Semi-empirical models for remote estimating colored dissolved organic matter (CDOM) in a productive tropical estuary.

Inland waters are important components of the global carbon cycle as they regulate the flow of terrestrial carbon to the oceans. In this context, remote monitoring of Colored Dissolved Organic Matter (CDOM) allows for analyzing the carbon content in aquatic systems. In this study, we develop semi-empirical models for remote estimation of the CDOM absorption coefficient at 400 nm (aCDOM) in a tropical estuarine-lagunar productive system using spectral reflectance data. Two-band ratio models usually work well for this task, but studies have added more bands to the models to reduce interfering signals, so in addition to the two-band ratio models, we tested three- and four-band ratios. We used a genetic algorithm (GA) to search for the best combination of bands, and found that adding more bands did not provide performance gains, showing that the proper choice of bands is more important. NIR-Green models outperformed Red-Blue models. A two-band NIR-Green model showed the best results (R2 = 0.82, RMSE = 0.22 m-1, and MAPE = 5.85%) using field hyperspectral data. Furthermore, we evaluated the potential application for Sentinel-2 bands, especially using the B5/B3, Log(B5/B3) and Log(B6/B2) band ratios. However, it is still necessary to further explore the influence of atmospheric correction (AC) to estimate the aCDOM using satellite data.

Uncertainties Involved in the Use of Thresholds for the Detection of Water Bodies in Multitemporal Analysis from Landsat-8 and Sentinel-2 Images.

Although the single threshold is still considered a suitable and easy-to-do technique to extract water features in spatiotemporal analysis, it leads to unavoidable errors. This paper uses an enumerative search to optimize thresholds over satellite-derived modified normalized difference water index (MNDWI). We employed a cross-validation approach and treated accuracy as a random variable in order to: (a) investigate uncertainty related to its application; (b) estimate non-optimistic errors involving single thresholding; (c) investigate the main factors that affect the accuracy's model, and (d) compare satellite sensors performance. We also used a high-resolution digital elevation model to extract water elevations values, making it possible to remove topographic effects and estimate non-optimistic errors exclusively from orbital imagery. Our findings evidenced that there is a region where thresholds values can vary without causing accuracy loss. Moreover, by constraining thresholds variation between these limits, accuracy is dramatically improved and outperformed the Otsu method. Finally, the number of scenes employed to optimize a single threshold drastically affects the accuracy, being not appropriate using a single scene once it leads to overfitted threshold values. More than three scenes are recommended.

Southern coastal subtropical shallow lakes skin temperature driven by climatic and non-climatic factors.

Subtropical coastal shallow lakes (SCSL) are sensitive ecosystems. The lake-skin-water temperature (LSWT) is an average lake temperature proxy and responds to changes in surroundings, affecting biological and physical lake processes. In this study, M*D11A1 products are used to develop daytime and nighttime LSWT time series for 20 SCSL in South America. The influence of climatic (air temperature, surface net solar radiation, wind speed, and wind direction) and non-climatic (latitude, lake area, perimeter, width, length, and morphology) factors are evaluated from 2001 to 2017. Pearson's coefficients (ρ) and auto- and cross-correlations are used to establish the relation between LWST and the selected factors. We identify that the dynamic of LSWT is sensitive to geomorphological factors (latitude and lake width) throughout the year, especially in summer. In winter, the LSTW regime is mainly affected by wind direction (ρ = -0.66, p value < 0.01). Linear models are fitted to the temperature series to check the trend changes in the inflection points and the warming or cooling trend for LSWT. Considering the complete series, the maximum warming rate of LSWT is 0.25 °C per decade (°C/dec). The analysis of the identified sub-periods reveals that warming and cooling can occur (significantly) in shorter periods. The average trends within sub-periods for skin temperature-daytime (± 0.0105 °C/dec), skin temperature-nighttime (0.0041 °C/dec), and air temperature (- s0.006 °C/dec; 0.007 °C/dec) are estimated. Our approach has the potential to be applied in future studies due to the expansion of knowledge about the behavior of SCSL and the understanding of the current and potential effects of climate change in association with physical and geomorphological traits.