Improving in-season estimation of rice yield potential and responsiveness to topdressing nitrogen application with Crop Circle active crop canopy sensor

Abstract

In-season site-specific nitrogen (N) management is a promising strategy to improve crop N use efficiency and reduce risks of environmental contamination. To successfully implement such precision management strategies, it is important to accurately estimate yield potential without additional topdressing N application (YP 0 ) as well as precisely assess the responsiveness to additional N application (RI) during the growing season. Previous research has mainly used normalized difference vegetation index (NDVI) or ratio vegetation index (RVI) obtained from GreenSeeker active crop canopy sensor with two fixed bands in red and near-infrared (NIR) spectrums to estimate these two parameters. The development of three-band Crop Circle active sensor provides a potential to improve in-season estimation of YP 0 and RI. The objectives of this study were twofold: (1) identify important vegetation indices obtained from Crop Circle ACS-470 sensor for estimating rice YP 0 and RI; and (2) evaluate their potential improvements over GreenSeeker NDVI and RVI. Four site-years of field N rate experiments were conducted in 2012 and 2013 at the Jiansanjiang Experiment Station of China Agricultural University located in Northeast China. The GreenSeeker and Crop Circle ACS-470 active canopy sensor with green, red edge, and NIR bands were used to collect rice canopy reflectance data at different key growth stages. The results indicated that both the GreenSeeker (best R 2 = 0.66 and 0.70, respectively) and Crop Circle (best R 2 = 0.71 and 0.77, respectively) sensors worked well for estimating YP 0 and RI at the stem elongation stage. At the booting stage, Crop Circle red edge optimized soil adjusted vegetation index (REOSAVI, R 2 = 0.82) and green ratio vegetation index (R 2 = 0.73) explained 26 and 22 % more variability in YP 0 and RI, respectively, than GreenSeeker NDVI or RVI. At the heading stage, the GreenSeeker sensor indices became saturated and consequently could not be used for YP 0 or RI estimation, while Crop Circle REOSAVI and normalized green index could still explain more than 70 % of YP 0 and RI variability. It is concluded that both sensors performed similarly at the stem elongation stage, but significantly better results were obtained by the Crop Circle sensor at the booting and heading stages. Furthermore, the results revealed that Crop Circle green band-based vegetation indices performed well for RI estimation while the red edge-based vegetation indices were the best for estimating YP 0 at later growth stages.

Use of Normalized Difference Vegetation Index to Assess N Status and Predict Grain Yield in Rice

“…3b). This is similar to Cao et al (2016), who found a comparable correlation ( r 2 = 0.63) in their experiments at a single location. Others (e.g., Harrell et al, 2011; Yao et al, 2012) have reported a poorer relationship between NDVI and grain yield with r 2 values ranging from 0.36 to 0.44.…”

“…Comparatively, there have been relatively few such studies in rice. Some have tested the ability of NDVI to assess rice N status (Zhu et al, 2007; Gnyp et al, 2014; Yao et al, 2014; Lu et al, 2017) and few have used NDVI to develop in‐season yield predictions (Harrell et al, 2011; Yao et al, 2012; Cao et al, 2016). However, most of these studies have focused their research on single sites, leaving at question the scalability of their findings to other sites representing different soils and management practices.…”

Potential of RapidEye and WorldView-2 Satellite Data for Improving Rice Nitrogen Status Monitoring at Different Growth Stages

“…However, the NDVI may saturate under moderate-to-high biomass conditions at later growth stages [28][29][30]. The RE-based VIs have been proven to be sensitive to crop canopy chlorophyll and N variation, even under the high biomass condition [7,[31][32][33][34]. Since both RapidEye and WorldView-2 have the RE band, the question of how RE-based indices could improve the estimation of rice N status needed to be answered.…”

Coefficient of Variation from Vegetation Index for Sugarcane Population and Stalk Evaluation

“…The CV 710 and CV 735 values were higher than CV 660 . Several studies showed the advantage of using red‐edge–based VIs for monitoring plant crop growth (Cho and Skidmore, 2006; Peng and Gitelson, 2012; Cao et al, 2015). Red‐edge band sits between the red to near infrared bands (680–740 nm) where light energy is strongly absorbed by chlorophyll pigments and at the same time highly reflected by leaf structure.…”

“…Reports in the literature indicated the use of red‐edge–based VIs improved the monitoring of plant conditions, especially as plants approached maturity and complete canopy closure (Mutanga and Skidmore, 2004; Cao et al, 2015). In the current study red‐edge VIs and their CVs had strong negative relationships at the early tillering stage (Fig.…”