Time-based remote sensing yield estimates of cotton in water-limiting environments

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.