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Published： 07 February 2024 ，
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王丹彤，屈永华.2024.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.遥感学报，28（2）： 359-374
Wang D T and Qu Y H. 2024. Time-series accuracy validation and variation characteristic analysis of MODIS leaf-area index products for crop in the middle reaches of the Heihe River. National Remote Sensing Bulletin， 28（2）：359-374
叶面积指数LAI（Leaf Area Index）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产品质量提供了有力支持。本文基于2018年和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。将该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（RMSE
2018
=1.17，RMSE
2019
=1.14）优于衰落季（RMSE
2018
=1.39，RMSE
2019
=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，因此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。本研究可为MODIS LAI产品的使用者和算法研究者提供参考。
Leaf-Area Index (LAI) is an important canopy structural parameter that accounts for the 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 situations because of variations in surface heterogeneity
data quality
and model accuracy
among others. The LAINet instrument
which is based on the wireless sensor network
can automatically obtain the LAI measured data with more intensive time frequency. It can provide strong support for the validation of satellite remote-sensing LAI products.
This article aims to validate the accuracy and evaluate the stability of MCD15A3H LAI products (Colletion 6) with time-series ground-observation data. The specific objectives include the following: (1) generation of reference products that meet MODIS LAI product validation based on ground network observation time-series data
(2) validation of accuracy of MODIS LAI products based on reference products
(3) evaluation of the time-series stability of MODIS LAI products
and (4) analysis of the reasons for the difference between the MODIS LAI product and the measured LAI.
This work adopts an indirect comparison method
that is
establishing an empirical regression model based on time-series ground-measured LAI and high-spatial-resolution satellite remote-sensing vegetation index to obtain a high-spatial-resolution satellite remote-sensing LAI reference map. The resolution of the reference map is upscaled to the same resolution as those of MODIS LAI products. Finally
we validate the accuracy and evaluate the stability of MODIS LAI products with the upscaled satellite remote-sensing LAI image.
Results show that compared with the reference true value of Landsat 8
the quality of the growing stage (RMSE
2018
=1.17
RMSE
2019
=1.14) is better than that of the senescence stage (RMSE
2018
=1.39
RMSE
2019
=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 the 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 in chlorophyll in the late growing season because the sensor observes from the space platform in the downward direction. Conversely
the LAINet instrument observes from the bottom of the canopy in the upward direction
which is primarily affected by the canopy-gap fraction. However
it is insensitive to changes in the pigment of leaves.
The accuracy validation and stability evaluation results of MODIS LAI products show that the time-series LAI can be retrieved using ground-based data and satellite remote-sensing data. However
considering the difference between the observation objects and algorithm principles of MODIS LAI and LAINet LAI is necessary when using the late-growing-season data of corn crops.
MODIS叶面积指数LAINet精度验证稳定性评价
MODISleaf area indexLAINetaccuracy validationstability evaluation
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