Thermal infrared (TIR) data are usually acquired at 1 a coarser spatial resolution (CR) than visible and near infrared 2 (VNIR). Several disaggregation methods have been recently devel-3 oped to enhance the TIR spatial resolution using VNIR data. These 4 approaches are based on the retrieval of a relation between TIR 5 and VNIR data at CR, or training of a neural network, to be 6 applied at the fine resolution afterward. In this work, different 7 disaggregation methods are applied to the combination of two 8 different sensors in the experimental test site of Barrax, Spain. 9 The main objective is to test the feasibility of these techniques 10 when applied to satellites provided with no TIR bands. Landsat 11 and moderate imaging spectroradiometer (MODIS) images were 12 used for this work. Land surface temperature (LST) from MODIS 13 images was disaggregated to the Landsat spatial resolution using 14 Landsat VNIR data. Landsat LST was used for the validation 15 and comparison of the different techniques. Best results were 16 obtained by the method based on a linear regression between 17 normalized difference vegetation index (NDVI) and LST. An aver-18 age RMSE = ±1.9 K was observed between disaggregated and 19 Landsat LST from four different dates in a study area of 120 km 2. 20 Index Terms-Image enhancement, image resolution, remote 21 sensing, temperature. 22 I. INTRODUCTION 23 T IME series of fine spatial and temporal resolution images 24 are key inputs in numerous studies, e.g., water resources 25 management [1], [2]. However, there is a limitation in the exist-26 ing satellites since revisit time for fine spatial resolution sensors 27 is typically poor, while those with a high revisit frequency are 28 characterized by a coarse spatial resolution. This is especially 29 true when focusing on the thermal infrared (TIR) since spa-30 tial resolution for the TIR bands is always coarser than that for 31 the visible and near infrared (VNIR) bands onboard the same 32 sensor [2]. 33 Disaggregation methods allow downscaling the TIR coarse 34 resolution (CR) to finer resolutions. In [3], a review of land 35 Manuscript

Fire danger models are a very useful tool for the prevention and extinction of forest fires. Some inputs of these models, such as vegetation status and temperature, can be obtained from remote sensing images, which offer higher spatial and temporal resolution than direct ground measures. In this paper, we focus on the Galicia region (north-west of Spain), and MODIS (Moderate Resolution Imaging Spectroradiometer) images are used to monitor vegetation status and to obtain land surface temperature as essential inputs in forest fire danger models. In this work, we tested the potential of artificial neural networks and logistic regression to estimate forest fire danger from remote sensing and fire history data. Remote sensing inputs used were the land surface temperature and the Enhanced Vegetation Index. A classification into three levels of fire danger was established. Fire danger maps based on this classification will facilitate fire prevention and extinction tasks.

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Abstract:The Biosphere Reserve of La Mancha Húmeda is a wetland-rich area located in central Spain. This reserve comprises a set of temporary lakes, often saline, where water level fluctuates seasonally. Water inflows come mainly from direct precipitation and runoff of small lake watersheds. Most of these lakes lack surface outlets and behave as endorheic systems, where water withdrawal is mainly due to evaporation, causing salt accumulation in the lake beds. Remote sensing was used to estimate the temporal variation of the flooded area in these lakes and their associated hydrological patterns related to the seasonality of precipitation and evapotranspiration. Landsat 7 ETM+ satellite images for the reference period 2013-2015 were jointly used with ground-truth datasets. Several inverse modeling methods, such as two-band and multispectral indices, single-band threshold, classification methods, artificial neural network, support vector machine and genetic programming, were applied to retrieve information on the variation of the flooded areas. Results were compared to ground-truth data, and the classification errors were evaluated by means of the kappa coefficient. Comparative analyses demonstrated that the genetic programming approach yielded the best results, with a kappa value of 0.98 and a total error of omission-commission of 2%. The dependence of the variations in the water-covered area on precipitation and evaporation was also investigated. The results show the potential of the tested techniques to monitor the hydrological patterns of temporary lakes in semiarid areas, which might be useful for management strategy-linked lake conservation and specifically to accomplish the goals of both the European Water Framework Directive and the Habitats Directive.

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