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 r(2), slope, and normality in predicting yield. From these findings, a temporal analysis was calculated as NDRE integrated over the season, namely NDRE-days. Significant r(2) values were detected for the individual remote sensing measurements, with the largest r(2) occurring around peak bloom (80 d after planting). An r(2) of 0.81 was identified between T and lint yield in 2015, whereas in 2017 the largest r(2) value with lint yield was with NDRE (r(2 )= .71). The temporal analysis showed a significant relationship between NDRE-days and lint yield (P < .0001; r(2 )= .58 in 2015 and r(2 )= .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.