On Parameterizing Soil Evaporation in a Direct Remote Sensing Model of ET: PT‐JPL

MARSHALL, MICHAEL; TU, KEVIN; ANDREO, VERONICA

WATER RESOURCES RESEARCH

AMER GEOPHYSICAL UNION

Año: 2020 vol. 56

0043-1397

Remote sensing models that measure evapotranspiration directly from the Penman-Monteith or Priestley-Taylor equations typically estimate the soil evaporation component over large areas using coarse spatial resolution relative humidity (RH) from geospatial climate datasets. As a result, the models tend to underperform in dry areas at local scales where moisture status is not well represented by surrounding areas.Earth observation sensors that monitor large-scale global dynamics (e.g., MODIS) afford comparable spatial coverage and temporal frequency, but at a higher spatial resolution than geospatial climate datasets. In thisstudy, we compared soil evaporation parameterized with optical and thermal indices derived from MODIS to RH-based soil evaporation as implemented in the Priestley Taylor-Jet Propulsion Laboratory (PT-JPL)model. We evaluated the parameterizations by subtracting PT-JPL transpiration from observation-based flux tower evapotranspiration in agricultural fields across the contiguous United States. We compared theapparent thermal inertia (ATI) index, land surface water index (LSWI), normalized difference water index (NDWI), and a new index derived from red and shortwave infrared bands (soil moisture divergence index[SMDI]). Relationships were significant at the 95% confidence band. LSWI and SMDI explained 18?33% of variance in 8-day soil evaporation. This led to a 3?11% increase in explained ET variance. LSWI and SMDItended to perform better at the irrigated sites than RH. LSWI and SMDI led to markedly better performance over other indices at a seasonal time step. L-band microwave backscatter can penetrate clouds and candistinguish soil from canopy moisture content. We are presently fusing red-SWIR-RADAR to improve soil evaporation estimation