Satellite Solutions: Facing Chlorophyll‐ a Retrieval in Small Mountain Lakes in the Sierra Nevada, Spain

Abstract National and international regulations enforce monitoring programmes of water quality to guide management actions of inland water ecosystems. Our study evaluates the effect of spectral and spatial resolutions on the estimation of chlorophyll‐ a concentrations in mountain lakes, and derives implications for addressing the adjacency effect, which is critical and understudied in small water bodies. Five lakes in Sierra Nevada (Spain) were repeatedly sampled during 2020, 2021, and 2023, and a total of 100 chlorophyll‐ a samples with suitable coincident satellite imagery were analyzed. Laboratory‐obtained chlorophyll‐ a concentrations were modeled comparing up to 86 spectral indices and bands as predictors from three satellites: Sentinel‐2 (12 bands, 20 m/pixel), Planet (8 bands, 3 m/pixel) and WorldView‐3 (11 bands, 1.24 m/pixel). Our results showed that multivariate models for estimating chlorophyll‐ a using spectral indices did not perform significantly better than using bands alone. The best models always had multiple predictors and included green and near‐infrared bands. Models based on Sentinel‐2 and Planet ( R adj 2 > 0.45) outperformed those of WorldView‐3 ( R adj 2 ∼ 0.37), confirming that the latter performed worst despite higher spatial resolution. Regarding distance to shoreline, the Planet model showed the most consistent performance, with stable R adj 2 values and low RMSE even at 3 m from shore with a high level of accuracy ( R adj 2 ∼ 0.3; RMSE ∼ 1.15 μg L −1 ). Data and models are released to facilitate near‐real‐time monitoring of these vulnerable ecosystems, where field sampling is extremely challenging.