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

被引：0

作者：

Wang D. ^{[1
]}

Qu Y. ^{[1
]}

机构：

[1] 1. Beijing Normal University, State Key Laboratory of Remote Sensing Science

[2] 2. Beijing Engineering Research Center for Global Land Remote Sensing Products/Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University

来源：

National Remote Sensing Bulletin | 2024年 / 28卷 / 02期

关键词：

accuracy validation; LAINet; leaf area index; MODIS; stability evaluation;

D O I：

10.11834/jrs.20221216

中图分类号：

学科分类号：

摘要：

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 (RMSE2018=1.17, RMSE2019= 1.14) is better than that of 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 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. © 2024 Science Press. All rights reserved.

引用

页码：359 / 374

页数：15

共 27 条

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