Estimating winter wheat biomass by coupling the CBA-Wheat model and multi-spectral remote sensing

Wang Shijun; Liu Miao; Zhao Yu; Liu Zhaoyu; Liu Xiuyu; Feng Haikuan; Sui Xueyan; Li Zhenhai

doi:10.11834/jrs.20243080

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Estimating winter wheat biomass by coupling the CBA-Wheat model and multi-spectral remote sensing
Pages: 1-12(2024)
Published Online： 07 March 2024 ，
DOI： 10.11834/jrs.20243080

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王士俊，刘苗，赵钰，柳昭宇，刘修宇，冯海宽，隋学艳，李振海.XXXX.多源遥感数据耦合CBA-Wheat模型的冬小麦生物量估算研究.遥感学报，XX（XX）： 1-12

Wang Shijun，Liu Miao，Zhao Yu，Liu Zhaoyu，Liu Xiuyu，Feng Haikuan，Sui Xueyan，Li Zhenhai. XXXX. Estimating winter wheat biomass by coupling the CBA-Wheat model and multi-spectral remote sensing. National Remote Sensing Bulletin， XX（XX）：1-12
王士俊，刘苗，赵钰，柳昭宇，刘修宇，冯海宽，隋学艳，李振海.XXXX.多源遥感数据耦合CBA-Wheat模型的冬小麦生物量估算研究.遥感学报，XX（XX）： 1-12 DOI： 10.11834/jrs.20243080.

Wang Shijun，Liu Miao，Zhao Yu，Liu Zhaoyu，Liu Xiuyu，Feng Haikuan，Sui Xueyan，Li Zhenhai. XXXX. Estimating winter wheat biomass by coupling the CBA-Wheat model and multi-spectral remote sensing. National Remote Sensing Bulletin， XX（XX）：1-12 DOI： 10.11834/jrs.20243080.

摘要

生物量是反映作物生长状况的重要指标，及时准确估计冬小麦地上生物量对于产量预测和田间管理决策具有重要意义。综合考虑遥感植被指数（Vegetation index，VI）与数字化生育期（Zadoks stage，ZS）创建的作物生物量模型（Crop biomass algorithm for wheat，CBA-Wheat）适用于全生育时期的冬小麦生物量估算，但是模型参数基于地面高光谱数据构建，而卫星遥感数据在应用过程中，需要使用更多的地面实测数据进行模型参数的调试，限制了模型的推广使用。因此，本研究采用遗传优化算法（Genetic algorithm，GA）对CBA-Wheat模型进行全局优化确定模型最优参数，利用高分辨率遥感影像提取VI与试验记录的ZS数据，分别构建以不同VI为输入变量的冬小麦生物量反演模型。结果表明：增强型植被指数（Enhanced Vegetation Index2，EVI2）为输入变量建立的模型精度最高，冬小麦生物量估算验证的决定系数（R

）和均方根误差（RMSE）分别为0.92和1.37t/ha。基于CBA-Wheat模型的生物量估算精度效果优于基于偏最小二乘回归方法的生物量估算精度（R

=0.85，RMSE=1.87t/ha）。本研究基于遗传算法优化的CBA-Wheat模型不仅具有较高的反演精度，而且适用于全生育期反演，在区域大面积生物量预测方面具有较好的应用潜力。

Abstract

Objective

Biomass is an important indicator to reflect the growth status of crops. Timely and accurate estimation of aboveground biomass of winter wheat is significant for yield prediction and field management decision-making. The crop biomass model (CBA-Wheat) developed by remote sensing spectral index (VI) and digital growth stage (ZS) is suitable for the estimation of winter wheat biomass in the whole growth period. The first layer of this model is a linear model of AGB and VI

which constructs linear regression models between AGB and vegetation index at different growth periods. On this basis

it was found that the AGB model coefficients of each period have a good evolutionary law with ZS. However

the model parameters are based on the ground hyperspectral data in the previous study

and the satellite data need much ground measured data to calibrate the model parameters

which limits the popularization at regional scale.

Method

In this study

genetic algorithm (GA) is used to globally optimize the model parameters of the CBA-Wheat model in this study.

GA combines the survival rules of the fittest in the process of biological evolution with the random information exchange system of the chromosome within the population and has a more efficient global optimization effect for some non-linear

multi model

multi objective function optimization problems. Two input variables of CBA-Wheat includes ZS data from field recorded and VI from high-resolution remote sensing images. Four VI-based CBA-Wheat models of enhanced vegetation index 2 (CBA-Wheat

EVI2

)

difference vegetation index (CBA-Wheat

DVI

)

ratio vegetation index (CBA-Wheat

RVI

) and modified simple ratio vegetation index (CBA-Wheat

MSR

)

were constructed and the best model was used for AGB mapping.

Meanwhile

performance of partial least squares regression (PLSR) was used to compare the CBA-Wheat model accuracy.

Result

The results showed that all four CBA-Wheat models have good accuracy

and the simulated winter wheat biomass is consistent with the measured biomass

Among them

the precision of the CBA-Wheat

EVI2

model has the highest determination coefficient (R

) and root mean square error (RMSE) of 0.92 and 1.37t/ha for validation results

respectively. The precision of biomass estimation based on CBA-Wheat model was better than that based on PLSR method (R

=0.85

RMSE=1.87t/ha). The CBA-Wheat model has good biomass estimation performance in various growth stages of winter wheat

and performs well in high biomass situations without significant underestimation.

Conclusion

The CBA-Wheat model optimized by genetic algorithm in this study not only has high inversion accuracy

but also is applicable to the inversion of the whole growth period

and has good application potential in the prediction of regional large area biomass.

It also provides a good method reference for biomass prediction of other crops.

关键词

冬小麦地上生物量遗传算法CBA-Wheat多源数据EVI2Sentinel-2遥感

Keywords

winter wheatbiomassCBA-Wheat modelgenetic algorithmmulti-source dataEVI2Sentinel-2remote sensing

references

Chastain R, Housman I, Goldstein J,Finco and MTenneson K.2019. Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM + top of atmosphere spectral characteristics over the conterminous United States,221: 274-285[DOI:10.1016/j.rse.2018.11.012http://dx.doi.org/10.1016/j.rse.2018.11.012]

Deng S B. 2014. ENVI remote sensing image processing method. Beijing: Science Press

邓书斌. 2014. ENVI遥感图像处理方法. 北京: 科学出版社

Dhillon M S, Dahms T, Kuebert-Flock C, Borg E, Conrad C and Ullmann T. 2020. Modelling Crop Biomass from Synthetic Remote Sensing Time Series: Example for the DEMMIN Test Site, Germany. Remote Sensing, 12(11), 1819. [DOI: 10.3390/rs12111819http://dx.doi.org/10.3390/rs12111819]

Du X, Li Q, Dong T and Jia K.2015.Winter wheat biomass estimation using high temporal and spatial resolution satellite data combined with a light use efficiency model.Geocarto International, 30(3): 258-269 [DOI: 10.1080/10106049.2014.937467http://dx.doi.org/10.1080/10106049.2014.937467]

Fang P, Yan N, Wei P, Zhao Y and Zhang X. 2021. Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery. Remote Sensing, 13(14), 2755. [DOI: 10.3390/rs13142755http://dx.doi.org/10.3390/rs13142755]

Feng Z L, Yi G H, Li P S, Li H Y and Dai Y. 2007. Review of paraller Genetic Algorithm. Computer Applications and Software, 35(1): 56-59

冯智莉, 易国洪, 李普山, 黎慧源, 代瑜. 2007. 并行化遗传算法研究综述. 计算机应用与软件, 35(1): 56-59

Gnyp M L, Bareth G, Li F, Lenz-Wiedemann V I S, Koppe W, Miao Y, Hennig S D, Jia L, Laudien R, Chen X and Zhang F.2014.Development and implementation of a multiscale biomass model using hyperspectral vegetation indices for winter wheat in the North China Plain.International Journal of Applied Earth Observation and Geoinformation, 33: 232-242 [DOI: 10.1016/j.jag.2014.05.006http://dx.doi.org/10.1016/j.jag.2014.05.006]

Huang H, Huang J, Li X, Zhuo W, Wu Y, Niu Q, Su W and Yuan W.2022.A dataset of winter wheat aboveground biomass in China during 2007–2015 based on data assimilation.Scientific Data, 9(1): 200 [DOI: 10.1038/s41597-022-01305-6http://dx.doi.org/10.1038/s41597-022-01305-6]

Jiang Z, Huete A R, Didan K and Miura T.2008.Development of a two-band enhanced vegetation index without a blue band.Remote Sensing of Environment, 112(10): 3833-3845 [DOI: 10.1016/j.rse.2008.06.006http://dx.doi.org/10.1016/j.rse.2008.06.006]

Jin X, Kumar L, Li Z, Xu X, Yang G and Wang J. 2016. Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral Data. Remote Sensing, 8(12), 972. [DOI: 10.3390/rs8120972http://dx.doi.org/10.3390/rs8120972]

Kross A, McNairn H, Lapen D, Sunohara M and Champagne C.2015.Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops.International Journal of Applied Earth Observation and Geoinformation, 34: 235-248 [DOI: 10.1016/j.jag.2014.08.002http://dx.doi.org/10.1016/j.jag.2014.08.002]

Li W G, Zhao C J, Wang J H and Liu L Y. 2009. Research situation and prospects of wheat condition monitoring based on growth model and remote sensing. Remote sensing for Natural Resources, 19(2): 6-9

李卫国, 赵春江, 王纪华, 刘良云. 2009. 遥感和生长模型相结合的小麦长势监测研究现状与展望. 自然资源遥感, 19(2): 6-9 [DOI: 10.3969/j.issn.1001-070X.2007.02.002http://dx.doi.org/10.3969/j.issn.1001-070X.2007.02.002]

Li Z, Zhao Y, Taylor J, Gaulton R, Jin X, Song X, Li Z, Meng Y, Chen P, Feng H, Wang C, Guo W, Xu X, Chen L and Yang G.2022.Comparison and transferability of thermal, temporal and phenological-based in-season predictions of above-ground biomass in wheat crops from proximal crop reflectance data.Remote Sensing of Environment, 273: 112967 [DOI: 10.1016/j.rse.2022.112967http://dx.doi.org/10.1016/j.rse.2022.112967]

Li Z, Jin X, Liu H, Xu X, Wang J.2019.Global sensitivity analysis of wheat grain yield and quality and the related process variables from the DSSAT-CERES model based on the extended Fourier Amplitude Sensitivity Test method. Journal of Integrative Agriculture, 18(7): 1547–1561. [DOI: 10.1016/s2095-3119(18)62046-5http://dx.doi.org/10.1016/s2095-3119(18)62046-5]

Liu Z Z, Zheng X W, Chen Y S, Zhang C C, Qin F and Zeng H W. 2017. Remote sensing estimation of biomass in winter wheat based on CASA model at region scale. Transactions of the Chinese Society of Agricultural Engineering, 33(04): 225-233+315-226

刘真真, 张喜旺, 陈云生, 张传才, 秦奋, 曾红伟. 2017. 基于CASA模型的区域冬小麦生物量遥感估算. 农业工程学报, 33 (04): 225-233+315-316 [DOI: 10.11975/j.issn.1002-6819.2017.04.031http://dx.doi.org/10.11975/j.issn.1002-6819.2017.04.031]

Pearson R and Miller L D. 1972. Remote mapping of standing crop biomass for estimation of the productivity of the short-grass Prairie, Pawnee National Grasslands, Colorado. Remote Sensing of Environment, VIII: 1355. [DOI: 10.1177/002076409904500102http://dx.doi.org/10.1177/002076409904500102]

Richardson A J and Wiegand C L. 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 43(12): 1541-1552 [DOI: 10.1109/TGE.1977.294499http://dx.doi.org/10.1109/TGE.1977.294499]

Rouse J W, Haas R H, Schell J A and Deering D W. 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec. Publ, 351(1): 309.

Tewes A, Hoffmann H, Nolte M, Krauss G, Schäfer F, Kerkhoff C and Gaiser T. 2020. How Do Methods Assimilating Sentinel-2-Derived LAI Combined with Two Different Sources of Soil Input Data Affect the Crop Model-Based Estimation of Wheat Biomass at Sub-Field Level? Remote Sensing, 12(6), 925. [DOI: 10.3390/rs12060925http://dx.doi.org/10.3390/rs12060925]

Wang Y B, Feng D J, Li S J, Wu W J and Ren H Y. 2016. Review of estimating crop biomass based on remote sensing information. Remote Sensing Technology and Application, 31(3): 468-475

王渊博, 冯德俊, 李淑娟, 武文娟, 任红艳. 2016. 基于遥感信息的农作物生物量估算研究进展. 遥感技术与应用, 31(3): 468-475

Wei D B, Liu J P, Cheng S and Zou Q J. 2019. Ant Colony Optimization Routing Algorithm Based on Multi-QoS Constraints in Satellite Networks. Computer Engineering, 45(7): 114-120

魏德宾, 刘健潘, 成胜, 邹启杰. 2019. 卫星网络中基于多QoS约束的蚁群优化路由算法. 计算机工程, 45(7): 114-120 [DOI: 10.19678/j.issn.1000-3428.0051284http://dx.doi.org/10.19678/j.issn.1000-3428.0051284]

Wold S, Sjöström M and Eriksson L.2001.PLS-regression: a basic tool of chemometrics.Chemometrics and Intelligent Laboratory Systems, 58(2): 109-130 [DOI: 10.1016/S0169-7439(01)00155-1http://dx.doi.org/10.1016/S0169-7439(01)00155-1]

Wu Z, Yang H H, Wu J J, Zheng H J and Song Q. 2023. Survey of Evolutionary Transfer Optimization Algorithms. Computer Engineering, 49(01): 1-14

伍洲, 杨寒石, 邬俊俊, 张海军, 宋晴. 2023. 进化迁移优化算法综述. 计算机工程, 49(01): 1-14

Yue J, Feng H, Li Z, Zhou C and Xu K.2021.Mapping winter-wheat biomass and grain yield based on a crop model and UAV remote sensing.International Journal of Remote Sensing, 42(5): 1577-1601 [DOI: 10.1080/01431161.2020.1823033http://dx.doi.org/10.1080/01431161.2020.1823033]

Yue J, Feng H, Yang G and Li Z.2018.A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy.Remote Sensing, 10(1) [DOI: 10.3390/rs10010066]

Yue J, Yang G, Tian Q, Feng H, Xu K and Zhou C.2019.Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices.ISPRS Journal of Photogrammetry and Remote Sensing, 150: 226-244 [DOI: 10.1016/j.isprsjprs.2019.02.022http://dx.doi.org/10.1016/j.isprsjprs.2019.02.022]

Zadoks J C, Chang T T and Konzak C F.1974.A decimal code for the growth stages of cereals.Weed Research, 14(6): 415-421 [DOI: 10.1111/j.1365-3180.1974.tb01084.xhttp://dx.doi.org/10.1111/j.1365-3180.1974.tb01084.x]

Zhao F, Yang G, Yang X, Cen H, Zhu Y, Han S, Yang H, He Y and Zhao C. 2021. Determination of Key Phenological Phases of Winter Wheat Based on the Time-Weighted Dynamic Time Warping Algorithm and MODIS Time-Series Data. Remote Sensing, 13(9), 1836.

Zhao Y, Meng Y, Han S, Feng H, Yang G and Li Z.2022.Should phenological information be applied to predict agronomic traits across growth stages of winter wheat? The Crop Journal, 10(5): 1346-1352 [DOI: 10.1016/j.cj.2022.08.003http://dx.doi.org/10.1016/j.cj.2022.08.003]

Zheng Y, Wu B F and Zheng M. 2017. Estimating the above ground biomass of wheat using Sentinel-2 data. National Remote Sensing Bulletin, 21(2): 318-328

郑阳, 吴炳方, 张淼. 2017. Sentinel-2数据的冬小麦地上干生物量估算及评价. 遥感学报, 21(2): 318-328 [DOI: 10.11834/jrs.20176269http://dx.doi.org/10.11834/jrs.20176269]

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