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背景效應影響無人機圖像估算葉片氮濃度（LNC）。

背景去除削弱了水稻LNC估計中對觀測時間的敏感性。

來自陽光像素的AACIre（AACIre sunlit）優于來自所有像素的AACIre。

AACIre sunlit在綠色像素方面的精度高于SAVI和CIre。 

摘要

背景效應是利用無人機（UAV）多光譜圖像監測作物葉片氮濃度（LNC）的一個關鍵限制。為了提高LNC的估計，已經開發了一些背景去除方法，但在研究中沒有對它們的性能進行比較，也不清楚它們是否對無人機圖像的觀測時間敏感。本研究評估了三種背景去除方法，即土壤調整植被指數法（SAVI）、綠色像素植被指數法（GPVI）和豐度調整植被指數法（AAVI），用于從基于無人機的多光譜圖像中估算水稻在各個生長階段和一天中不同觀測時間的LNC。選擇紅邊葉綠素指數（CIre）作為后兩種方法的共同基礎。特別是，AAVI方法經過了改進，增加了端部構件的數量，實現了端部構件的自動提取，并進一步評估了將光照部位與樹冠陰影部位分離的效果。
 

我們的研究結果表明，非正午觀測時間的植被指數（VIs）與LNC的關系在個體和整個生長階段都優于正午觀測時間的植被指數（VIs）。與SAVI和CIre green相比，AACIre for all pixels（AACIre all）對觀察時間的靈敏度最弱，并且在單階段（接合：r2=0.70，啟動：r2=0.76，標題：r2=0.70）和跨階段（r2=0.66）模型中產生了最佳關系。在三類像素衍生的AAVIs中，AACIre sunlit（R2=0.90，RMSE=0.17%，Bias=0.03%）在LNC估計精度方面顯著優于AACIre all（R2=0.85，RMSE=0.23%，Bias=0.08%）和AACIre shaded（R2=0.38，RMSE=0.49%，Bias=0.40%）。這項研究表明，改進的AAVI方法在減少背景效應、更準確地監測生長參數方面具有重大價值，并可推廣到其他作物和地區，以改進精確的作物管理和基于田間的高通量表型分析。
 

An assessment of background removal approaches for improved estimation of rice leaf nitrogen concentration with unmanned aerial vehicle multispectral imagery at various observation times 

Highlights

Background effect impacted leaf N concentration (LNC) estimation with UAV imagery.

Background removal weaked sensitivity to observation time in rice LNC estimation.

AACIre from sunlit pixels (AACIre-sunlit) outperformed AACIre from all pixels.

AACIre-sunlit yielded higher accuracies than SAVI and the CIre from green pixels.

Abstract

Background effect is a crucial limitation for the monitoring of leaf nitrogen concentration (LNC) in crops with unmanned aerial vehicle (UAV) multispectral imagery. Some background removal approaches have been developed for improve the estimation of LNC, but their performances are not compared in one study and it is unclear whether they are sensitive to the observation time of UAV imagery. This study evaluated three background removal approaches, i.e., the soil-adjusted vegetation index (SAVI) approach, the green pixel vegetation index approach (GPVI) and abundance adjusted vegetation index (AAVI), for estimating rice LNC from UAV-based multispectral imagery at individual and across growth stages as well as different observation times of the day. The red edge chlorophyll index (CIre) was chosen as the common basis for the last two approaches. In particular, the AAVI approach was refined with a higher number of endmembers and automated endmember extraction, and further evaluated for assessing the effect of separating sunlit components from shaded components of the canopy.
 

Our results demonstrated that the vegetation indices (VIs) for off-noon observation times showed better relationships with LNC than those for noon at individual and across growth stages. Compared to both SAVI and CIre-green, the AACIre for all pixels (AACIre-all) exhibited the weakest sensitivity to observation time and yielded the best relationships for single-stage (jointing: r2=0.70, booting: r2=0.76, heading: r2=0.70) and across-stage (r2=0.66) models. Among the AAVIs derived from three categories of pixels, the AACIre-sunlit (R2 =0.90, RMSE=0.17%, Bias=0.03%) outperformed AACIre-all (R2 =0.85, RMSE=0.23%, Bias=0.08%) and then AACIre-shaded (R2 =0.38, RMSE=0.49%, Bias=0.40%) remarkably for the estimation accuracy of LNC. This study suggests that the refined AAVI approach has great value in reducing the background effect for more accurate monitoring of growth parameters and could be extended to other crops and regions for improved precision crop management and field-based high-throughput phenotyping.