The conventional Food and Agriculture Organization of the United Nations (FAO) Penman–Monteith (P-M) equation requires knowledge of the available energy to estimate reference evapotranspiration (ETo); however, it is common to ignore the minor energy components (MECs). This study was conducted to determine the effect of not including the MECs in the FAO P-M equation. Fifteen-min micrometeorological records of four sites (covered with corn, soybeans, and prairie) in central Iowa, USA, during the years 2007–2012 were investigated. The major/minor components of the energy equation were either measured or estimated by in-situ eddy covariance instruments. It was discovered that, on average, the MECs accounted for at least 13% of daily net radiation, leading to 27% decrease in daily ETo. Therefore, an equation is introduced to estimate MECs as a function of net radiation, air temperature, and Monin–Obukhov length; and another equation is regressed to roughly estimate daily MECs as a function of net radiation and day of the year. The findings in this study suggest a fundamental modification of FAO P-M formula by considering the inclusion of MECs in the energy term.

Estimation of daily evapotranspiration (ET) over cloudy regions highly desires models which rely on meteorological data only. Notwithstanding, the conventional crop coefficient (K c) method requires detailed knowledge of geo/biophysical properties of the coupled land-vegetation system, precipitation, and soil moisture. Six Eddy Covariance (EC) towers in Iowa, California and New Hampshire of the USA (covering corn, soybeans, prairie, and deciduous forest) were selected. Investigation on 6 years (2007-2012) 15-min micrometeorological records of these sites revealed that there is an indubitable strong interaction between relative humidity (RH), reference ET (ET o), and actual ET at different timescales. This allowed to bypass the need for the non-meteorological inputs and express K c as a second-order polynomial function of RH and ET o , the ambient regression evapotranspiration model (AREM). The coefficients of the empirical function are crop-specific and may require calibration over different soil types. The mean absolute percentage error (MAPE) of the regression against daily EC observations was 17% during the growing season, and 32% throughout the year with root mean square error (RMSE) of 0.74 mm day −1 and coefficient of determination of 0.71. The model was fully operational (MAPE of 34% and RMSE of 0.82 mm day −1) over the four Iowan sites based on inputs from local weather stations and NLDAS-2 forcing data of NASA. AREM was capable of capturing the dynamics of ET at 15-min and daily timescales irrespective of varying complexities associated with biophysical, geophysical and climatological states.

There are numerous applications that require crop classification as early as possible in the growing season. However, information about land cover from official land cover maps of the United States (cropland data layer [CDL] maps by the National Agricultural Statistics Service) are generally not available until after harvest. In the Upper Midwest, the primary rotating crops are corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] (covering ∼63% of Iowa) with an irregular annual rotation. This study investigated the feasibility of early‐season classification of corn and soybean fields in Iowa by comparing the current and previous years’ 30‐m 16‐d Landsat 8 images during the growing season to produce normalized difference vegetation index (NDVI) maps, along with the last‐updated CDL land cover, to construct “agricultural units.” We assigned a geometric weight to each unit by performing Bayesian discriminant analysis using the concept of a sliding threshold to categorize pixels. An examination of 8 yr of 250‐m 16‐d NDVI measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) over Iowa showed that late June is the most promising time for categorization. The geometrical model was tested on a 24‐ by 28‐km2 region in southwestern Iowa on 1 July 2014 (Day of the Year 182). There was an 86% agreement with the CDL data set (88 and 83% for corn and soybean, respectively, in the confusion matrix). This demonstrates that in spite of the complexity of crop behavior, a geometrical approach integrating probabilistic methods, previous statistical records, and map disaggregation into agricultural units can be a promising method for early‐season crop classification. Classification of corn and soybeans in Iowa is important early in the season. Bayesian discriminant analysis and field geometry were combined with a sliding threshold. This geometrical approach is a promising method for early‐season crop classification.

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