Page Banner

United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES Title: Soil heat flux variability influenced by row direction in irrigated cotton

Authors
item Agam, Nurit -
item Kustas, William
item Evett, Steven
item Colaizzi, Paul
item Cosh, Michael
item McKee, Lynn

Submitted to: Advances in Water Resources
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: July 6, 2012
Publication Date: November 29, 2012
Citation: Agam, N., Kustas, W.P., Evett, S.R., Colaizzi, P.D., Cosh, M.H., McKee, L.G. 2012. Soil heat flux variability influenced by row direction in irrigated cotton. Advances in Water Resources. 50:31-40.

Interpretive Summary: In temperate humid climates, where 100% vegetation cover is common, soil heat flux (G) is a relatively small component of earth’s surface energy balance. Since micro-meteorological techniques for estimating surface energy balance and evapotranspiration (ET) were initially developed in temperate regions, G was not considered to be a critical component. However, under partial vegetation cover conditions, particularly for row crops in arid environments, G may reach 50% of total net radiation and even for taller canopies, can account for 30-50% of net radiation. Therefore, inaccurate estimates of G may lead to non-negligible errors in the surface energy balance and ET estimation, adversely affecting irrigation scheduling and water resource assessments in water-limited agricultural areas. In this study the effect of row orientation and sensor position on G magnitude and variability in an irrigated row crop of cotton is reported. In addition, the effect of errors in water content estimation on the heat storage at the uppermost soil layer is assessed. The research was conducted in the Southern High Plains of the USA, as part of the Bushland Evapotranspiration and Agricultural Remote Sensing Experiment of 2008 (BEAREX08). Measurements were concentrated in two irrigated cotton fields, one with north-south (N-S) and the other with east-west (E-W) row orientations. It was found that using properly calibrated soil moisture sensors reduced errors from 27% to 9%. In addition, row orientation had an effect on the temporal trace in G and when summed over the daytime period. Important short-term (15-min average) variability in G at the various locations in the interrow was observed under partial canopy cover conditions for the N-S row orientation, while the daily sum of G () in both row orientations was similar. In the beginning and the end of the growing season, was larger in the N-S orientation field. In the E-W orientation field, a minimum of 3 sensor sets (as are often deployed at ET measurement installations) were found to adequately describe the G pattern of the 10-location average, with errors as small as 6% and with a transitory maximum error of 12%. In the N-S row orientation field, however, no 3 sensor combination was adequate to represent the field average G. This suggests that reliable surface energy balance and ET estimation for row crops requires a more extensive array of soil heat flux measurements, particularly for N-S oriented row crops.

Technical Abstract: In applications of micrometeorological techniques for surface energy balance estimation, most often the least attention and effort has been devoted to determining the area-average soil heat flux (G). Although spatial and temporal variability in G under sparse/clumped vegetation conditions is significant and has been studied, less attention has been devoted to evaluating the variability of G with respect to row crops, particularly with respect to row orientation. The variation in G for row crops is related to the effect of differential shading of the soil surface, which is dependent on plant architecture, row spacing, and row orientation/direction. The effect of the latter on G variability, to the authors’ knowledge has not been previously studied. In this study the effect of row orientation and sensor position on G magnitude and variability in an irrigated row crop of cotton is reported. In addition, the effect of errors in water content estimation on the heat storage at the uppermost soil layer is assessed. The research was conducted in the Southern High Plains of the USA, as part of the Bushland Evapotranspiration and Agricultural Remote Sensing Experiment of 2008 (BEAREX08). Measurements were concentrated in two irrigated cotton fields, one with north-south (N-S) and the other with east-west (E-W) row orientations. In each field, 10 sets of sensors in two replicates at 5 locations across the interrow (76 cm row spacing) were used to quantify G positional variability. In each location, a sensor set consisted of a soil heat flux plate installed at a depth of 80 mm, above which thermocouples were installed at 60, 20 and 0 mm depths. Average field volumetric soil water content (W) was measured with Hydra Probe sensors (W_hp) and in the NE field conventional time domain reflectrometry was also used to assess soil water content (W_tdr). Correcting W_hp to the local soil improved G computations, and reduced the overestimation from 27% for the uncorrected to 9% when corrected W_hp was used. Row orientation had an effect on the temporal trace in G and when summed over the daytime period. Important short-term (15-min average) variability in G at the various locations in the interrow was observed under partial canopy cover conditions for the N-S row orientation, while the daily sum of G (<G>) in both row orientations was similar. In the beginning and the end of the growing season, <G> was larger in the N-S orientation field. In the E-W orientation field, 3-replicate sensor sets (as are often deployed at flux tower installations) were found to adequately describe the G pattern of the 10-location average, with errors as small as 6% and with a transitory maximum error of 12%. In the N-S row orientation field, however, no 3-replicate combination was adequate to represent the field average G.

Last Modified: 10/30/2014