Temporal Variation and Component Allocation Characteristics of Geometric and Physical Parameters of Maize Canopy for the Entire Growing Season
The accurate monitoring of crop parameters is important for crop yield prediction and canopy parameter inversion from remote sensing. Process-based and semi-empirical crop models are the main approaches to modeling the temporal changes in crop parameters. However, the former requires too many input...
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MDPI AG
2022-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/13/3017 |
| _version_ | 1797434027952570368 |
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| author | Bingze Li Ming Ma Shengbo Chen Xiaofeng Li Si Chen Xingming Zheng |
| author_facet | Bingze Li Ming Ma Shengbo Chen Xiaofeng Li Si Chen Xingming Zheng |
| author_sort | Bingze Li |
| collection | DOAJ |
| description | The accurate monitoring of crop parameters is important for crop yield prediction and canopy parameter inversion from remote sensing. Process-based and semi-empirical crop models are the main approaches to modeling the temporal changes in crop parameters. However, the former requires too many input parameters and the latter has the problem of poor portability. In this study, new semi-empirical geometric and physical parameters of the maize canopy model (GPMCM) crop model adapted to northeast China were proposed based on a time-series field datasets collected from 11 sites in the Nong’an and Changling Counties of Jilin Province, China, during DOY (day of year) 163 to DOY 278 in 2021. The allocation characteristics of and correlations between each maize canopy parameter were investigated for the whole growing season using the 22 algorithms of crop parameters, and the following conclusions were obtained. (1) The high correlation coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>R</mi></semantics></math></inline-formula> mean = 0.79) of LAI with other canopy parameters indicated that it was a good indicator for predicting other parameters. (2) Better performance was achieved by the regression method based on the two-stage simulation. The root-mean-squared error (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi></mrow></semantics></math></inline-formula>) of geometric parameters including maize height, stem long radius, and short radius were 12.91 cm, 0.74 mm, and 0.73 mm, respectively, and the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi></mrow></semantics></math></inline-formula> of the physical parameters including the FAGB, AGB, VWC, and RWC of the stems and leaves, ranged from 0.05 kg/m<sup>2</sup> to 4.24 kg/m<sup>2</sup> (2.0% to 12.9% for mean absolute percentage error (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mi>A</mi><mi>P</mi><mi>E</mi></mrow></semantics></math></inline-formula>)). (3) The extension of the field-scale GPMCM to the 500 m MODIS-scale still provided a good accuracy (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mi>A</mi><mi>P</mi><mi>E</mi></mrow></semantics></math></inline-formula>: 11% to 18.5%) and confirmed the feasibility of the large-scale application of the GPMCM. The proposed CPMCM can predict the temporal dynamics of maize geometric and physical parameters, and it is helpful to establish the forward and reverse models of remote sensing and improve the inversion accuracy of crop parameters. |
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| language | English |
| last_indexed | 2024-03-09T10:26:03Z |
| publishDate | 2022-06-01 |
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| spelling | doaj.art-96dac412737c4c32ac31e04d9b754b472023-12-01T21:40:19ZengMDPI AGRemote Sensing2072-42922022-06-011413301710.3390/rs14133017Temporal Variation and Component Allocation Characteristics of Geometric and Physical Parameters of Maize Canopy for the Entire Growing SeasonBingze Li0Ming Ma1Shengbo Chen2Xiaofeng Li3Si Chen4Xingming Zheng5School of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, ChinaSchool of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, ChinaSchool of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, ChinaSchool of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, ChinaSchool of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, ChinaSchool of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, ChinaThe accurate monitoring of crop parameters is important for crop yield prediction and canopy parameter inversion from remote sensing. Process-based and semi-empirical crop models are the main approaches to modeling the temporal changes in crop parameters. However, the former requires too many input parameters and the latter has the problem of poor portability. In this study, new semi-empirical geometric and physical parameters of the maize canopy model (GPMCM) crop model adapted to northeast China were proposed based on a time-series field datasets collected from 11 sites in the Nong’an and Changling Counties of Jilin Province, China, during DOY (day of year) 163 to DOY 278 in 2021. The allocation characteristics of and correlations between each maize canopy parameter were investigated for the whole growing season using the 22 algorithms of crop parameters, and the following conclusions were obtained. (1) The high correlation coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>R</mi></semantics></math></inline-formula> mean = 0.79) of LAI with other canopy parameters indicated that it was a good indicator for predicting other parameters. (2) Better performance was achieved by the regression method based on the two-stage simulation. The root-mean-squared error (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi></mrow></semantics></math></inline-formula>) of geometric parameters including maize height, stem long radius, and short radius were 12.91 cm, 0.74 mm, and 0.73 mm, respectively, and the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi></mrow></semantics></math></inline-formula> of the physical parameters including the FAGB, AGB, VWC, and RWC of the stems and leaves, ranged from 0.05 kg/m<sup>2</sup> to 4.24 kg/m<sup>2</sup> (2.0% to 12.9% for mean absolute percentage error (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mi>A</mi><mi>P</mi><mi>E</mi></mrow></semantics></math></inline-formula>)). (3) The extension of the field-scale GPMCM to the 500 m MODIS-scale still provided a good accuracy (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mi>A</mi><mi>P</mi><mi>E</mi></mrow></semantics></math></inline-formula>: 11% to 18.5%) and confirmed the feasibility of the large-scale application of the GPMCM. The proposed CPMCM can predict the temporal dynamics of maize geometric and physical parameters, and it is helpful to establish the forward and reverse models of remote sensing and improve the inversion accuracy of crop parameters.https://www.mdpi.com/2072-4292/14/13/3017maize modelmaize canopy parametersleaf area index (LAI)above ground biomass (AGB)vegetation water content (VWC) |
| spellingShingle | Bingze Li Ming Ma Shengbo Chen Xiaofeng Li Si Chen Xingming Zheng Temporal Variation and Component Allocation Characteristics of Geometric and Physical Parameters of Maize Canopy for the Entire Growing Season Remote Sensing maize model maize canopy parameters leaf area index (LAI) above ground biomass (AGB) vegetation water content (VWC) |
| title | Temporal Variation and Component Allocation Characteristics of Geometric and Physical Parameters of Maize Canopy for the Entire Growing Season |
| title_full | Temporal Variation and Component Allocation Characteristics of Geometric and Physical Parameters of Maize Canopy for the Entire Growing Season |
| title_fullStr | Temporal Variation and Component Allocation Characteristics of Geometric and Physical Parameters of Maize Canopy for the Entire Growing Season |
| title_full_unstemmed | Temporal Variation and Component Allocation Characteristics of Geometric and Physical Parameters of Maize Canopy for the Entire Growing Season |
| title_short | Temporal Variation and Component Allocation Characteristics of Geometric and Physical Parameters of Maize Canopy for the Entire Growing Season |
| title_sort | temporal variation and component allocation characteristics of geometric and physical parameters of maize canopy for the entire growing season |
| topic | maize model maize canopy parameters leaf area index (LAI) above ground biomass (AGB) vegetation water content (VWC) |
| url | https://www.mdpi.com/2072-4292/14/13/3017 |
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