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Application of day and night digital photographs for estimating maize biophysical characteristics

机译:日夜数码照片在估算玉米生物物理特性中的应用

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In this study, an inexpensive camera-observation system called the Crop Phenology Recording System (CPRS), which consists of a standard digital color camera (RGB cam) and a modified near-infrared (NIR) digital camera (NIR cam), was applied to estimate green leaf area index (LAI), total LAI, green leaf biomass and total dry biomass of stalks and leaves of maize. The CPRS was installed for the 2009 growing season over a rainfed maize field at the University of Nebraska-Lincoln Agricultural Research and Development Center near Mead, NE, USA. The vegetation indices called Visible Atmospherically Resistant Index (VARI) and two green-red-blue (2g-r-b) were calculated from day-time RGB images taken by the standard commercially-available camera. The other vegetation index called Night-time Relative Brightness Index in NIR (NRBINIR) was calculated from night-time flash NIR images taken by the modified digital camera on which a NIR band-pass filter was attached. Sampling inspections were conducted to measure bio-physical parameters of maize in the same experimental field. The vegetation indices were compared with the biophysical parameters for a whole growing season. The VARI was found to accurately estimate green LAI (R-2 = 0.99) and green leaf biomass (R-2 = 0.98), as well as track seasonal changes in maize green vegetation fraction. The 2g-r-b was able to accurately estimate total LAI (R-2 = 0.97). The NRBINIR showed the highest accuracy in estimation of the total dry biomass weight of the stalks and leaves (R-2 = 0.99). The results show that the camera-observation system has potential for the remote assessment of maize biophysical parameters at low cost.
机译:在这项研究中,使用了一种廉价的相机观测系统,称为作物物候记录系统(CPRS),该系统由标准的数码彩色相机(RGB cam)和改良的近红外(NIR)数码相机(NIR cam)组成。估算玉米茎和叶的绿叶面积指数(LAI),总LAI,绿叶生物量和总干生物量。 CPRS安装在美国内布拉斯加州林德大学附近的内布拉斯加林肯大学农业研究与发展中心的雨育玉米田中,用于2009年生长季节。从标准市售相机拍摄的白天RGB图像计算出植被指数,称为可见大气耐候指数(VARI)和两个绿红蓝(2g-r-b)。另一种植被指数称为NIR的夜间相对亮度指数(NRBINIR),是根据安装了NIR带通滤波器的改良型数码相机拍摄的夜间NIR闪光图像计算得出的。进行了抽样检查,以测量同一实验田中玉米的生物物理参数。将植被指数与整个生长季节的生物物理参数进行比较。发现VARI可以准确估计绿色LAI(R-2 = 0.99)和绿色叶片生物量(R-2 = 0.98),并跟踪玉米绿色植被比例的季节性变化。 2g-r-b能够准确估算总LAI(R-2 = 0.97)。 NRBINIR在估算茎和叶的总干生物量重量方面显示出最高的准确性(R-2 = 0.99)。结果表明,该相机观测系统具有以低成本远程评估玉米生物物理参数的潜力。

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