Dualex植物多酚-叶绿素仪：从Sentinel-2光谱带反演冠层叶绿素含量以估算冬小麦集约种植系统的氮素吸收

品质至上，客户至上，您的满意就是我们的目标

返回旧版

技术文章

当前位置: 首页 > 技术文章

Dualex植物多酚-叶绿素仪：从Sentinel-2光谱带反演冠层叶绿素含量以估算冬小麦集约种植系统的氮素吸收

发表时间：2022-05-05 13:45:05点击：1205

来源：北京博普特科技有限公司

Dualex是一款源自于法国国家科学院 (CNRS)及巴黎第十一大学技术，由奥地利PESSL公司生产（原法国Force-A公司）开发的新型多功能叶片测量仪。它可同时准确测量叶片的叶绿素含量、叶片表层的类黄酮和花青素含量，适用于植物生理学和农学（如水稻叶绿素浓度，玉米氮素状况，葡萄藤等）相关研究。

其测量对象可以是单子叶植物，双子叶植物或多年生植物。这款设备简单易用，可进a行实时和非破坏性测量。由于不需要校准标定和事先的样品制备，测量工作可在实验室或现场完成。此外，该设备在各种温度的和环境光照条件下均可进行简单、快速、无损测量叶片中的叶绿素、多酚以及花青素。

Dualex便携式智能气象-叶绿素-花青素-黄酮醇测量仪

热点

小麦含氮量只能用哨-21的10m波段

在冠层水平上准确估计了小麦氮含量。

像素内的异质性影响GAI估计精度。

红边带增加了生长季节的GAI估计精度。

摘要

在农业系统中，减少氮输入是最常见的调整环境氮输入的方法之一。这种调整通常与基于作物冠层（分别为NC和CNC）中临界和实际吸收氮（kg/ha）概念的氮营养指数（NNI）有关。因此，基于免费提供的卫星图像，在大面积范围内准确估计NC和CNC是需要解决的关键问题。本研究基于农民田地的大量数据集，强调了高度相关性（R2） = 用人工神经网络（ANN）从Sentinel-2（S2）反演的小麦CNC和冠层叶绿素含量（CCC）之间的差异为0.90）。估计值与4和21kg/ha（取决于生长阶段）的误差有关，这是评估NNI的一个有前景的结果。这个结果有四个主要结果：（i）在树冠水平上工作的重要性；（ii）与所考虑品种的关系的独立性；（iii）关系对成长阶段的依赖性；以及（iv）仅使用10个 M S2波段，为精准农业开辟道路。同时，研究了与CNC和NC相关的三个生物物理变量（BV）的估计精度，即绿地指数（GAI）、叶片叶绿素含量（Cab）和CCC。从这一分析中，对红边带的附加值进行了量化，以改进对3个感兴趣BV的估计，以及与现场异质性相关的性能降低。

关键词：氮含量，冠层叶绿素含量，人工神经网络，红边带，哨兵-2，缩放问题，集约种植制度

Retrieval of the canopy chlorophyll content from Sentinel-2 spectral bands to estimate nitrogen uptake in intensive winter wheat cropping systems

Highlights

The wheat nitrogen content can be estimated only with the 10 m bands of Sentinel-2.

The wheat nitrogen content is accurately estimated at the canopy level.

Within-pixel heterogeneity affects the GAI estimation accuracy.

The red-edge bands increase the GAI estimation accuracy along the growing season.

Abstract

One of the most common approaches to reducing the environmental impact of nitrogen (N) fertilisation in intensive agrosystems is to adjust the N input of the crop requirement. This adjustment is frequently related to the nitrogen nutrition index (NNI) based on the concepts of the critical and actual N absorbed (kg/ha) in the crop canopy (respectively, NC and CNC). Accurate estimation of the NC and CNC at the field scale over large areas based on freely available satellite imagery is thus a key issue to address. Relying on a large dataset of farmers' fields, this study highlights the high correlation (R2 = 0.90) between the wheat CNC and canopy chlorophyll content (CCC) retrieved from Sentinel-2 (S2) with an Artificial Neural Network (ANN). The estimation is related to errors of 4 and 21 kg/ha (depending on the growing stage), which is a promising result for evaluating the NNI. There are four major outcomes from this result: (i) the importance of working at the canopy level; (ii) the independence of the relationship to the considered cultivars; (iii) the dependence of the relationship on the growing stage; and (iv) the potential to use only the 10 m S2 bands, opening the way for precision agriculture. In parallel, estimation accuracies were investigated for the three biophysical variables (BV) related to the CNC and NC, i.e., the green area index (GAI), leaf chlorophyll content (Cab) and CCC. From this analysis, the added value of the red-edge bands for improving the estimation of the 3 BVs of interest was quantified as was the performance reduction related to the field heterogeneity.

Keywords

Nitrogen content

Canopy chlorophyll content

Artificial neural network

Red-edge bands

Sentinel-2

Scaling issue

Intensive cropping systems

相关阅读

Dualex 便携式智能气象-叶绿素-花青素-黄酮醇测量仪

PESSL便携式便携式植物多酚-叶绿素仪原理

Dualex植物多酚-叶绿素仪：腐殖酸通过铁依赖和非铁依赖的协同机制缓解禾本科植物的铁失绿症