Empirical relationships for monitoring water quality of lakes and reservoirs through multispectral images

Remote sensing techniques can be used to estimate water quality variables such as chlorophyll\mbi a, total suspended particles, and water transparency. This paper describes empirical algorithms for the estimation of these variables using Landsat Thematic Mapper (TM) data. Ground data were taken from several Spanish lakes covering a variety of trophic statuses, ranging from oligotrophic to hypereutrophic. The studied lakes were the Albufera de Valencia and lakes and ponds of the Southeast Regional Park in Madrid. Empirical equations were obtained to estimate chlorophyll\mbi a from the ratio in reflectance values between bands 2 and 4 of TM ( R
²
\mmb = 0.66, p\lt 0.001), transparency [Secchi disk (SD)] from reflectance in band 2 ( R
²
\mmb = 0.80, pbf \lt 0.001), and total suspended particles from reflectance in band 4 ( R
²
\mmb = 0.92, p\lt 0.001). The spectral equivalence between TM and the recent satellite Deimos-1 was also tested. By applying the proposed algorithms to this new sensor, the temporal resolution is improved by up to 3 days, which increases spatial resolution to 22 m. The algorithms were validated using three Deimos-1 scenes of the Albufera de Valencia together with ground measurements. Results of this validation showed root-mean-square errors (RMSEs) of 40\nbspmg·m
^\mmb-3
for Chl-\mbi a (data range: 32\mmb - 238\nbspmg·m
^-3
), 10\nbspmg·L
^{\mmb -1}
for total suspended solid (TSS) (data range: 25\mmb -89\nbspmg·L
^{\mmb -1}
), and 0.10 m for SD (data range: 0.17-0.40 m). In any case, these results show the potential of Deimos-1 as a substitute of TM in water quality monitoring in small/medium water bodies, providing continuity to three decades of TM imagery.