Revista de Teledetección 2023-01-30T13:38:00+01:00 Luis Ángel Ruiz Fernández Open Journal Systems <p class="default" style="text-align: justify; text-justify: inter-ideograph; margin: 0cm 0cm 6.0pt 0cm;"><em>Spanish Journal of Remote Sensing / Revista de Teledetección (RAET)</em> is a biannual scientific journal that publishes original research papers related to a wide range of methods and applications in remote sensing. The official publication languages are both, Spanish and English. The journal is open access and there are no charges for publication..</p> Models for the estimation of sugarcane yield in Costa Rica with field data and vegetation indices 2022-11-28T11:30:33+01:00 Bryan Alemán-Montes Pere Serra Alaitz Zabala <p>Remote sensing offers important inputs for sugarcane yield estimation, since its temporal and spatial approaches allow monitoring the phenological cycle of the crop. The objective of this research was to apply an operational method for the estimation of sugarcane yield and sugar content through the combination of field variables with vegetation indices, calculated with the satellite sensors on board Sentinel-2 and Landsat-8 in a cooperative from Costa Rica. In addition, historical harvest data and start months of phenological cycle were used to estimate sugarcane yield and sugar content per ton using multiple linear regressions. The integration of historical data and Simple Ratio (SR) vegetation index, calculated in different steps of the phenological cycle (in the months of September, December and January), allowed us to obtain an estimation model of sugarcane yield (tons of sugarcane per hectare) with a regression coefficient (R2) of 0.64 and a RMSE of 8.0 tons/ha. While for sugar content (kilograms of refined sugar per ton) we obtained a R2 of 0.59 integrating historical variables and the vegetation indexes SR and Green Normalized Difference Vegetation Index (GNDVI); in this case the RMSE was 4.9 kg/tons. Ultimately, this operational method of yield estimation provides tools for decision making before, during and after the harvest stage.</p> 2023-01-30T00:00:00+01:00 Copyright (c) 2023 Bryan Alemán-Montes, Pere Serra, Alaitz Zabala Gross primary production (GPP) changes of natural vegetation in the Comunidad Valenciana (2001-2018) 2022-11-17T10:29:02+01:00 Beatriz Martínez Sergio Sánchez-Ruiz Manuel Campos-Taberner Francisco Javier García-Haro María Amparo Gilabert <p>This work analyzes the vegetation changes in the Comunidad Valenciana observed during the period 2001-2018, using the daily GPP (Gross Primary Production) time series at 1-km spatial resolution derived from Earth observation-based (EO) data. The GPP time series have been obtained from EO-based data (e.g., MODIS/Terra-Aqua and SEVIRI/MSG) and meteorological (e.g., precipitation and temperature) data using the light use efficiency model proposed by Monteith. The carbon fluxes detection has been performed by means of a multi-resolution analysis (MRA) based on the wavelet transform (WT). This analysis allows to decomposing the signal into different temporal resolution components. The interanual trend determines the vegetation change, positive (greening) or negative (browning) of vegetation photosynthetic activity over long-term scales. The negative long-term changes observed in natural vegetation reveal the presence of areas characterized by high degradated conditions. This is the case of Natural Pack of ‘Serra d’ Espadà’ in Castellon province, which is also controlled by a local ecosystem conservation program. To identify more precisely these areas, the areas affected by abrupt changes (associated to forest fires) in which vegetation has not been yet recovered have been removed. In this case, the results show a good agreement with the official burnt areas from the local government.</p> 2023-01-30T00:00:00+01:00 Copyright (c) 2023 Beatriz Martínez, Sergio Sánchez-Ruiz, Manuel Campos-Taberner, Francisco Javier García-Haro, María Amparo Gilabert Land cover classification with spatial resolution of 10 meters in forests of the Colombian Caribbean based on Sentinel 1 and 2 missions 2022-12-05T12:43:23+01:00 Jesús A. Anaya Susana Rodríguez-Buriticá María C. Londoño <p>A Land cover map of the Colombian Caribbean were generated with data from the Sentinel-1 and Sentinel-2 missions for the year 2020. The main objective was to evaluate Sentinel 1 and 2 images to generate a classification for Caribbean forests. The images were processed using Google Earth Engine (GEE) and then classified using Random Forest. The Overall Accuracy, the Mean Decrease Accuracy and the Mean Decrease in Gini were calculated for the optical and radar bands, this allowed evaluating the importance of different regions of the electromagnetic spectrum in the classification of vegetation cover and the relative importance of the spectral bands. The accuracy of the land cover map was 76% using exclusively Sentinel-2 bands, with a slight increase when Sentinel-1 data was incorporated. The SWIR region was the most important of both Sentinel programs for increasing accuracy. We highlight the importance of coastal aerosol band 1 (442.7 nm) in the classification despite its low spatial resolution. The overall accuracy reached 83% when adding the Elevation data from the Shuttle Radar Topography Mission (SRTM) as auxiliary variable. These results indicate great potential for the generation of vegetation cover maps at the regional level while maintaining a pixel size of 10 m. This article highlights the relative importance of the different bands and its contribution to improve accuracy.</p> 2023-01-30T00:00:00+01:00 Copyright (c) 2023 Jesús A. Anaya, Susana Rodríguez-Buriticá, María C. Londoño Delimitation of burned areas in Chile based on dNBR thresholds adjusted according to region and land cover 2022-12-30T14:17:26+01:00 Raimundo Sánchez María José Briones Alexis Gamboa Rafaella Monsalve Denis Berroeta Luis Valenzuela <p>The delimitation of burned areas is an important step for the study of forest fires, and the use of satellite remote sensing allows a scalable methodology. Previous studies use a dNBR threshold to determine the presence of burned areas, but this threshold is affected by vegetation variability determined by the geography of the study area and land use coverage. For them, the difference in the normalized index of burned areas (dNBR) was used to study the mega fires that affected the central zone of Chile in the summer of 2017. An automated methodology was developed that, based on satellite images and polygons of the burned areas provided by the National Forestry Corporation of Chile (CONAF) generates a set of dNBR thresholds differentiated by administrative region and land use. The application of differentiated dNBR thresholds significantly improves the accuracy of the burnt area delimitation model, although it does not achieve satisfactory results for all land uses. This methodological advance will make it possible to improve the design and control of policies for the prevention, conservation and restoration of ecosystems affected by forest fires.</p> 2023-01-30T00:00:00+01:00 Copyright (c) 2023 Raimundo Sánchez, María José Briones, Alexis Gamboa, Rafaella Monsalve, Denis Berroeta, Luis Valenzuela A simple method for the estimation of minimum and maximum air temperature monthly mean maps using MODIS images in the region of Murcia, Spain 2023-01-23T10:47:48+01:00 Alfonso Galdón-Ruíz Guillermo Fuentes-Jaque Jesús Soto Luis Morales-Salinas <p>Air temperature records are acquired by networks of weather stations which may be several kilometres apart. In complex topographies the representativeness of a meteorological station may be diminished in relation to a flatter valley, and the nearest station may have no relation to a place located near it. The present study shows a simple method to estimate the spatial distribution of minimum and maximum air temperatures from MODIS land surface temperature (LST) and normalized difference vegetation index (NDVI) images. Indeed, there is a strong correlation between MODIS day and night LST products and air temperature records from meteorological stations, which is obtained by using geographically weighted regression equations, and reliable results are found. Then, the results allow to spatially interpolate the coefficients of the local regressions using altitude and NDVI as descriptor variables, to obtain maps of the whole region for minimum and maximum air temperature. Most of the meteorological stations show air temperature estimates that do not have significant differences compared to the measured values. The results showed that the regression coefficients for the selected locations are strong for the correlations between minimum temperature with LSTnight (R2 = 0.69–0.82) and maximum temperature with LSTday (R2 = 0.70–0.87) at the 47 stations. The root mean square errors (RMSE) of the statistical models are 1.0 °C and 0.8 °C for night and daytime temperatures, respectively. Furthermore, the association between each pair of data is significant at the 95% level (p&lt;0.01).</p> 2023-01-30T00:00:00+01:00 Copyright (c) 2023 Alfonso Galdón Ruíz, Guillermo Fuentes-Jaque, Jesús Soto Espinosa, Luis PhenoApp. A Google Earth Engine based tool for monitoring phenology 2022-11-26T14:18:05+01:00 Diego García-Díaz Ricardo Díaz-Delgado <p>PhenoApp application have been developed within the framework of the eLTER Plus and SUMHAL projects, as a tool aimed at scientists and managers of the sites integrated in the eLTER network, for which long-term phenology monitoring can be assessed. The application provides a dynamic map that allows the selection of any site in the network and queries the phenological metrics of each pixel or group of pixels generated with the Sentinel-2 time series of images using the Ndvi2Gif and PhenoPY python libraries. The application also integrates phenology products from MODIS (MCD12Q2.006) and Copernicus Sentinel 2 High Resolution Vegetation Phenology Product (HR VPP). In addition, the application incorporates a web form that allows the user to provide the phenology data obtained in situ (through direct observation or phenocams), which will be used to perform a validation of the different products obtained via satellite. As an example, we carried out a preliminary validation in one of the sites of the eLTER network located in the Doñana Natural Area (END). We used in situ data provided by the network of phenocams in the Doñana Biological Reserve since 2016 installed by the Singular Scientific and Technical Infrastructure of Doñana (ICTS-Doñana). A preliminary validation analysis highlights the need to consider the discrepancies between the different products and methods according to the phenological variability inherent in each ecosystem.</p> 2023-01-30T00:00:00+01:00 Copyright (c) 2023 Diego García-Díaz, Ricardo Díaz-Delgado Evaluation of the impact of super-resolution on GEOSAT-2 multispectral images 2022-12-29T09:33:54+01:00 César Fernández Carolina de Castro Lucía García María Elena Calleja Rubén Niño Silvia Fraile Rafael Sousa <p>The growing need to observe the Earth in greater detail means the appearance of new techniques to improve the geometric value of images, preserving their radiometric characteristics. Security and Defence sectors are strategic users of these advances, but not the only ones. By being able to preserve the radiometric characteristics of the data, precision agriculture is a key beneficiary of these improvements. In this way, more detailed data and information can be provided on the specific needs of each crop, which means a direct implication for the farmer, the agricultural companies, and the environment. In this work, the Random Forest and XGBoost methods were applied in order to improve the resolution of the GEOSAT-2 images while preserving their radiometric values. In addition, the quality of the enhanced images was evaluated. Also, the satisfactory evaluation of the improved images is presented, both in terms of resolution and the final quality obtained. This evaluation has been conducted by the Copernicus Coordinated data Quality Control (CQC) team, allowing the addition of a new product to the GEOSAT portfolio, ready to be integrated into the Copernicus Programme data offer.</p> 2023-01-30T00:00:00+01:00 Copyright (c) 2023 César Fernández, Carolina de Castro, Lucía García López, María Elena Calleja, Rubén Niño, Silvia Fraile, Rafael Sousa