Summer maize LAI retrieval based on multi-source remote sensing data

Fangjiang Pan, Jinkai Guo, Jianchi Miao, Haiyu Xu, Bingquan Tian, Daocai Gong, Jing Zhao, Yubin Lan

Abstract


Leaf Area of Index (LAI) refers to half of the total leaf area of all crops per unit area. It is an important index to represent the photosynthetic capacity and biomass of crops. To obtain LAI conditions of summer maize in different growth stages quickly and accurately, further guiding field fertilization and irrigation. The Unmanned aerial vehicles (UAV) multispectral data, growing degree days, and canopy height model of 2020-2021 summer maize were used to carry out LAI inversion. The vegetation index was constructed by the ground hyperspectral data and multispectral data of the same range of bands. The correlation analysis was conducted to verify the accuracy of the multispectral data. To include many bands as possible, four vegetation indices which included R, G, B, and NIR bands were selected in this study to test the spectral accuracy. There were nine vegetation indices calculated with UAV multispectral data which were based on the red band and the near-infrared band. Through correlation analysis of LAI and the vegetation index, vegetation indices with a higher correlation to LAI were selected to construct the LAI inversion model. In addition, the Canopy Height Model (CHM) and Growing degree days (GDD) of summer maize were also calculated to build the LAI inversion model. The LAI inversion of summer maize was carried out based on multi-growth stages by using the general linear regression model (GLR), Multivariate nonlinear regression model (MNR), and the partial least squares regression (PLSR) models. R² and RMSE were used to assess the accuracy of the model. The results show that the correlation between UAV multispectral data and hyperspectral data was greater than 0.64, which was significant. The Wide Dynamic Range Vegetation Index (WDRVI), Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), Plant Biochemical Index (PBI), Optimized Soil-Adjusted Vegetation Index (OSAVI), CHM and GDD have a higher correlation with LAI. By comparing the models constructed by the three methods, it was found that the PLSR has the best inversion effect. It was based on OSAVI, GDD, RVI, PBI, CHM, NDVI, and WDRVI, with the training model’s R² being 0.8663, the testing model’s R² being 0.7102, RMSE was 1.1755. This study showed that the LAI inversion model based on UAV multispectral vegetation index, GDD, and CHM improves the accuracy of LAI inversion effectively. That means the growing degree days and crop population structure change have influenced the change of maize LAI certainly, and this method can provide decision support for maize growth monitoring and field fertilization.
Keywords: maize, UAV multispectral, leaf area of index, growing degree day, canopy height model, vegetation index
DOI: 10.25165/j.ijabe.20231602.7285

Citation: Pan F L, Guo J K, Miao J C, Xu H Y, Tian B Q, Gong D C, et al. Summer maize LAI retrieval based on multi-source remote sensing data. Int J Agric & Biol Eng, 2023; 16(2): 179-186.

Keywords


maize, UAV multispectral, leaf area of index, growing degree day, canopy height model, vegetation index

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References


Zhang X F, Liao C H, Sun Q, Zhao P J, Pan Y F, Wang X Y, et al. Ecological and environment variables conversion based on remote sensing technology. Beijing: Science Press, 2017; 82p. (in Chinese)

Xia T, Wu W B, Zhou Q B, Zhou Y, Yu L. An estimation method of winter wheat leaf area index based on hyperspectral data. Scientia Agricultura Sinica, 2012; 45(10): 2085-2092.

Luan Q, Guo J P, Ma Y L, Zhang L M, Wang J X. A general model for estimating leaf area index of maize. Chinses Journal of Agrometeorology, 2020; 41(8): 506-519. (in Chinese).

Qi M Y, Song W B. Review on the main determination methods and equipments of leaf area index. Journal of AnHui agricultural, 2012; 40(31): 15097-15099. (in Chinese).

Yang H B, Zhao J, Lan Y B, Lu L Q, Li Z M. Fraction vegetation cover extraction of winter wheat based on spectral information and texture features obtained by UAV. International Journal of Precision Agricultural Aviation, 2019; 2(2): 54–61.

Tahir M N, Lan Y B, Zhang Y L, Wang Y K, Nawaz F, Shah M A A, et al. Real time estimation of leaf area index and groundnut yield using multispectral UAV. International Journal of Precision Agricultural Aviation, 2020; 3(1): 1–6.

García-Martínez H, Flores-Magdaleno H, Ascencio-Hernández R, Khalil-Gardezi A, Tijerina-Chávez L, Mancilla-Villa O R, et al. Corn grain yield estimation from vegetation indices, canopy cover, plant density, and a neural network using multispectral and RGB images acquired with unmanned aerial vehicles. Agriculture, 2020; 10(7): 277. doi: 10.3390/agriculture10070277.

Xia T, Wu W B, Zhou Q B, Zhou Y, Yu L. An estimation method of winter wheat leaf area index based on hyperspectral data. Scientia Agricultural Sinica, 2012; 45(10): 2085-2092. (in Chinese).

Gao L, Yang G J, Yu H Y, Xu B, Zhao X Q, Dong J H, et al. Retrieving winter wheat leaf area index based on unmanned aerial vehicle hyperspectral remoter sensing. Transactions of the CSAE, 2016; 32(22): 113-120. (in Chinese).

Song K S, Zhang B, Li F, Duan H T, Wang Z M. Correlative analyses of hyperspectral reflectance, soybean LAI and aboveground biomass. Transactions of the CSAE, 2005; 21(1): 36- 40. (in Chinese).

Strachan I B, Pattey E, Boisvert J B. Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance. Remote Sensing of Environment, 2002; 80(2): 213-224.

Li C C, Niu Q L, Yang G J, Feng H K, Liu J G, Wang Y J. Estimation of leaf area index of soybean breeding materials based on UAV digital images. Transactions of the CSAM, 2017; 48(8): 147-158. (in Chinese).

Chen Y L, Gu X H, Dong Y S, Hu S W, Zhang Q Y, Zhao J. Predicition of winter wheat yield based on remote sensing with accumulate temperature. Journal of Triticeae Crops, 2014; 34(8): 147-158.

Su L J, Liu Y H, Wang Q J. Rice growth model in China based on growing degree days. Transactions of the CSAE, 2020; 36(1): 162-174. (in Chinese)

Huang J X, Wang J L, Huang R, Huang H, Su W, Zhu D H. Forecasting of regional maize maturity using accumulated temperature-solar radiation model and Leaf Area Index integral area model. Transactions of the CSAM, 2019; 50(12): 133-143. (in Chinese).

Sun M M, Jiang L X, Yu R H, Sun Y T. Study on heat index at growing stages of corn and the planting border of different varieties in the north. Chinese Journal of Agrometeorology, 1998; 19(4): 8-13. (in Chinese).

Wang H, Bai Y L, Yang L P, Lu Y L, Wang L. A summer maize dressing decision-making model based on effective accumulated temperature on UAV remote sensing image. Chinese Journal of Eco-Agriculture, 2012; 20(4): 408-413. (in Chinese).

Cai J B, Chang H F, Chen H, Zhang B Z, Wei Z, Peng Z G. Simulation of maize dry matter accumulation in normalized logistic model with different effective accumulated temperatures in field. Transactions of the CSAM, 2020; 51(5): 263-271. (in Chinese).

Tao H L, Xu L Q, Feng H K, Yang G J, Dai Y, Niu Y C. Estimation of plant height and leaf area index of winter wheat based on UAV hyperspectral remote sensing. Transactions of the CSAM, 2020; 51(12): 193-201. (in Chinese).

Umut Hasan, Mamat Sawut, Chen S, Li D. Inversion of leaf area index of winter wheat based on GF-1/2 image]. Acta Agronomica Sinica, 2020; 46(5): 787-797. (in Chinese).

Shi Z F, Wang R Y, Li Y H, Yan H, Zhang X X. LAI estimation based on multi-spectral remote sensing of UAV and its application in saline soil improvement. Scientia Agricultural Sinica, 2020; 53(9): 1795-1805. (in Chinese).

Du X Y, Wan L, Cen H Y, Chen S B, Zhu J P, Wang H Y, et al. Multi-temporal monitoring of leaf area index of rice under different nitrogen treatments using UAV images. Int J Precis Agric Aviat, 2020; 3(1): 7–12.

Paulo C S, Francisco Z, Marco Y, John G, Hector V, Cristian E, et al Evaluation of models to determine LAI on poplar stands using spectral indices from Sentinel-2 satellite images. Ecological modelling, 2020; 428: 109058. doi: 10.1016/j.ecolomdel.2020.109058.

Zhu Y. Linear model, nonlinear model and generalized linear model. Statistics & Information Forum, 1996; 3: 45-48. (in Chinese).

Teng Y. Application of partial least square regression in spectral analysis. Application of IC, 2020; 37(1): 16-17. (in Chinese).

Chen P F, Li G, Shi Y J, Xu Z T, Yang F T, Cao Q J. Validation of an unmanned aerial vehicle hyperspectral sensor and its application in maize leaf area index estimation. Scientia Agricultura Sincia, 2018; 51(8): 1464-1474. (in Chinese).

Gao M Y, Zhang J S, Pan Y M, Duan Y M, Zhang D J. Retrieval of winter wheat leaf area index based on vegetation index and crop height. Chinese Journal of Agricultural and Regional Planning, 2020; 41(8): 49-57. (in Chinese).

Liang L, Geng D, Yan J, Qiu S Y, Di L Q, Wang S G, et al. Estimating crop LAI using spectral feature extraction and the hybrid inversion method. Remote Sensing, 2020; 12(21): 3534. doi:10.3390/rs12213534.

Huang Q, Zhou Q B, Wang L M, Li D D. Relationship between winter wheat growth grades obtained from remote-sensing and meteorological factor. Transactions of the CSAM, 2014; 45(12): 301-307. (in Chinese).

Raj R, Walker J P, Nandan R, Nandan R, Naik B, Jagarlapudi A. Leaf area index estimation using top-of-canopy airborne RGB images. International Journal of Applied Earth Observations and Geoinformation, 2021; 96: 102282. doi: 10.1016/j.jag.2020.102282.

Luo S Z, Wang C, Xi X H, Nie S, Fan X Y, Chen H Y, et al. Combining hyperspectral imagery and LiDAR pseudo-waveform for predicting crop LAI, canopy height and above-ground biomass. Ecological Indicators, 2019; 102: 801-812.




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