The use of high-throughput phenotyping aids breeding programs in making more informed selections and advancements. This study's objectives were to determine which proximal remote sensing parameters (normalized difference red edge [NDRE], normalized difference vegetation index [NDVI], difference between canopy and air temperatures [∆T], and plant height) are robust estimators of cotton lint yield and to use a time-integrated function of one parameter as a single phenotypic measurement for predicting yield. This study evaluated remote sensing parameters (NDRE, NDVI, ∆T, and plant height) measured weekly from squaring through boll production and development. Of these measurements, NDRE was most consistent in terms of r2, slope, and normality in predicting yield. From these findings, a temporal analysis was calculated as NDRE integrated over the season, namely NDRE-days. Significant r2 values were detected for the individual remote sensing measurements, with the largest r2 occurring around peak bloom (80 d after planting). An r2 of 0.81 was identified between ∆T and lint yield in 2015, whereas in 2017 the largest r2 value with lint yield was with NDRE (r2=.71). The temporal analysis showed a significant relationship between NDRE-days and lint yield (P <.0001; r2=.58 in 2015 and r2=.68 in 2017) that was not cultivar specific. This study presents a suitable method that breeders could use to efficiently evaluate plant growth and estimate yield for variety selections while cutting resource requirements.