No abstract

The atmospheric effects that influence on the signal registered by remote sensors might be minimized in order to provide reliable spectral information. In aquatic systems, the application of atmospheric correction aims to minimize such effects and avoid the under or overestimation of remote sensing reflectance (R rs). Accurately R rs provides better information about the state of aquatic system, it means, establishing the concentration of aquatic compounds more precisely. The aim of this study is to evaluate the outputs from several atmospheric correction methods (Dark Object Subtraction-DOS; Quick Atmospheric Correction-QUAC; Fast Line-of-sight Atmospheric Analysis of Hypercubes-FLAASH; Atmospheric Correction for OLI 'lite'-ACOLITE, and Provisional Landsat-8 Surface Reflectance Algorithm-L8SR) in order to investigate the suitability of R rs for estimating total suspended matter concentrations (TSM) in the Barra Bonita Hydroelectrical Reservoir. To establish TSM concentrations via atmospherically corrected Operational Land Imager (OLI) scene, the TSM retrieval model was calibrated and validated with in situ data. Thereby, the achieved results from TSM retrieval model application demonstrated that L8SR is able to provide the most suitable R rs values for green and red spectral bands, and consequently, the lowest TSM retrieval errors (Mean Absolute Percentage Error about 10% and 12%, respectively). Retrieved R rs from near infrared band is still a challenge for all the tested algorithms.

In this present research, we assessed the performance of band algorithms in estimating chlorophyll-a (Chl-a) concentration based on bands of two new sensors: Operational Land Imager onboard Landsat-8 satellite (OLI/Landsat-8), and MultiSpectral Instrument onboard Sentinel-2A (MSI/Sentinel-2A). Band combinations designed for Thematic Mapper onboard Landsat-5 satellite (TM/Landsat-5) and MEdium Resolution Imaging Spectrometer onboard Envisat platform (MERIS/Envisat) were adapted for OLI/ Landsat-8 and MSI/Sentinel-2A bands. Algorithms were calibrated using in situ measurements collected in three field campaigns carried out in different seasons. The study area was the Barra Bonita hydroelectric reservoir (BBHR), a highly productive aquatic system. With exception of the three-band algorithm, the algorithms were spectrally few affected by sensors changes. On the other hands, algorithm performance has been hampered by the bio-optical difference in the reservoir sections, drought in 2014 and pigment packaging.

Coloured dissolved organic matter (CDOM) is the most abundant dissolved organic matter (DOM) in many natural waters and can affect the water quality, such as the light penetration and the thermal properties of water system. So the objective of this letter was to estimate the CDOM absorption coefficient at 440 nm, a CDOM (440), in Barra Bonita Reservoir (São Paulo State, Brazil) using operational land imager (OLI)/Landsat-8 images. For this two field campaigns were conducted in May and October 2014. During the field campaigns remote sensing reflectance (R rs) were measured using a TriOS hyperspectral radiometer. Water samples were collected and analysed to obtain the a CDOM (440). To predict the a CDOM (440) from R rs at two key wavelengths (650 and 480 nm) were regressed against laboratory-derived a CDOM (440) values. The validation using in situ data of a CDOM (440) algorithm indicated a goodness of fit, R 2 = 0.70, with a root mean square error (RMSE) of 10.65%. The developed algorithm was applied to the OLI/Lansat-8 images. Distribution maps were created with OLI/Landsat-8 images based on the adjusted algorithm.

Droughts are natural events that can cause water scarcity and can consequently have undesired environmental, social and political effects. Because droughts are related to land use and land cover modifications, satellite images are used to monitor and identify drought episodes through indices as Standardized Precipitation Index based on rainfall data and vegetation-based indices as Normalized Difference Vegetation Index (NDVI). Changes in vegetation cover have as impact the increasing of the land surface temperature (LST) that is a significant indicative of drought occurrence. This work explored the NDVI-LST relation through the Vegetation Health Index (VHI) in a tropical environment in Tietê River, State of São Paulo, Brazil, in order to assess changes in vegetation condition in two periods (2000 and 2014). Results showed that stressed areas are coincident with areas presenting high rate of modification in land cover; this areas presented low values of VHI and high values of LST. The worst conditions are verified in 2014, the same period of the most severe drought occurrence that reduced storage capacity in reservoirs in Tietê River.