• 生物资源 •

### 基于时序LAI的地块尺度玉米长势监测方法

1. 1. 中国农业大学土地科学与技术学院,北京 100083
2. 农业部农业灾害遥感重点实验室,北京 100083
3. 滕州市第一中学,滕州 277500
• 收稿日期:2018-01-19 修回日期:2018-12-11 出版日期:2019-03-20 发布日期:2019-03-20
• 作者简介:

作者简介：苏伟,女,山东省滕州市人,博士,副教授,博士生导师,主要从事农业遥感应用研究。E-mail: suwei@cau.edu.cn

• 基金资助:
国家自然科学基金项目（41671433,41371327）; 十三五国家重点研发计划项目（2017YFD0300903）

### Field-scale corn growth monitoring using time series LAI

Wei SU1,2(), Dehai ZHU1,2, Mingyu SU, Jianxi HUANG1,2, Zhe LIU1,2, Hao GUO1,2

1. 1. College of Land Science and Technology, China Agricultural University, Beijing 100083, China
2. Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China
3. No.1 Middle School of Tengzhou, Tengzhou 277500, China
• Received:2018-01-19 Revised:2018-12-11 Online:2019-03-20 Published:2019-03-20

Abstract:

Crop growth monitoring is an important work for crop management and yield prediction. Aimed to solve the problem of mixed spectrum resulting from small agricultural land plots in China, this study used high and medium spatial resolution images including Landsat-7 ETM+, Landsat-8 OLI, GF-1, and HJ-1 A/B images for corn growth monitoring. In order to avoid too much reliance on the Normalized Difference Vegetation Index (NDVI) parameters, leaf area index (LAI) was selected as the corn growth monitoring parameter and the PROSAIL radiative transfer model was used to retrieve LAI. Three indices (LAI ratio to previous year (RPLAI), vegetation condition index based on LAI (LVCI), and mean vegetation condition index based on LAI (MLVCI)) were used for real-time monitoring of corn growth. The results of a case study in the 852 Farm of Heilongjiang Farms & Land Reclamation Administration in 2015 indicate that: (1) The simultaneous GF-1 image reflentance and the Landsat-8 OLI image reflentance are highly correlated. The correlation coefficients R2 of near-infrared bands, green bands, and red bands of the GF-1 image and the Landsat-8 OLI image are 0.9320, 0.7339, and 0.7153, respectively. This is the precondition for establishing the time series LAI for corn growth monitoring using multi-source remote sensing images. (2) The accuracy of retrieving LAI using PROSAIL radiative transfer model is high——the correlation coefficient R2 is 0.8030 and the root mean squared error (RMSE) is 0.7675. The retrieved time series LAI indicate that LAI increased quickly at the end of June, reached the maximum at the end of July or the beginning of August, and started to decrease at the end of August. (3) The RPLAI, LVCI, and MLVCI indices were used for the real-time monitoring of corn growth and the results indicate that the growth in 2015 was at an average level, and corn growth in the northern part of the area was better than in the southern part.