Optimization of Landsat Chl-<i>a</i> Retrieval Algorithms in Freshwater Lakes through Classification of Optical Water Types
The application of remote sensing data to empirical models of inland surface water chlorophyll-a concentrations (chl-<i>a</i>) has been in development since the launch of the Landsat 4 satellite series in 1982. However, establishing an empirical model using a chl-<i>a</i> ret...
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
MDPI AG
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/73c9876e8f6740a1b6c3091fe50cdd29 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | The application of remote sensing data to empirical models of inland surface water chlorophyll-a concentrations (chl-<i>a</i>) has been in development since the launch of the Landsat 4 satellite series in 1982. However, establishing an empirical model using a chl-<i>a</i> retrieval algorithm is difficult due to the spatial heterogeneity of inland lake water properties. Classification of optical water types (OWTs; i.e., differentially observed water spectra due to differences in water properties) has grown in favour in recent years over traditional non-turbid vs. turbid classifications. This study examined whether top-of-atmosphere reflectance observations in visible to near-infrared bands from Landsat 4, 5, 7, and 8 sensors can be used to identify unique OWTs using a guided unsupervised classification approach in which OWTs are defined through both remotely sensed reflectance and surface water chemistry data taken from samples in North American and Swedish lakes. Linear regressions of algorithms (Landsat reflectance bands, band ratios, products, or combinations) to lake surface water chl-<i>a</i> were built for each OWT. The performances of chl-<i>a</i> retrieval algorithms within each OWT were compared to those of global chl-<i>a</i> algorithms to test the effectiveness of OWT classification. Seven unique OWTs were identified and then fit into four categories with varying degrees of brightness as follows: turbid lakes with a low chl-<i>a</i>:turbidity ratio; turbid lakes with a mixture of high chl-<i>a</i> and turbidity measurements; oligotrophic or mesotrophic lakes with a mixture of low chl-<i>a</i> and turbidity measurements; and eutrophic lakes with a high chl-<i>a</i>:turbidity ratio. With one exception (r<sup>2</sup> = 0.26, <i>p</i> = 0.08), the best performing algorithm in each OWT showed improvement (r<sup>2</sup> = 0.69–0.91, <i>p</i> < 0.05), compared with the best performing algorithm for all lakes combined (r<sup>2</sup> = 0.52, <i>p</i> < 0.05). Landsat reflectance can be used to extract OWTs in inland lakes to provide improved prediction of chl-<i>a</i> over large extents and long time series, giving researchers an opportunity to study the trophic states of unmonitored lakes. |
|---|