Chlorophyll-a (Chl-a) concentration is a key parameter to describe water quality in marine and freshwater environments. Nowadays, several products with Chl-a have derived from satellite imagery, but they are not available or reliable sometimes for coastal and/or small water bodies. Thus, in the last decade several methods have been described to estimate Chl-a with high-resolution (30 m) satellite imagery, such as Landsat, but a standardized method to estimate Chl-a from Landsat imagery has not been accepted yet. Therefore, this study evaluated the predictive performance of regression models (Simple Linear Regression [SLR], Multiple Linear Regression [MLR] and Generalized Additive Models [GAMs]) to estimate Chl-a based on Landsat imagery, using in situ Chl-a data collected (synchronized with the overpass of Landsat 8 satellite) and spectral reflectance in the visible light portion (bands 1–4) and near infrared (band 5). These bands were selected because of Chl-a absorbance/reflectance properties in these wavelengths. According to goodness of fit, GAM outperformed SLR and MLR. However, the model validation showed that MLR performed better in predicting log-transformed Chl-a. Thus, MLR, constructed by using four spectral bands (1, 2, 3, and 5), was considered the best method to predict Chl-a. The coefficients of this model suggested that log-transformed Chl-a concentration had a positive linear relationship with bands 1 (coastal/aerosol), 3 (green), and 5 (NIR). On the other hand, band 2 (blue) suggested a negative relationship, which implied high coherence with Chl-a absorbance/reflectance properties measured in the laboratory, indicating that Landsat 8 images could be applied effectively to estimate Chl-a concentrations in coastal environments.

Agave lechuguilla waste biomass (guishe) is an undervalued abundant plant material with natural active compounds such as flavonoids. Hence, the search and conservation of flavonoids through the different productive areas have to be studied to promote the use of this agro-residue for industrial purposes. In this work, we compared the proportion of total flavonoid content (TFC) among the total polyphenolics (TPC) and described the variation of specific flavonoid profiles (HPLC-UV-MS/MS) of guishe from three locations. Descriptive environmental analysis, using remote sensing, was used to understand the phytochemical variability among the productive regions. Furthermore, the effect of extractive solvent (ethanol and methanol) and storage conditions on specific flavonoid recovery were evaluated. The highest TPC (16.46 ± 1.09 GAE/g) was observed in the guishe from region 1, which also had a lower normalized difference water index (NDWI) and lower normalized difference vegetation index (NDVI). In contrast, the TFC was similar in the agro-residue from the three studied areas, suggesting that TFC is not affected by the studied environmental features. The highest TFC was found in the ethanolic extracts (6.32 ± 1.66 QE/g) compared to the methanolic extracts (3.81 ± 1.14 QE/g). Additionally, the highest diversity in flavonoids was found in the ethanolic extract of guishe from region 3, which presented an intermedia NDWI and a lower NDVI. Despite the geo-climatic induced variations of the phytochemical profiles, the results confirm that guishe is a valuable raw material in terms of its flavonoid-enriched bioactive extracts. Additionally, the bioactive flavonoids remain stable when the conditioned agro-residue was hermetically stored at room temperature in the dark for nine months. Finally, the results enabled the establishment of both agro-ecological and biotechnological implications.