Enabling Deep-Neural-Network-Integrated Optical and SAR Data to Estimate the Maize Leaf Area Index and Biomass with Limited In Situ Data
Accurate estimation of the maize leaf area index (LAI) and biomass is of great importance in guiding field management and early yield estimation. Physical models and traditional machine learning methods are commonly used for LAI and biomass estimation. However, these models and methods mostly rely o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/21/5624 |
| _version_ | 1797466566425575424 |
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| author | Peilei Luo Huichun Ye Wenjiang Huang Jingjuan Liao Quanjun Jiao Anting Guo Binxiang Qian |
| author_facet | Peilei Luo Huichun Ye Wenjiang Huang Jingjuan Liao Quanjun Jiao Anting Guo Binxiang Qian |
| author_sort | Peilei Luo |
| collection | DOAJ |
| description | Accurate estimation of the maize leaf area index (LAI) and biomass is of great importance in guiding field management and early yield estimation. Physical models and traditional machine learning methods are commonly used for LAI and biomass estimation. However, these models and methods mostly rely on handcrafted features and theoretical formulas under idealized assumptions, which limits their accuracy. Deep neural networks have demonstrated great superiority in automatic feature extraction and complicated nonlinear approximation, but their application to LAI and biomass estimation has been hindered by the shortage of in situ data. Therefore, bridging the gap of data shortage and making it possible to leverage deep neural networks to estimate maize LAI and biomass is of great significance. Optical data cannot provide information in the lower canopy due to the limited penetrability, but synthetic aperture radar (SAR) data can do this, so the integration of optical and SAR data is necessary. In this paper, 158 samples from the jointing, trumpet, flowering, and filling stages of maize were collected for investigation. First, we propose an improved version of the mixup training method, which is termed mixup<sup>+</sup>, to augment the sample amount. We then constructed a novel gated Siamese deep neural network (GSDNN) based on a gating mechanism and a Siamese architecture to integrate optical and SAR data for the estimation of the LAI and biomass. We compared the accuracy of the GSDNN with those of other machine learning methods, i.e., multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), and a multilayer perceptron (MLP). The experimental results show that without the use of mixup<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>+</mo></msup></semantics></math></inline-formula>, the GSDNN achieved a similar accuracy to that of the simple neural network MLP in terms of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> and RMSE, and this was slightly lower than those of MLR, SVR, and RFR. However, with the help of mixup<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>+</mo></msup></semantics></math></inline-formula>, the GSDNN achieved state-of-the-art performance (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> = 0.71, 0.78, and 0.86 and RMSE = 0.58, 871.83, and 150.76 g/m<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula>, for LAI, Biomass_wet, and Biomass_dry, respectively), exceeding the accuracies of MLR, SVR, RFR, and MLP. In addition, through the integration of optical and SAR data, the GSDNN achieved better accuracy in LAI and biomass estimation than when optical or SAR data alone were used. We found that the most appropriate amount of synthetic data from mixup<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>+</mo></msup></semantics></math></inline-formula> was five times the amount of original data. Overall, this study demonstrates that the GSDNN + mixup<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>+</mo></msup></semantics></math></inline-formula> has great potential for the integration of optical and SAR data with the aim of improving the estimation accuracy of the maize LAI and biomass with limited in situ data. |
| first_indexed | 2024-03-09T18:41:36Z |
| format | Article |
| id | doaj.art-f62d8ad38cc848e6997dd8df475bad0c |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T18:41:36Z |
| publishDate | 2022-11-01 |
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| series | Remote Sensing |
| spelling | doaj.art-f62d8ad38cc848e6997dd8df475bad0c2023-11-24T06:42:03ZengMDPI AGRemote Sensing2072-42922022-11-011421562410.3390/rs14215624Enabling Deep-Neural-Network-Integrated Optical and SAR Data to Estimate the Maize Leaf Area Index and Biomass with Limited In Situ DataPeilei Luo0Huichun Ye1Wenjiang Huang2Jingjuan Liao3Quanjun Jiao4Anting Guo5Binxiang Qian6State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAccurate estimation of the maize leaf area index (LAI) and biomass is of great importance in guiding field management and early yield estimation. Physical models and traditional machine learning methods are commonly used for LAI and biomass estimation. However, these models and methods mostly rely on handcrafted features and theoretical formulas under idealized assumptions, which limits their accuracy. Deep neural networks have demonstrated great superiority in automatic feature extraction and complicated nonlinear approximation, but their application to LAI and biomass estimation has been hindered by the shortage of in situ data. Therefore, bridging the gap of data shortage and making it possible to leverage deep neural networks to estimate maize LAI and biomass is of great significance. Optical data cannot provide information in the lower canopy due to the limited penetrability, but synthetic aperture radar (SAR) data can do this, so the integration of optical and SAR data is necessary. In this paper, 158 samples from the jointing, trumpet, flowering, and filling stages of maize were collected for investigation. First, we propose an improved version of the mixup training method, which is termed mixup<sup>+</sup>, to augment the sample amount. We then constructed a novel gated Siamese deep neural network (GSDNN) based on a gating mechanism and a Siamese architecture to integrate optical and SAR data for the estimation of the LAI and biomass. We compared the accuracy of the GSDNN with those of other machine learning methods, i.e., multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), and a multilayer perceptron (MLP). The experimental results show that without the use of mixup<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>+</mo></msup></semantics></math></inline-formula>, the GSDNN achieved a similar accuracy to that of the simple neural network MLP in terms of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> and RMSE, and this was slightly lower than those of MLR, SVR, and RFR. However, with the help of mixup<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>+</mo></msup></semantics></math></inline-formula>, the GSDNN achieved state-of-the-art performance (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> = 0.71, 0.78, and 0.86 and RMSE = 0.58, 871.83, and 150.76 g/m<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula>, for LAI, Biomass_wet, and Biomass_dry, respectively), exceeding the accuracies of MLR, SVR, RFR, and MLP. In addition, through the integration of optical and SAR data, the GSDNN achieved better accuracy in LAI and biomass estimation than when optical or SAR data alone were used. We found that the most appropriate amount of synthetic data from mixup<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>+</mo></msup></semantics></math></inline-formula> was five times the amount of original data. Overall, this study demonstrates that the GSDNN + mixup<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>+</mo></msup></semantics></math></inline-formula> has great potential for the integration of optical and SAR data with the aim of improving the estimation accuracy of the maize LAI and biomass with limited in situ data.https://www.mdpi.com/2072-4292/14/21/5624gated Siamese deep neural networkmixup<sup>+</sup>LAIbiomassmaize |
| spellingShingle | Peilei Luo Huichun Ye Wenjiang Huang Jingjuan Liao Quanjun Jiao Anting Guo Binxiang Qian Enabling Deep-Neural-Network-Integrated Optical and SAR Data to Estimate the Maize Leaf Area Index and Biomass with Limited In Situ Data Remote Sensing gated Siamese deep neural network mixup<sup>+</sup> LAI biomass maize |
| title | Enabling Deep-Neural-Network-Integrated Optical and SAR Data to Estimate the Maize Leaf Area Index and Biomass with Limited In Situ Data |
| title_full | Enabling Deep-Neural-Network-Integrated Optical and SAR Data to Estimate the Maize Leaf Area Index and Biomass with Limited In Situ Data |
| title_fullStr | Enabling Deep-Neural-Network-Integrated Optical and SAR Data to Estimate the Maize Leaf Area Index and Biomass with Limited In Situ Data |
| title_full_unstemmed | Enabling Deep-Neural-Network-Integrated Optical and SAR Data to Estimate the Maize Leaf Area Index and Biomass with Limited In Situ Data |
| title_short | Enabling Deep-Neural-Network-Integrated Optical and SAR Data to Estimate the Maize Leaf Area Index and Biomass with Limited In Situ Data |
| title_sort | enabling deep neural network integrated optical and sar data to estimate the maize leaf area index and biomass with limited in situ data |
| topic | gated Siamese deep neural network mixup<sup>+</sup> LAI biomass maize |
| url | https://www.mdpi.com/2072-4292/14/21/5624 |
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