Dryland river basins frequently support both irrigated agriculture and riparian vegetation and remote sensing methods are needed to monitor water use by both crops and natural vegetation in irrigation districts. We developed an algorithm for estimating actual evapotranspiration (ET_a) based on the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectrometer (MODIS) sensor on the EOS-1 Terra satellite and locally-derived measurements of reference crop ET (ET_o). The algorithm was calibrated with five years of ET_a data from three eddy covariance flux towers set in riparian plant associations on the upper San Pedro River, Arizona, supplemented with ET_a data for alfalfa and cotton from the literature. The algorithm was based on an equation of the form ET_a = ET_o [a(1 − e^−bEVI) − c], where the term (1 − e^−bEVI) is derived from the Beer-Lambert Law to express light absorption by a canopy, with EVI replacing leaf area index as an estimate of the density of light-absorbing units. The resulting algorithm capably predicted ET_a across riparian plants and crops (r² = 0.73). It was then tested against water balance data for five irrigation districts and flux tower data for two riparian zones for which season-long or multi-year ET_a data were available. Predictions were within 10% of measured results in each case, with a non-significant (P = 0.89) difference between mean measured and modeled ET_a of 5.4% over all validation sites. Validation and calibration data sets were combined to present a final predictive equation for application across crops and riparian plant associations for monitoring individual irrigation districts or for conducting global water use assessments of mixed agricultural and riparian biomes.

Monitoring water surface dynamics is essential for the management of lakes and reservoirs, especially those are intensively impacted by human exploitation and climatic variation. Although modern satellites have provided a superior solution over traditional methods in monitoring water surfaces, manually downloading and processing imagery associated with large study areas or long-time scales are time-consuming. The Google Earth Engine (GEE) platform provides a promising solution for this type of “big data” problems when it is combined with the automatic water extraction index (AWEI) to delineate multi-temporal water pixels from other forms of land use/land cover. The aim of this study is to assess the performance of a completely automatic water extraction framework by combining AWEI, GEE, and Landsat 8 OLI data over the period 2014–2018 in the case study of New Zealand. The overall accuracy (OA) of 0.85 proved the good performance of this combination. Therefore, the framework developed in this research can be used for lake and reservoir monitoring and assessment in the future. We also found that despite the temporal variability of climate during the period 2014–2018, the spatial areas of most of the lakes (3840) in the country remained the same at around 3742 km². Image fusion or aerial photos can be employed to check the areal variation of the lakes at a finer scale.

Remote sensing by satellite-borne sensors presents a significant opportunity to enhance the spatio-temporal coverage of environmental monitoring programmes for lakes, but the estimation of classic water quality attributes from inland water bodies has not reached operational status due to the difficulty of discerning the spectral signatures of optically active water constituents. Determination of water colour, as perceived by the human eye, does not require knowledge of inherent optical properties and therefore represents a generally applicable remotely-sensed water quality attribute. In this paper, we implemented a recent algorithm for the retrieval of colour parameters (hue angle, dominant wavelength) and derived a new correction for colour purity to account for the spectral bandpass of the Landsat 8 Operational Land Imager (OLI). We used this algorithm to calculate water colour on almost 45,000 observations over four years from 1486 lakes from a diverse range of optical water types in New Zealand. We show that the most prevalent lake colours are yellow-orange and blue, respectively, while green observations are comparatively rare. About 40% of the study lakes show transitions between colours at a range of time scales, including seasonal. A preliminary exploratory analysis suggests that both geo-physical and anthropogenic factors, such as catchment land use, provide environmental control of lake colour and are promising avenues for future analysis.