Remote sensing-based maize growth process parameters revel the maize yield: a comparison of field- and regional-scale
Abstract The accurate estimation of regional crop yields holds significant importance for optimizing subsequent resource allocation and maximizing economic returns in agriculture. Crop yield can be effectively estimated by assessing the overall growth status through long-term remote sensing observat...
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| Language: | English |
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BMC
2025-02-01
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| Series: | BMC Plant Biology |
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| Online Access: | https://doi.org/10.1186/s12870-025-06146-0 |
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| author | Minghan Cheng Xiuliang Jin Chenwei Nie Kaihua Liu Tianao Wu Yuping Lv Shuaibing Liu Xun Yu Yi Bai Yadong Liu Lin Meng Xiao Jia Yuan Liu Lili Zhou Fei Nan |
| author_facet | Minghan Cheng Xiuliang Jin Chenwei Nie Kaihua Liu Tianao Wu Yuping Lv Shuaibing Liu Xun Yu Yi Bai Yadong Liu Lin Meng Xiao Jia Yuan Liu Lili Zhou Fei Nan |
| author_sort | Minghan Cheng |
| collection | DOAJ |
| description | Abstract The accurate estimation of regional crop yields holds significant importance for optimizing subsequent resource allocation and maximizing economic returns in agriculture. Crop yield can be effectively estimated by assessing the overall growth status through long-term remote sensing observations. However, most previous studies have relied on remote sensing data from one or a few periods for yield estimation, thus lacking a comprehensive description of entire crop growth. Furthermore, past algorithms have not considered their applicability across different observational scales (e.g., unmanned aerial vehicle (UAV)- and satellite-observed). Considering this, we extracted four maize growth process parameters using Leaf Area Index (LAI) obtained from UAV (equipped with multispectral sensor, centimeter-level) and satellite (MODIS, 1 km) observations: PP_a (representing the duration of the crop growth period), PP_b (representing the peak growth stage of the crop), PP_c (representing the initial state of the crop), and LAImax (maximum LAI). These parameters were used to construct a maize yield estimation model applicable at both regional and field scales. The results indicate that the four process parameters extracted in this study can accurately estimate crop yields, with rRMSE = 14.08% at the field-scale and rRMSE = 17.75% at the regional-scale. Among these parameters, PP_a, representing the duration of the crop growth period, and the maximum LAI, are the parameters that individually contribute the most to the estimation accuracy. Moreover, the proposed method exhibited good spatial applicability (field-scale: Moran Index (MI) = -0.18; regional-scale: MI = 0.19). In conclusion, the parameters describing maize growth process derived from long-term-series observations can effectively estimate maize yield across different observation scales. This method not only facilitates the optimization of agronomic practices based on UAV observations but also supports the decision of regional agricultural policies based on satellite observations. Furthermore, crop yield estimation utilizing process-based parameters provides a new perspective for related studies. |
| format | Article |
| id | doaj-art-9d5cf02cc6e4467a90fdeb7380b442a8 |
| institution | Kabale University |
| issn | 1471-2229 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Plant Biology |
| spelling | doaj-art-9d5cf02cc6e4467a90fdeb7380b442a82025-02-09T12:27:58ZengBMCBMC Plant Biology1471-22292025-02-0125111510.1186/s12870-025-06146-0Remote sensing-based maize growth process parameters revel the maize yield: a comparison of field- and regional-scaleMinghan Cheng0Xiuliang Jin1Chenwei Nie2Kaihua Liu3Tianao Wu4Yuping Lv5Shuaibing Liu6Xun Yu7Yi Bai8Yadong Liu9Lin Meng10Xiao Jia11Yuan Liu12Lili Zhou13Fei Nan14Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College, Yangzhou UniversityJiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College, Yangzhou UniversityInstitute of Crop Sciences, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of AgricultureCollege of Agricultural Science and Engineering, Hohai UniversityCollege of Environmental Science and Engineering, Xiamen University of TechnologyCollege of Hydraulic Science and Engineering, Yangzhou UniversityInstitute of Crop Sciences, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of AgricultureInstitute of Crop Sciences, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of AgricultureInstitute of Crop Sciences, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of AgricultureInstitute of Crop Sciences, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of AgricultureInstitute of Crop Sciences, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of AgricultureInstitute of Crop Sciences, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of AgricultureInstitute of Crop Sciences, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of AgricultureInstitute of Crop Sciences, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of AgricultureInstitute of Crop Sciences, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of AgricultureAbstract The accurate estimation of regional crop yields holds significant importance for optimizing subsequent resource allocation and maximizing economic returns in agriculture. Crop yield can be effectively estimated by assessing the overall growth status through long-term remote sensing observations. However, most previous studies have relied on remote sensing data from one or a few periods for yield estimation, thus lacking a comprehensive description of entire crop growth. Furthermore, past algorithms have not considered their applicability across different observational scales (e.g., unmanned aerial vehicle (UAV)- and satellite-observed). Considering this, we extracted four maize growth process parameters using Leaf Area Index (LAI) obtained from UAV (equipped with multispectral sensor, centimeter-level) and satellite (MODIS, 1 km) observations: PP_a (representing the duration of the crop growth period), PP_b (representing the peak growth stage of the crop), PP_c (representing the initial state of the crop), and LAImax (maximum LAI). These parameters were used to construct a maize yield estimation model applicable at both regional and field scales. The results indicate that the four process parameters extracted in this study can accurately estimate crop yields, with rRMSE = 14.08% at the field-scale and rRMSE = 17.75% at the regional-scale. Among these parameters, PP_a, representing the duration of the crop growth period, and the maximum LAI, are the parameters that individually contribute the most to the estimation accuracy. Moreover, the proposed method exhibited good spatial applicability (field-scale: Moran Index (MI) = -0.18; regional-scale: MI = 0.19). In conclusion, the parameters describing maize growth process derived from long-term-series observations can effectively estimate maize yield across different observation scales. This method not only facilitates the optimization of agronomic practices based on UAV observations but also supports the decision of regional agricultural policies based on satellite observations. Furthermore, crop yield estimation utilizing process-based parameters provides a new perspective for related studies.https://doi.org/10.1186/s12870-025-06146-0Process parametersLeaf area indexMaize yieldRemote sensing |
| spellingShingle | Minghan Cheng Xiuliang Jin Chenwei Nie Kaihua Liu Tianao Wu Yuping Lv Shuaibing Liu Xun Yu Yi Bai Yadong Liu Lin Meng Xiao Jia Yuan Liu Lili Zhou Fei Nan Remote sensing-based maize growth process parameters revel the maize yield: a comparison of field- and regional-scale BMC Plant Biology Process parameters Leaf area index Maize yield Remote sensing |
| title | Remote sensing-based maize growth process parameters revel the maize yield: a comparison of field- and regional-scale |
| title_full | Remote sensing-based maize growth process parameters revel the maize yield: a comparison of field- and regional-scale |
| title_fullStr | Remote sensing-based maize growth process parameters revel the maize yield: a comparison of field- and regional-scale |
| title_full_unstemmed | Remote sensing-based maize growth process parameters revel the maize yield: a comparison of field- and regional-scale |
| title_short | Remote sensing-based maize growth process parameters revel the maize yield: a comparison of field- and regional-scale |
| title_sort | remote sensing based maize growth process parameters revel the maize yield a comparison of field and regional scale |
| topic | Process parameters Leaf area index Maize yield Remote sensing |
| url | https://doi.org/10.1186/s12870-025-06146-0 |
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