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.

Seasonal changes control the development of salt crust over the Salar de Aguas Calientes Sur located in Andes Highlands, Chile. Precipitations throughout the Altiplanic winter (December to March) and austral winter (June to September) caused ponds to enlarge and surface salt crusts to dissolve driving roughness and dielectric features of the salar surface change over time. A four-year time series backscattering coefficient analysis, obtained by Sentinel 1 and ALOS-2/PALSAR-2 with 10 m of spatial resolution, demonstrated the capability of microwaves to discriminate seasonal patterns illustrated in this paper. Both sensors showed to be sensitive to changes in the surface crust due to weather conditions. Backscattered power gradually increased during the driest months as the rough salt crusts develop and decreased rapidly due to precipitations or flooding events, which lead to a smoothing appearance to radar. The high temporal frequency of acquisition in Sentinel 1 (5–13 scenes/month) allowed the discrimination among climate and annual seasonality and episodic events in the C-band backscatter coefficient. On the other hand, ALOS-2/PALSAR-2 showed subsurface changes at L-band since the salinity of the brine in the soil reduces the penetration depth of backscattered power for shorter wavelengths. Results might be useful to monitor salars with geographic and weather conditions similar to Salar de Aguas Calientes Sur.

The objective of this work is to monitor the mining subsidence in a mountain range sector of central Chile between 2014 and 2018 through the processing of a time series of Sentinel 1 images using differential interferometry techniques (DInSAR). As a pretest, nine interferometric pairs were considered and processed using SNAP and SNAPHU software. Coherence levels obtained in complex topography sectors were low, due to both temporal and geometrical decorrelation. However, it is proposed that data from the PAZ project (X-band) and the Argentinean SAOCOM satellite (L-band) be used in the future.

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Chile is one of the countries with the highest seismicity in the world and is affected by three types of seismogenic sources; interplate, intraplate and superficial or cortical intraplate. In this context, in the eastern sector of the city of Santiago, capital of Chile, the Falla San Ramón (FSR) is located. It is a cortical seismogenic source, which threatens its habitants and the various economic activities that are located in that sector, geological hazards such as earthquakes and mass removals. In relation to the above, this study aims to identify and establish the subsidence areas in a longitudinal strip of the Santiago mountain front and its impact on the neighboring communes to the FSR trace during the years 2011 to 2017. To do this, The DInSAR technique was used with the Small Baseline Subset (SBAS) algorithm through a time series of images from the TerraSAR-X (TSX) satellite. The results show subsidence zones, with average displacements ranging from -13.11 mm to +9.89 mm, with an average annual speed rate of -2.19 to +1.65 mm/year.