Advancing Prediction of Pathogenicity of Familial Hypercholesterolemia LDL Receptor Commonest Variants With Machine Learning Models. [PDF]
Santos RD.
europepmc +1 more source
Crop evapotranspiration (ET) is a vital component of the water cycle and energy balance in agricultural ecosystems. Scientifically-based and accurate estimation of regional crop evapotranspiration plays a crucial role in regional agricultural water ...
Xinguo Chen +5 more
doaj +1 more source
Mapping Evapotranspiration of Agricultural Areas in Ghana. [PDF]
Aidoo K +4 more
europepmc +1 more source
This study aimed at estimating actual daily evapotranspiration(ETdaily) in pasture areas at Embrapa Beef Cattle's Experimental Farm, located in the municipality ofCampo Grande, Brazil, by means of the SEBAL algorithm and Landsat 5-TM images.
LOEBMANN, D. G. dos S. W. +7 more
core
Sensitivity Analysis of Hot/Cold Pixel Selection in SEBAL Model for ET Estimation
The objective of this study was to evaluate the sensitivity of instantaneous latent heat flux (LE) estimation from Surface Energy Balance Algorithm for Land (SEBAL) by changing hot/cold pixel selections.
Feng, Leyang
core
Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach. [PDF]
Damásio M +7 more
europepmc +1 more source
Evapotranspiration Estimation with Small UAVs in Precision Agriculture. [PDF]
Niu H +4 more
europepmc +1 more source
Estimating Evapotranspiration of Western Kentucky using SEBAL
Evapotranspiration is a vital aspect of the hydrological cycle, concerning 15% of the atmosphere\u27s water vapor. Landsat ETM+, a USGS satellite, collects multispectal data of the Earth, with a return interval of 16 days.
Collett, Steven
core +1 more source
Application of time-lagged satellite image-based crop coefficients for estimating actual evapotranspiration through FAO-56 method. [PDF]
Moazenzadeh R.
europepmc +1 more source
Estimation of Landsat-like daily evapotranspiration for crop water consumption monitoring using TSEB model and data fusion. [PDF]
Chen D, Zhuang Q, Zhang W, Zhu L.
europepmc +1 more source

