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Several built-up indices have been proposed in the literature in order to extract the urban sprawl from satellite data. Given their relative simplicity and easy implementation, such methods have been widely adopted for urban growth monitoring. Previous research has shown that built-up indices are sensitive to different factors related to image resolution, seasonality, and study area location. Also, most of them confuse urban surfaces with bare soil and barren land covers. By gathering the existing built-up indices, the aim of this paper is to discuss some of their advantages, difficulties, and limitations. In order to illustrate our study, we provide some application examples using Sentinel 2A data.

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Southeastern Mexico, particularly Tabasco's flatlands which experienced a severe flood in 2007, was used as a case study for testing a methodology for the estimation of direct damage looses on agricultural crops by flooding. We proposed an accurate delineation of agricultural lands of multispectral images (SPOT-5) which consist on ensemble classifiers trough a majority voting, that combine spatial and spectral information. Finally in order to evaluate the impact of floodwater, a radar data (RADARSAT-1), were used for both, delineating the flood extent and estimating water depth. These layers were overlaid on the agricultural crop classification layer, and crop yield damage was estimated using a depth damage function. The results of this research quantified and evaluated the overall economic loss (tangible damage) from the impact of floodwater on agricultural crops.