A MODIS approach to predicting stream water quality in Wisconsin

Efforts to predict water quality often rely heavily on static landuse/land-cover (LULC) classifications derived from remote sensing imagery. However, LULC classifications are infrequently updated, and the development of regular (annual) land cover maps that accurately capture intra- and inter-annual change may be expensive in terms of data and labor costs. In addition, existing land cover products may not include classes relevant to the assessment of water quality. Conversely, data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments are freely available, preprocessed, frequently updated, and available in a range of useful data products. Our goal was to predict water quality for streams in Wisconsin, USA using a model constructed exclusively from MODIS products. We used streamwater nitrate and dissolved phosphorus data from 2001 to 2004 from two previous studies (Robertson et al., 2006 and Stanley and Maxted, 2008) to evaluate the potential for using MODIS in an empirical model to predict water quality, thereby reducing reliance on possibly dated land cover maps. Using predictors derived exclusively from MODIS data products, we successfully predicted 80% of the variation in measured nitrate concentrations. Remote sensing based models explained 51% of the variation in dissolved phosphorus concentrations. Predictions of water quality were developed on both a per-pixel and a watershed basis. The remote sensing approach offers the potential for spatially continuous estimates of stream nutrient concentrations, and complements the suite of approaches currently used to assess water quality; including periodic stream surveys models, and point-source data.

► We predict stream nitrate and dissolved phosphorus concentrations using MODIS.
► Models for a year are developed using MODIS data from the previous year.
► Models are dynamic and do not rely on static land cover maps.
► Models predict 80% of observed NO3-N and 51% of dissolved P concentrations.
► A new algorithm to derive the most parsimonious PLS regression model is described.