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引用本文:

DOI:

10.11834/jrs.20221216

收稿日期:

2021-04-14

修改日期:

2022-02-22

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黑河中游农作物MODIS叶面积指数产品时间序列精度验证与变化特征分析
王丹彤, 屈永华
北京师范大学
摘要:

叶面积指数(Leaf Area Index,LAI)是表征植被生长状态的一个重要的冠层结构参数。MODIS LAI产品是全球常用的遥感LAI产品之一。然而,由于地表异质性、数据质量、模型精度等多方面的差异,MODIS LAI产品质量表现各有不同。基于无线传感器网络的LAINet仪器可以自动获取时间频率更密集的LAI实测数据,为验证卫星遥感LAI产品提供了有力支持。本文通过2018和2019年黑河中游时间序列地面实测LAI数据与高空间分辨率卫星遥感植被指数数据建立经验回归模型,反演高空间分辨率卫星遥感LAI作为参考LAI真值,对MODIS LAI产品进行了精度验证与稳定性评价,分析了MODIS LAI与LAINet地面测量的差异原因。结果表明,与Landsat 8参考真值相比,MODIS LAI生长季的质量(RMSE2018=1.17,RMSE2019=1.14)优于衰落季(RMSE2018=1.39,RMSE2019=1.84),MODIS LAI总体低估,尤其是生长季后期。时间序列上,MODIS LAI产品能够刻画植被生长和凋落的季节特征,但生长前期波动性要强于后期。与LAINet观测方式的差异是MODIS LAI低估的主要原因,即遥感传感器从太空平台向下观测,LAI值在生长季后期受到叶绿素降低的影响,而LAINet仪器从冠层下向上观测,主要受到冠层间隙率的影响,而对叶片内色素变化不敏感。对MODIS LAI产品的精度验证与稳定性评价结果表明,可以利用地面实测数据和卫星遥感数据反演时间序列LAI,但是,在使用类似玉米作物的生长季后期数据的时候,需要考虑到MODIS LAI与LAINet LAI观测对象与算法原理的差异。本文的分析结果能够为MODIS LAI产品的使用者和算法研究者提供参考依据。

Time series accuracy validation and variation characteristics analysis of MODIS leaf area index products for crop in the middle reaches of Heihe River
Abstract:

Abstract: Leaf Area Index (LAI) is an important canopy structural parameter that gives account of qualities of the growth state of vegetation. MODIS LAI product is one of the most commonly used remote sensing LAI products in the world. However, the quality of MODIS LAI products varies with different situation because of differences in surface heterogeneity, data quality, and model accuracy and so on. The LAINet instrument which is based on the wireless sensor network can automatically obtain the LAI measured data with more intensive time frequency, which provides strong support for the validation of satellite remote sensing LAI products. Objective: The purpose of this article is to validate the accuracy and evaluate the stability of MCD15A3H LAI products (Colletion 6) with time series ground observation data. The specific objectives include 1) generate reference products that meet MODIS LAI product validation based on the ground network observation time series data; 2) validate the accuracy of MODIS LAI products based on reference products; 3) evaluate the time series stability of MODIS LAI products; 4) analyze the reasons for the difference between the MODIS LAI product and the measured LAI. Method: This paper adopts an indirect comparison method, that is, establishing an empirical regression model based on time series ground-measured LAI and the high spatial resolution satellite remote sensing vegetation index to retrieve the high spatial resolution satellite remote sensing LAI reference map. Upscale the resolution of the reference map to the same resolution as MODIS LAI products. Finally, we validate the accuracy and evaluate the stability of MODIS LAI products with the upscaled satellite remote sensing LAI image. Result: The results show that compared with the reference true value of Landsat 8, the quality of the growing stage (RMSE2018=1.17, RMSE2019=1.14) is better than the senescence stage (RMSE2018=1.39, RMSE2019=1.84), and MODIS LAI is generally underestimated to Landsat LAI, especially in the late growing stage. MODIS LAI products can portray the seasonal characteristics in both vegetation growth and falling stages in time series, but the instability in the early period of growth is stronger than that in the later period. The difference in observation methods is the main reason for the underestimation of MODIS LAI, that is, the LAI value of the remote sensing sensor is affected by the decrease of chlorophyll in the late growing season, because the sensor observes from the space platform in downward direction, while the LAINet instrument observes from the bottom of the canopy in upward direction, which is mainly affected by the canopy gap fraction, but it is not sensitive to changes of pigment in the leaves. Conclusion: The accuracy validation and stability evaluation results of MODIS LAI products show that the time series LAI can be retrieved by using ground-based data and satellite remote sensing data. However, it is necessary to consider the difference between the observation objects and algorithm principles of MODIS LAI and LAINet LAI when using the late growing season data of corn crops.

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