The Enhanced Vegetation Index (EVI) is a key Earth science parameter used to assess vegetation, originally developed and calibrated for the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. With the impending decommissioning of the MODIS sensors by the year 2020/2022, alternative platforms will need to be used to estimate EVI. We compared Landsat 5 (2000–2011), 8 (2013–2016) and the Visible Infrared Imaging Radiometer Suite (VIIRS; 2013–2016) to MODIS EVI (2000–2016) over a 420,083-ha area of the arid lower Colorado River Delta in Mexico. Over large areas with mixed land cover or agricultural fields, we found high correspondence between Landsat and MODIS EVI (R² = 0.93 for the entire area studied and 0.97 for agricultural fields), but the relationship was weak over bare soil (R² = 0.27) and riparian vegetation (R² = 0.48). The correlation between MODIS and Landsat EVI was higher over large, homogeneous areas and was generally lower in narrow riparian areas. VIIRS and MODIS EVI were highly similar (R² = 0.99 for the entire area studied) and did not show the same decrease in performance in smaller, narrower regions as Landsat. Landsat and VIIRS provide EVI estimates of similar quality and characteristics to MODIS, but scale, seasonality and land cover type(s) should be considered before implementing Landsat EVI in a particular area.

Key Points: Asynchrony in phenology metrics derived from optical, microwave, and fluorescence satellite data increases as the growing season progresses Globally, peak‐/end‐of‐season phenology metrics are temporally organized such that fluorescence < optical < microwave Integration of these independent satellite‐based indices reveals vegetation senescence as a prolonged, complex, and well‐organized process

Declines in riparian ecosystem greenness and water use have been observed in the delta of the Lower Colorado River (LCR) since 2000. The purpose of our case study was to measure these metrics on the U.S. side of the border between Hoover and Morelos Dams to see if declining greenness was unique to the portion of the river in Mexico. In this case study, five riparian reaches of the LCR from Hoover to Morelos Dam since 2000 were studied to evaluate trends in riparian ecosystem health. We measure these riparian woodlands using remotely sensed measurements of the two-band Enhanced Vegetation Index (EVI2; a proxy for greenness); daily evapotranspiration (ET; mmd⁻¹) using EVI2 (ET(EVI2)); and an annualized ET based on EVI2, the Phenology Assessment Metric (PAM ET), an annualized ET using Landsat time-series. A key finding is that riparian health and its water use has been in decline since 2000 on the U.S. portion of the LCR, depicting a loss of green vegetation over the last two decades. EVI2 results show a decline of −13.83%, while average daily ET(EVI2) between the first and last decade had a decrease of over 1 mmd⁻¹ (−27.30%) and the respective average PAM ET losses were 170.91 mmyr⁻¹ (−17.95%). The difference between the first and last five-year periods, 2000–2005 and 2016–2020, showed the largest decrease in daily ET(EVI) of 1.24 mmd⁻¹ (−32.61%). These declines come from a loss in healthy, green, riparian plant-cover, not a change in plant water use efficiency nor efficient use of managed water resources. Our results suggest further deterioration of biodiversity, wildlife habitat and other key ecosystem services on the U.S. portion of the LCR.

The droughts striking the Colorado Plateau, where the Hopi Tribe and Navajo Nation Native American reservation lands are located, and their impacts have appeared slowly and relatively unnoticed in conventional national drought monitoring efforts like the National Drought Monitor. To understand the effect of drought-based drivers on vegetation productivity in the Hopi Tribe and Navajo Nation reservation lands, an assessment approach was developed integrating climate, land cover types, and topographical data with annual geospatially explicit normalized difference vegetation index (NDVI)-related productivity from 1989 to 2014 derived from 15-day composite multi-sensor NDVI time series data. We studied vegetation–environment relationships by conducting multiple linear regression analysis to explain the driver of vegetation productivity changes. Our results suggest that the interannual change of vegetation productivity showed high variability in middle elevations where needleleaf forest is the dominant vegetation cover type. Our analysis also shows that the spatial variation in interannual variability of vegetation productivity was more driven by climate drivers than by topography ones. Specifically, the interannual variability in spring precipitation and fall temperature seems to be the most significant factor that correlated with the interannual variability in vegetation productivity during the last two and a half decades.