Investigating water use over the Choptank River Watershed using a multisatellite data fusion approach

Abstract

The health of the Chesapeake Bay ecosystem has been declining for several decades due to high levels of nutrients and sediments largely tied to agricultural production systems. Therefore, monitoring of agricultural water use and hydrologic connections between crop lands and Bay tributaries has received increasing attention. Remote sensing retrievals of actual evapotranspiration (ET) can provide valuable information in support of these hydrologic modeling efforts, spatially and temporally describing consumptive water use by crops and natural vegetation and quantifying response to expansion of irrigated area occurring with Bay watershed. In this study, a multisensor satellite data fusion methodology, combined with a multiscale ET retrieval algorithm, was applied over the Choptank River watershed located within the Lower Chesapeake Bay region on the Eastern Shore of Maryland, USA to produce daily 30 m resolution ET maps. ET estimates directly retrieved on Landsat satellite overpass dates have high accuracy with relative error (RE) of 9%, as evaluated using flux tower measurements. The fused daily ET time series have reasonable errors of 18% at the daily time step ‐ an improvement from 27% errors using standard Landsat‐only interpolation techniques. Annual water consumption by different land cover types was assessed, showing reasonable distributions of water use with cover class. Seasonal patterns in modeled crop transpiration and soil evaporation for dominant crop types were analyzed, and agree well with crop phenology at field scale. Additionally, effects of irrigation occurring during a period of rainfall shortage were captured by the fusion program. These results suggest that the ET fusion system will have utility for water management at field and regional scales over the Eastern Shore. Further efforts are underway to integrate these detailed water use data sets into watershed‐scale hydrologic models to improve assessments of water quality and inform best management practices to reduce nutrient and sediment loads to the Chesapeake Bay.

Field-Scale Assessment of Land and Water Use Change over the California Delta Using Remote Sensing

“…In brief, STARFM compares MODIS and Landsat image pairs on days when both are available, computes spatial weighting statistics, then applies these weights to downscale MODIS images between clear-sky Landsat overpasses. STARFM was originally developed to fuse surface reflectance imagery, but has also been applied successfully to ET datasets developed over a variety of agricultural and forested landscapes in the United States [20,[41][42][43][44][45] and in Spain [46]. …”

The Grape Remote Sensing Atmospheric Profile and Evapotranspiration Experiment

“…18). Comparison of retrieved Landsat LAI with ground LAI measurements in the north and south vineyards from 2013 to 2016 yielded an MAE of 0.44 and an MAPE of ~25% (Sun et al 2017a). An example of the time series in daily LAI estimated from Sun et al (2017a) versus the LAI ground sampling in 2014 near the flux towers indicates good agreement (Fig.…”

“…4b). ET fusion experiments in different land-cover types are described by Cammalleri et al (2013Cammalleri et al ( , 2014, Semmens et al (2016), Yang et al (2017a,b), and Sun et al (2017b).…”

“…Leaf area index is a key input to TSEB (as well as many other land surface models) and a quantity associated with many biophysical applications (Myneni et al 2002). Seasonal maps of LAI may also be useful for estimating grape yield in vineyards (Sun et al 2017a). A machine learning approach (Gao et al 2012) was applied to generate daily LAI maps at 30-m resolution over the GRAPEX field sites using Landsat surface reflectance and the MODIS LAI products.…”

“…2). The upscaling of sap-flow measurement to canopy level is challenging and will use LAI data collected in situ along with remote sensing-based estimates of daily LAI described below (Sun et al 2017a). Transpiration estimates from sap-flow data will be compared to estimates from a new micrometeorological technique using turbulence data from eddy covariance flux towers that provide E and T at field scale.…”