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Remote Sensing for Land & Resources    2018, Vol. 30 Issue (4) : 115-124     DOI: 10.6046/gtzyyg.2018.04.18
Application of MODIS remote sensing products in the estimation of grass yield in Sanjiang Source Area
Xifeng CAO1, Lin SUN1, Zifei ZHAO1, Xiaofeng HAN2, Mingjie YAN3
1. College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
2. College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, China
3. Shandong Geo-Surveying and Mapping Institute, Jinan 250000, China
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The growth of grassland in Sanjiang Source Area has an important influence on the development of livestock husbandry and the ecological balance of Sanjiang ecosystem. It is of great importance to estimate grass yield reasonably and accurately. In view of the larger area and the complicated surface structure, this study is aimed at predicting the grass yield by using MODIS product data. The authors built a prediction model of grass yield in Sanjiang by using six kinds of MODIS products (LAI, FPAR, NDVI, EVI, GPP and LST) from April 2009 to October 2009 and, in combination with partial least squares regression (PLS) and multiple linear regression method, accomplished estimation of grass yield by remote sensing. Based on the built model, the authors used the 140 scene data from April to October 2011 for application testing, and then compared the predicting results with standard values which were measured from June to August 2011 in 16 grassland ecological monitoring stations in Sanjiang. The results show that there is a good correlation between grass yield estimated based on the six MODIS products and the measured actual grass yield. A comparison with the result of multiple linear regression shows that the result of PLS has a higher coefficient (R 2≈0.829~0.878) and lower root mean squared error (RMSE≈42.457~93.674 kg·hm -2).

Keywords Sanjiang Source Area      MODIS      time series      partial least squares regression      grass yield estimation     
:  TP751  
Issue Date: 07 December 2018
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Xifeng CAO
Zifei ZHAO
Xiaofeng HAN
Mingjie YAN
Cite this article:   
Xifeng CAO,Lin SUN,Zifei ZHAO, et al. Application of MODIS remote sensing products in the estimation of grass yield in Sanjiang Source Area[J]. Remote Sensing for Land & Resources, 2018, 30(4): 115-124.
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Fig.1  Location of the study area
数据名称 产品 空间分辨率/m 时间分辨率/d DN值数据范围 缩放因子
MCD12Q1 LC 500 365 0~254
1 000 8 7 500~65 535
1 000 16 -2 000~10 000 0.000 1
1 000 8 0~100 0.1
MOD17A2 GPP 1 000 8 0~32 700 0.000 1
Tab.1  Information of MODIS land products
Fig.2  NDVI time series
Fig.3  EVI time series
Fig.4  LAI time series
Fig.5  FPAR time series
Fig.6  GPP time series
时间 多元线性回归模型
6月 Y1=-14.8-8 186.5XFPAR129-3 812.3XGPP145+8 584.0XGPP193+5 555.0XLAI129-970.0XNDVI129
7月 Y2=-182.1+6 025.7XFPAR129+1 061.8XFPAR257-1 770.8XLAI129-229.0XLAI257+273.8XNDVI129
8月 Y3=173.4-10 626.5XFPAR129-1 495.2XFPAR257+6 243.5XLAI129+727.1XLAI257-430.0XNDVI129
Tab.2  Multivariable linear regression model
Fig.7  Principal component contribution
Fig.8  t[1]/u[1]plane
Fig.9  Variable projection importance index
Fig.10  t[1]/t[2] plane
Fig.11  Standard model distance of monitoring points on Y
Fig.12  Standard model distance of monitoring points on X
解释因子 模型(6月) 模型(7月) 模型(8月)
常数项(a) 1.316 54 1.672 89 1.582 48
Tab.3  Constants of model
Fig.13  Coefficients of estimation model in June 2009
Fig.14  Comparison of miltiple linear regression and PLS
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