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This study estimated the severity of Nilahue-Barahona and Las Máquinas wildfires, occurred in central Chile during the summer of 2016-17, by an empirical-statistical modelling based on pre- and post-fire arithmetic differences of spectral indices sensitive to vigor, turgor and calcination states of vegetation. By doing this, map of damages were created to aid the efficient management and ecological restoration of disturbed forestry ecosystems. The index differences were calculated from Sentinel-2 satellite images, acquired anually in the summers spanning from 2016 to 2019. The resulting nine index-derived differences were used as predictors of burn severity, field-measured during the summer of 2019 using the CBI (composite burn index) method, into a linear stepwise regression that allowed for selecting those with the highest predictability. CBI yielded low correlations as its calculation includes low vegetation strata largely recovered at the time of the field data collection. However, when overstory field data were used alone, correlations increased (70 % of the data = 0.80, P < 0.05). This was because this stratum was still appreciably damaged during the field campaign, along with its best representation from the image planimetry. The burn severity of both wildfires was mapped using the overstory data as regressand in a model based on NDWIex-ante-2019, NDWIex-ante-2018, NBRex-ante-2018 and NBRex-ante-2017 differences (R² ad = 0.77, RMSE = 0.35). [ABSTRACT FROM AUTHOR]

Simple Summary: Measures of plant water status on kiwifruit were evaluated using Sentinel-2 (A/B) for two seasons in Central Chile. For more than ten years, Central Chile has faced drought conditions, which impact crop production and quality, increasing food security risk. Under this scenario, implementing management practices that allow increasing water use efficiency is urgent. The study was carried out on kiwifruit trees, located in the O'Higgins region, Chile for season 2018–2019 and 2019–2020. We evaluate the time-series of nine vegetation indices in the VNIR and SWIR regions derived from Sentinel-2 (A/B) satellites to establish how much variability in the canopy water status there was. Over the study's site, eleven sensors were installed in five trees, which continuously measured the leaf's turgor pressure (Yara Water-Sensor). A strong Spearman's (ρ) correlation between turgor pressure and vegetation indices was obtained, having −0.88 with EVI and −0.81 with GVMI for season 2018–2019, and lower correlation for season 2019–2020, reaching −0.65 with Rededge1 and −0.66 with EVI. However, the NIR range's indices were influenced by the vegetative development of the crop rather than its water status. The red-edge showed better performance as the vegetative growth did not affect it. It is necessary to expand the study to consider higher variability in kiwifruit's water conditions and incorporate the sensitivity of different wavelengths. [ABSTRACT FROM AUTHOR]