Rice Leaf Chlorophyll Content Estimation Using UAV-Based Spectral Images in Different Regions
Estimation of crop biophysical and biochemical characteristics is the key element for crop growth monitoring with remote sensing. With the application of unmanned aerial vehicles (UAV) as a remote sensing platform worldwide, it has become important to develop general estimation models, which can int...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/12/11/2832 |
| _version_ | 1797466094679621632 |
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| author | Songtao Ban Weizhen Liu Minglu Tian Qi Wang Tao Yuan Qingrui Chang Linyi Li |
| author_facet | Songtao Ban Weizhen Liu Minglu Tian Qi Wang Tao Yuan Qingrui Chang Linyi Li |
| author_sort | Songtao Ban |
| collection | DOAJ |
| description | Estimation of crop biophysical and biochemical characteristics is the key element for crop growth monitoring with remote sensing. With the application of unmanned aerial vehicles (UAV) as a remote sensing platform worldwide, it has become important to develop general estimation models, which can interpret remote sensing data of crops by different sensors and in different agroclimatic regions into comprehensible agronomy parameters. Leaf chlorophyll content (LCC), which can be measured as a soil plant analysis development (SPAD) value using a SPAD-502 Chlorophyll Meter, is one of the important parameters that are closely related to plant production. This study compared the estimation of rice (<i>Oryza sativa</i> L.) LCC in two different regions (Ningxia and Shanghai) using UAV-based spectral images. For Ningxia, images of rice plots with different nitrogen and biochar application rates were acquired by a 125-band hyperspectral camera from 2016 to 2017, and a total of 180 samples of rice LCC were recorded. For Shanghai, images of rice plots with different nitrogen application rates, straw returning, and crop rotation systems were acquired by a 5-band multispectral camera from 2017 to 2018, and a total of 228 samples of rice LCC were recorded. The spectral features of LCC in each study area were analyzed and the results showed that the rice LCC in both regions had significant correlations with the reflectance at the green, red, and red-edge bands and 8 vegetation indices such as the normalized difference vegetation index (NDVI). The estimation models of LCC were built using the partial least squares regression (PLSR), support vector regression (SVR), and artificial neural network (ANN) methods. The PLSR models tended to be more stable and accurate than the SVR and ANN models when applied in different regions with R<sup>2</sup> values higher than 0.7 through different validations. The results demonstrated that the rice canopy LCC in different regions, cultivars, and different types of sensor-based data shared similar spectral features and could be estimated by general models. The general models can be implied to a wider geographic extent to accurately quantify rice LCC, which is helpful for growth assessment and production forecasts. |
| first_indexed | 2024-03-09T18:31:53Z |
| format | Article |
| id | doaj.art-0e6e455e369d4e89ade2916751b78520 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-09T18:31:53Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-0e6e455e369d4e89ade2916751b785202023-11-24T07:26:19ZengMDPI AGAgronomy2073-43952022-11-011211283210.3390/agronomy12112832Rice Leaf Chlorophyll Content Estimation Using UAV-Based Spectral Images in Different RegionsSongtao Ban0Weizhen Liu1Minglu Tian2Qi Wang3Tao Yuan4Qingrui Chang5Linyi Li6Agricultural Information Institute of Science and Technology, Shanghai Academy of Agricultural Sciences, Shanghai 201403, ChinaSchool of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, ChinaAgricultural Information Institute of Science and Technology, Shanghai Academy of Agricultural Sciences, Shanghai 201403, ChinaCollege of Natural Resource and Environment, Northwest A&F University, Xianyang 712100, ChinaAgricultural Information Institute of Science and Technology, Shanghai Academy of Agricultural Sciences, Shanghai 201403, ChinaCollege of Natural Resource and Environment, Northwest A&F University, Xianyang 712100, ChinaAgricultural Information Institute of Science and Technology, Shanghai Academy of Agricultural Sciences, Shanghai 201403, ChinaEstimation of crop biophysical and biochemical characteristics is the key element for crop growth monitoring with remote sensing. With the application of unmanned aerial vehicles (UAV) as a remote sensing platform worldwide, it has become important to develop general estimation models, which can interpret remote sensing data of crops by different sensors and in different agroclimatic regions into comprehensible agronomy parameters. Leaf chlorophyll content (LCC), which can be measured as a soil plant analysis development (SPAD) value using a SPAD-502 Chlorophyll Meter, is one of the important parameters that are closely related to plant production. This study compared the estimation of rice (<i>Oryza sativa</i> L.) LCC in two different regions (Ningxia and Shanghai) using UAV-based spectral images. For Ningxia, images of rice plots with different nitrogen and biochar application rates were acquired by a 125-band hyperspectral camera from 2016 to 2017, and a total of 180 samples of rice LCC were recorded. For Shanghai, images of rice plots with different nitrogen application rates, straw returning, and crop rotation systems were acquired by a 5-band multispectral camera from 2017 to 2018, and a total of 228 samples of rice LCC were recorded. The spectral features of LCC in each study area were analyzed and the results showed that the rice LCC in both regions had significant correlations with the reflectance at the green, red, and red-edge bands and 8 vegetation indices such as the normalized difference vegetation index (NDVI). The estimation models of LCC were built using the partial least squares regression (PLSR), support vector regression (SVR), and artificial neural network (ANN) methods. The PLSR models tended to be more stable and accurate than the SVR and ANN models when applied in different regions with R<sup>2</sup> values higher than 0.7 through different validations. The results demonstrated that the rice canopy LCC in different regions, cultivars, and different types of sensor-based data shared similar spectral features and could be estimated by general models. The general models can be implied to a wider geographic extent to accurately quantify rice LCC, which is helpful for growth assessment and production forecasts.https://www.mdpi.com/2073-4395/12/11/2832UAVspectral imagingricechlorophyll content |
| spellingShingle | Songtao Ban Weizhen Liu Minglu Tian Qi Wang Tao Yuan Qingrui Chang Linyi Li Rice Leaf Chlorophyll Content Estimation Using UAV-Based Spectral Images in Different Regions Agronomy UAV spectral imaging rice chlorophyll content |
| title | Rice Leaf Chlorophyll Content Estimation Using UAV-Based Spectral Images in Different Regions |
| title_full | Rice Leaf Chlorophyll Content Estimation Using UAV-Based Spectral Images in Different Regions |
| title_fullStr | Rice Leaf Chlorophyll Content Estimation Using UAV-Based Spectral Images in Different Regions |
| title_full_unstemmed | Rice Leaf Chlorophyll Content Estimation Using UAV-Based Spectral Images in Different Regions |
| title_short | Rice Leaf Chlorophyll Content Estimation Using UAV-Based Spectral Images in Different Regions |
| title_sort | rice leaf chlorophyll content estimation using uav based spectral images in different regions |
| topic | UAV spectral imaging rice chlorophyll content |
| url | https://www.mdpi.com/2073-4395/12/11/2832 |
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