWT for the main basin (LM) during the study period did not show significant changes (Cv < 9%). The average valu...

We quantified the influence of sea surface temperature (SST) in the Niño 1+2 region and precipitation on the NDVI dynamics of the dry forest along Peru’s northern coast during 2003–2023. We used monthly series from MODIS (NDVI), CHIRPS (precipitation), and NOAA SST, seasonally standardized (z-score). We applied a 36-month rolling correlation, time–frequency analysis (XWT/WTC), and autoregressive models with exogenous regressors (ARX). Cross-correlation showed that NDVI responds with a positive one-month lag to both SST and precipitation, with correlation coefficients of 0.764 and 0.613, respectively. Among four AR(2) models evaluated, the ARX with lagged SST provided the best fit (AIC = 295.35), slightly outperforming the combined SST+precipitation model (AIC = 295.46). Akaike weights favored the former on grounds of parsimony. The results indicate a positive sensitivity of NDVI t...