Estimation of winter wheat biomass based on remote sensing data at various spatial and spectral resolutions

doi:10.1007/s11707-009-0012-x

Front Earth Sci Chin

2009, Vol. 3

Issue (1) : 118-128 https://doi.org/10.1007/s11707-009-0012-x

RESEARCH ARTICLE

Estimation of winter wheat biomass based on remote sensing data at various spatial and spectral resolutions

Yansong BAO^1,2,3(

), Wei GAO^2,3, Zhiqiang GAO^2,3,4

1. School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044, China; 2. USDA UV-B Monitoring and Research Program, Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO80521, USA; 3. Key Lab of Geographic Information Science, East China Normal University, Shanghai 200062, China; 4. Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

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Abstract

Biomass can indicate plant growth status, so it is an important index for plant growth monitoring. This paper focused on the methodology of estimating the winter wheat biomass based on hyperspectral field data, including the LANDSAT TM and EOS MODIS images. In order to develop the method of retrieving the wheat biomass from remote sensed data, routine field measurements were initiated during periods when the LANDSAT satellite passed over the study region. In the course of the experiment, five LANDSAT TM images were acquired respectively at early erecting stage, jointing stage, earring stage, flowering stage and grain-filling stage of the winter wheat, and the wheat biomass was measured at each stage. Based on the TM and MODIS images, spectral indices such as NDVI, RDVI, EVI, MSAVI, SIPI and NDWI were calculated. At the same time, the hyperspectral field data was used to compute the normalized difference in spectral indices, red-edge parameters, spectral absorption, and reflection feature parameters. Then the correlation coefficients between the wheat biomass and spectral parameters of the experiment sites were computed. According to the correlation coefficients, the optimal spectral parameters for estimating the wheat biomass were determined. The best-fitting method was employed to build the relationship models between the wheat biomass and the optimal spectral parameters. Finally, the models were used to estimate the wheat biomass based on the TM and MODIS data. The maximum RMSE of estimated biomass was 66.403 g/m².

Keywords LANDSAT TM EOS MODIS biomass retrieval spectral indices

Corresponding Author(s): BAO Yansong,Email:ysbao@hotmail.com

Issue Date: 05 March 2009

Cite this article:

Yansong BAO,Wei GAO,Zhiqiang GAO. Estimation of winter wheat biomass based on remote sensing data at various spatial and spectral resolutions[J]. Front Earth Sci Chin, 2009, 3(1): 118-128.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-009-0012-x
https://academic.hep.com.cn/fesci/EN/Y2009/V3/I1/118

Fig.1 Experimental plots of Beijing in 2004 and 2005

Tab.1 Wavelengths of B1 and B2

Fig.2 Inverted Gaussian reflectance model and red-edge parameters

reflectanceand spectral parameters	4/3/2005	4/15/2004	4/21/2005	5/8/2005	5/18/2004	5/22/2005	6/2/2004
455	-0.627	-0.706	-0.741	-0.35	0.198	-0.239	-0.144
550	-0.566	-0.731	-0.665	-0.254	0.205	-0.29	-0.001
680	-0.753	-0.765	-0.796	-0.515	0.038	-0.296	-0.527
980	0.627	0.364	0.385	0.214	0.246	0.132	0.389
1090	0.644	0.459	0.43	0.319	0.268	0.195	0.388
1200	0.375	-0.054	0.095	-0.068	0.169	-0.063	0.156
1285	0.38	-0.048	0.095	-0.038	0.141	-0.055	0.135
1468	-0.686	-0.717	-0.748	-0.611	0.004	-0.34	-0.598
1685	-0.468	-0.629	-0.581	-0.454	0.015	-0.313	-0.461
2200	-0.649	-0.713	-0.741	-0.619	0.045	-0.334	-0.602
[670,890]	0.85	0.799	0.797	0.624	0.091	0.308	0.538
[890,980]	0.837	0.906	0.823	0.725	0.535	0.489	0.679
[920,980]	0.844	0.909	0.818	0.722	0.486	0.49	0.647
[857,1210]	0.825	0.884	0.823	0.743	0.459	0.465	0.692
[820,1650]	0.826	0.849	0.799	0.746	0.333	0.396	0.657
[820,2200]	0.81	0.799	0.785	0.733	0.161	0.358	0.643
REP	0.71	0.894	0.686	0.575	-0.012	0.392	0.291
Lo	0.815	0.82	0.76	0.467	0.25	0.335	0.481
Lwidth	-0.775	-0.493	-0.7	0.557	-0.522	0.015	-0.615
depth 672	0.849	0.785	0.788	0.622	0.122	0.298	0.563
area 672	0.855	0.828	0.798	0.568	0.099	0.333	0.552
ND 672	-0.821	-0.855	-0.772	-0.518	-0.052	-0.363	-0.409
depth 980	0.862	0.912	0.803	0.736	0.461	0.492	0.631
area 980	0.861	0.912	0.802	0.743	0.411	0.504	0.64
ND 980	0.286	0.433	-0.311	-0.649	0.293	-0.24	0.122
depth 1190	0.871	0.892	0.788	0.769	0.252	0.444	0.571
area 1190	0.868	0.894	0.79	0.768	0.253	0.443	0.587
ND 1190	-0.536	-0.777	-0.808	-0.698	-0.117	-0.393	-0.571
depth 1450	0.792	0.761	0.761	0.706	0.109	0.313	0.578
area 1450	-0.767	-0.786	-0.333	0.052	-0.464	0.165	-0.554
ND 1450	-0.107	0.483	0.501	0.401	0.167	0.365	0.493
P_depth 560	-0.401	-0.472	-0.742	-0.63	-0.063	-0.126	-0.349
PND 560	0.797	0.718	0.823	0.512	0.099	0.145	0.63
P_area 560	0.792	0.704	0.826	0.56	0.098	0.141	0.622
P_depth 920	0.738	0.728	0.738	0.41	0.488	0.259	0.652
PND 920	0.842	0.883	0.796	0.721	0.381	0.515	0.503
P_area 920	0.846	0.865	0.79	0.728	0.302	0.542	0.436
P_depth 1100	0.499	-0.672	-0.071	-0.025	-0.297	-0.033	-0.486
PND 1100	0.862	0.896	0.789	0.77	0.24	0.475	0.585
P_area 1100	0.864	0.897	0.787	0.765	0.277	0.462	0.591
P_depth 1280	-0.557	-0.718	-0.764	-0.639	0.216	-0.172	-0.288
PND 1280	0.817	0.786	0.75	0.749	-0.066	0.34	0.522
P_area 1280	0.78	0.766	0.777	0.662	-0.308	0.189	0.441
P_depth 1690	-0.08	-0.488	-0.659	-0.411	-0.227	-0.399	-0.545
PND 1690	0.734	0.747	0.751	0.546	0.112	0.227	0.65
P_area 1690	0.719	0.74	0.761	0.548	0.162	0.248	0.658
P_depth 2230	-0.562	-0.301	-0.469	-0.544	0.512	-0.27	-0.351
PND 2230	0.519	0.635	0.748	0.314	0.449	0.031	0.369
P_area 2230	0.54	0.662	0.762	0.385	0.379	0.066	0.433

Tab.2 Correlation coefficients between the biomass and hyperspectral parameters

Tab.3 Biomass retrieval model based on the hyperspectral parameters at different growth stages

Tab.4 Spectral indices used in this paper

Tab.5 The correlation coefficients of wheat biomass and spectral indices based on the simulated data

Tab.6 Correlation coefficients of wheat biomass and spectral indices based on TM data

Fig.3 Relation between NDVI and biomass on April 1, 2004 (early erecting stage)

Fig.4 Relation between NDVI and biomass on April 17, 2004 (jointing stage)

Fig.5 Relation between NDVI and biomass on May 6, 2005 (earring stage)

Fig.6 Relation between NDVI and biomass on May 19, 2004 (flowing stage)

Fig.7 The relation between NDVI and biomass on May 22, 2004 (grain-filling stage)

Fig.8 The relation between NDVI and biomass on vegetative stage

Tab.7 Correlation coefficient between biomass and MODIS spectral indices

Fig.9 Relation between biomass and MODIS shortwave infrared reflectance during vegetative stage

Fig.10 Biomass map on April 1, 2004

Fig.11 Biomass map on April 17, 2004

Fig.12 Biomass map on May 6, 2005

Fig.13 Biomass map on March 29, 2004

Fig.14 Biomass map on April 14, 2004

Fig.15 Biomass map on May 8, 2005

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