欢迎访问《生态学杂志》官方网站,今天是 分享到:

生态学杂志 ›› 2022, Vol. 41 ›› Issue (3): 562-568.doi: 10.13292/j.1000-4890.202202.029

• 研究报告 • 上一篇    下一篇

基于地面高光谱和GF-1卫星的区域花生LAI估算

李军玲1*,李梦夏1,陈争2     

  1. 1中国气象局·河南省农业气象保障与应用技术重点实验室, 河南省气象科学研究所, 郑州 450003;2开封市气象局, 河南开封 475400)
  • 出版日期:2022-03-10 发布日期:2022-03-11

Estimation of regional peanut LAI based on terrestrial hyperspectrum and GF-1 satellite.

LI Jun-ling1*, LI Meng-xia1, CHEN Zheng2   

  1. (1Henan Key Laboratory of Agrometeorological Ensuring and Applied Technique, China Meteorological Administration/Henan Institute of Meteorological Sciences, Zhengzhou 450003, China; 2Kaifeng Meteorological Bureau, Kaifeng 475400, Henan, China).
  • Online:2022-03-10 Published:2022-03-11

摘要: 掌握作物叶面积指数(LAI)及其动态变化对于作物生长监测和估产等有重要意义。利用地面高光谱数据进行作物生长参数的反演是农业遥感研究的热点,但其中大多是利用地面高光谱数据建立作物LAI的估算模型研究,难以进行区域化应用。为把地面高光谱研究结果应用到卫星尺度,实现区域花生LAI的反演,从而对大面积花生长势进行监测,本文利用GF1卫星传感器的光谱响应函数和地面高光谱数据,在试验站小区试验和大田试验基础上,基于地面观测光谱数据构建多种宽波段光谱指数,建立基于高光谱指数的花生LAI遥感估算模型。通过比较估算模型的决定系数和验证精度,认为基于RVI指数建立的模型(LAI=0.481RVI0.830)是LAI估算的最佳模型。基于最优模型进行花生LAI遥感制图,获得花生LAI分布情况。利用野外试验观测数据验证遥感反演LAI精度,结果表明,利用宽波段指数和GF-1适用于花生LAI估算,对今后进行大面积花生长势监测有重要意义。

关键词: 光谱响应函数, 高分一号卫星, 叶面积指数, 宽波段指数

Abstract: Examining crop leaf area index (LAI) and its dynamic change is of great significance for crop growth monitoring and yield estimation. The inversion of crop growth parameters based on terrestrial hyperspectral data is a hotspot in agricultural remote sensing research. However, most previous studies used terrestrial hyperspectral data to establish the estimation model of crop LAI, which is difficult for regional application. In order to apply the terrestrial hyperspectral research results to the satellite scale and realize the inversion of regional peanut LAI, thereby to monitor the largearea peanut growth, we constructed a variety of wideband spectral indices based on the terrestrial observation spectrum data, and built a remote sensing peanut LAI estimation model based on the hyperspectral index. The hyperspectral index was constructed with the GF-1 satellite sensor spectral response function and the terrestrial hyperspectral data on the basis of plot test and field test of the observation station. By comparing the determination coefficient and verification accuracy of different estimation models, we found that the model based on the RVI index (LAI=0.481RVI0.830) was the best one for LAI estimation. Based on the optimal model, the peanut LAI distribution was obtained by performing the peanut LAI remote sensing mapping, and the remote sensing inversion LAI accuracy was verified by field data. The results showed that the use of wideband index and GF-1 would be suitable for peanut LAI estimation, which has great significance for monitoring large-area peanut growth.

Key words: spectral response function, GF-1, leaf area index, wide band index.