Estimation of Rice Panicle Biomass by Remotely Sensed Photosynthetically Active Absorption

doi:10.15933/j.cnki.1004-3268.2024.06.017

Abstract

Abstract: Due to the complexity of canopy geometry and spectral characteristics,the traditional vegetation index inversion method is difficult to estimate the biomass of rice panicle. Based on the absorption coefficient of photosynthetically active radiation obtained by the UAV platform，this paper established a remote model for estimating panicle biomass based on photosynthetically active absorption by analyzing the change of the photosynthetically active radiation absorption capacity in different spectral regions during rice post‑heading period.The results showed that compared with the traditional empirical model using vegetation index,the model using blue‑red absorption difference index proposed in this paper had higher accuracy for panicle biomass estimation,with the coefficient of determination of 0.83,the root mean square error of 147.50 g/m²,and the coefficient of variation of 10.19%.Using the calibrated model for validation samples from different year and location,the validation accuracy was quite high with R² of 0.72,which presented the good migration ability of the model.Therefore,the model can achieve accurate estimation of rice panicle biomass by remotely sensed photosynthetically active absorption,which is helpful to improve the precision and efficiency of crop yield estimation at large scales.

Key words: Rice, Panicle biomass, UAV remote sensing, Photosynthetically active radiation, Absorption coefficient

摘要： 受水稻抽穗后冠层几何和光谱特征复杂性的影响，基于传统植被指数的遥感方法难以对水稻稻穗的生物量进行精确估算。基于无人机平台获取的光合有效辐射区域吸收系数，通过分析水稻抽穗后对不同波段光合有效辐射吸收能力的变化，建立基于光合有效吸收的穗生物量估测模型。结果表明，基于蓝红吸收差值指数建立的模型相较于传统的植被指数经验模型对穗生物量有更优的估算精度，对于2018年海南试验田获取的数据，该模型的决定系数（R²）为0.83，均方根误差（RMSE）为147.50 g/m²，变异系数（CV）为10.19%，跨年跨地测试的R²为0.72，证明该模型具有良好的迁移能力。因此，基于光合有效吸收的穗生物量估测模型实现了稻穗生物量的遥感准确估测，有助于提升农作物大面积估产精度与估产效率。

关键词: 水稻, 穗生物量, 无人机遥感, 光合有效辐射, 吸收系数

CLC Number:

S127

ZHANG Chaoran, PENG Yi, YANG Kaili, YUAN Ningge, LI Yuanjin, LIU Xiaojuan, WU Huili. Estimation of Rice Panicle Biomass by Remotely Sensed Photosynthetically Active Absorption[J]. Journal of Henan Agricultural Sciences, 2024, 53(6): 154-161.

张朝冉, 彭漪, 杨凯丽, 袁宁鸽, 黎远金, 刘小娟, 吴慧丽. 基于光合有效吸收的水稻穗生物量遥感估测[J]. 河南农业科学, 2024, 53(6): 154-161.

References

［1］康艺维，陈玉宇，张迎信.水稻粒型基因克隆研究进展及育种应用展望［J］.中国水稻科学，2020，34（6）：479‑490.
KANG Y W，CHEN Y Y，ZHANG Y X.Research progress and breeding prospects of grain size associated genes in rice［J］.Chinese Journal of Rice Science，2020，34（6）：479‑490.

［2］程式华. 中国水稻育种百年发展与展望［J］.中国稻米，2021，27（4）：1‑6.

CHENG S H.One‑hundred years’ development and prospect of rice breeding in China［J］.China Rice，2021，27（4）：1‑6.

［3］杨凯丽.几何和光谱信息相结合的无人机遥感水稻全生育期长势参数估测［D］.武汉：武汉大学，2023.

YANG K L.Combining geometric and spectral information of rice throughout the growing season using UAV remote sensing for growth parameters estimation［D］.Wuhan：Wuhan University，2023.

［4］陈青春，蓝盾，赵新飞，等.不同生育期水稻干物质量与产量的关系研究［J］.湖北农业科学，2017，56（7）：1221‑1227.

CHEN Q C，LAN D，ZHAO X F，et al.Study on the relationship between dry weight and yield of rice at different growth stages［J］.Hubei Agricultural Sciences，2017，56（7）：1221‑1227.

［5］何维.基于ASAR和生长模拟模型的水稻长势监测研究［D］.北京：中国林业科学研究院，2007.

HE W.Rice monitoring based on the connection of ASAR data with crop growth simulation model［D］.Beijing：Chinese Academy of Forestry，2007.

［6］王晗，向友珍，李汪洋，等.基于无人机多光谱遥感的冬油菜地上部生物量估算［J］.农业机械学报，2023，54 （8）：218‑229.

WANG H，XIANG Y Z，LI W Y，et al.Estimation of winter rapeseed above‑ground biomass based on UAV multi‑spectral remote sensing［J］.Transactions of the Chinese Society for Agricultural Machinery，2023，54（8）：218‑229.

［7］魏鹏飞，徐新刚，李中元，等.基于无人机多光谱影像的夏玉米叶片氮含量遥感估测［J］.农业工程学报，2019，35（8）：126‑133.

WEI P F，XU X G，LI Z Y，et al.Remote sensing estimation of nitrogen content in summer maize leaves based on multispectral images of UAV［J］.Transactions of the Chinese Society of Agricultural Engineering，2019，35 （8）：126‑133.

［8］向友珍，安嘉琪，赵笑，等.基于无人机多光谱遥感的大豆生长参数和产量估算［J］.农业机械学报，2023，54 （8）：230‑239.

XIANG Y Z，AN J Q，ZHAO X，et al.Soybean growth parameters and yield estimation based on UAV multispectral remote sensing［J］.Transactions of the Chinese Society for Agricultural Machinery，2023，54（8）：230‑239.

［9］刘杨，冯海宽，黄珏，等.基于无人机数码影像的马铃薯生物量估算［J］.农业工程学报，2020，36（23）：181‑192.

LIU Y，FENG H K，HUANG J，et al.Estimation of potato biomass based on UAV digital images［J］.Transactions of

the Chinese Society of Agricultural Engineering，2020，36 （23）：181‑192.

［10］杨俊，丁峰，陈晨，等.小麦生物量及产量与无人机图像特征参数的相关性［J］.农业工程学报，2019，35 （23）：104‑110.

YANG J，DING F，CHEN C，et al.Correlation of wheat biomass and yield with UAV image characteristic parameters［J］.Transactions of the Chinese Society of Agricultural Engineering，2019，35（23）：104‑110.

［11］郭燕，井宇航，贺佳，等.基于无人机影像多源信息的冬小麦生物量与产量估算［J］.河南农业科学，2023，52（12）：149‑161.

GUO Y，JING Y H，HE J，et al.Winter wheat aboveground biomass and yield estimation based on multi‑source information from UAV imagery［J］.Journal of Henan Agricultural Sciences，2023，52（12）：149‑161.

［12］臧贺藏，王从胜，赵巧丽，等.基于深度学习的小麦倒伏自动分类方法研究［J］.河南农业科学，2023，52 （11）：167‑173.

ZANG H C，WANG C S，ZHAO Q L，et al.Study on automatic classification method of wheat lodging based on deep learning［J］.Journal of Henan Agricultural Sciences，2023，52（11）：167‑173.

［13］李敏，郭雷风，王瑞利.无人机遥感在马铃薯田间表型研究中的应用［J］.河南农业科学，2023，52（9）：24‑32.

LI M，GUO L F，WANG R L.Application of unmanned aerial vehicle（UAV）remote sensing in the study of potato field phenotype［J］.Journal of Henan Agricultural Sciences，2023，52（9）：24‑32.

［14］曹中盛，李艳大，黄俊宝，等.基于无人机数码影像的水稻叶面积指数监测［J］.中国水稻科学，2022，36 （3）308‑317.

CAO Z S，LI Y D，HUANG J B，et al.Monitoring rice leaf area index based on unmanned aerial vehicle（UAV）digital images［J］.Chinese Journal of Rice Science，2022，36（3）：308‑317.

［15］LI S Y，YUAN F，ATA‑UI‑KARIM S T，et al.Combining color indices and textures of UAV‑based digital imagery for rice LAI estimation［J］.Remote Sensing，2019，11（15）：1763.

[16］JIANG Q，FANG S H，PENG Y，et al.UAV‑based biomass estimation for rice‑combining spectral，TIN‑based structural and meteorological features［J］.Remote Sensing，2019，11（7）：890.

［17］KAWAMURA K，ASAI H，YASUDA T，et al.Field phenotyping of plant height in an upland rice field in Laos using low‑cost small unmanned aerial vehicles（UAVs）［J］. Plant Production Science，2020，23（4）：452‑465.

［18］ZHOU C Q，YE H B，HU J，et al.Automated counting of rice panicle by applying deep learning model to images from unmanned aerial vehicle platform［J］.Sensors，2019，19（14）：3106.

［19］万亮，杜晓月，陈硕博，等.基于无人机多源图谱融合的水稻稻穗表型监测［J］.农业工程学报，2022，38（9）：162‑170.

WAN L，DU X Y，CHEN S B，et al.Rice panicle phenotyping using UAV‑based multi‑source spectral image data fusion［J］.Transactions of the Chinese Society of Agricultural Engineering，2022，38（9）：162‑170.

［20］GITELSON A A.Remote estimation of fraction of radiation absorbed by photosynthetically active vegetation：Generic algorithm for maize and soybean［J］.Remote Sensing Letters，2019，10（3）：283‑291.

［21］LUO S J，JIANG X Q，YANG K L，et al.Multispectral remote sensing for accurate acquisition of rice phenotypes：Impacts of radiometric calibration and unmanned aerial vehicle flying altitudes［J］.Frontiers in Plant Science，2022，13：958106.

［22］金桂秀，李相奎.北方水稻栽培［M］.济南：山东科学技术出版社，2019.

JIN G X，LI X K.Northern rice cultivation［M］.Ji’nan：Shandong Science and Technology Press，2019.

［23］蒋琴素，成琪璐，徐礼根，等.基于穗光谱指数的水稻产量预测［J］.浙江农业学报，2018，30（2）：187‑193.

JIANG Q S，CHENG Q L，XU L G，et al.Rice yield prediction with panicle spectral indices［J］.Acta Agriculturae Zhejiangensis，2018，30（2）：187‑193.

［24］GAMON J A，PEÑUELAS J，FIELD C B.A narrow‑waveband spectral index that tracks diurnal changes in photosynthetic efficiency［J］.Remote Sensing of Environment，1992，41（1）：35‑44.

［25］BLACKBURN G A. Quantifying chlorophylls and caroteniods at leaf and canopy scales［J］.Remote Sensing of Environment，1998，66（3）：273‑285.

［26］SIMS D A，GAMON J A.Relationships between leaf pigment content and spectral reflectance across a wide range of species，leaf structures and developmental stages［J］.Remote Sensing of Environment，2002，81 （2/3）：337‑354.

［27］GITELSON A，ARKEBAUER T，SOLOVCHENKO A，et al.An insight into spectral composition of light available for photosynthesis via remotely assessed absorption coefficient at leaf and canopy levels［J］.Photosynthesis Research，2022，151（1）：47‑60.

［28］PENG Y，SOLOVCHENKO A，ZHANG C R，et al.Remote sensing of rice phenology and physiology via absorption coefficient derived from unmanned aerial vehicle imaging［J］.Precision Agriculture，2024，25 （1）：285‑302.

［29］蒋琦.基于无人机遥感影像的水稻生物量估测研究［D］.武汉：武汉大学，2021.

JIANG Q.Remote estimation of rice above ground biomass with unmanned aerial vehicle data［D］.Wuhan：Wuhan University，2021.

［30］段博.基于无人机多光谱影像的水稻生长参数提取和估产［D］.武汉：武汉大学，2021.

DUAN B.Remote estimation of rice growth parameter and yield with unmanned aerial vehicle multispectral data［D］.Wuhan：Wuhan University，2021.

［31］BRICAUD A，CLAUSTRE H，RAS J，et al.Natural variability of phytoplanktonic absorption in oceanic waters：Influence of the size structure of algal populations［J］.Journal of Geophysical Research‑Oceans，2004，109（C11）：C11010.

［32］FÉRET J B，GITELSON A A，NOBLE S D，et al.PROSPECT‑D：Towards modeling leaf optical properties through a complete lifecycle［J］.Remote Sensing of Environment，2017，193：204‑215.

[1]	LIU Qihua, SUN Zhaowen, YIN Xiubo, ZHENG Chongke. Effect of Optimal Reduction of Chemical Fertilizer on Eating and Nutritional Quality of Superior and Inferior Grains of Dry Direct‑Sowing Rice in Wheat‑Rice Rotation Cropping District [J]. Journal of Henan Agricultural Sciences, 2024, 53(6): 27-36.
[2]	DONG Hongrui, ZHU Jie, ZHOU Yong, LI Haoxuan, YANG Wei, JIANG Mengdie, ZHU Bo, NIE Jiangwen, LIU Zhangyong. Effects of Panicle Potassium Fertilizer Rates and Foliar Fertilizer on Rice Physiological Characteristics，Yield and Quality under Crayfish‑Rice Integrated System [J]. Journal of Henan Agricultural Sciences, 2024, 53(5): 30-39.
[3]	ZHANG Yongze, WANG Ruigang, WANG Yimei, WANG Zihao, LI Junzhou, DU Yanxiu, SUN Hongzheng, PENG Ting, ZHAO Quanzhi, ZHANG Jing. Effects of Sowing Date and Nitrogen Application Rate on Population Quality，Yield and Nitrogen Use Efficiency of Medium Indica Hybrid Rice [J]. Journal of Henan Agricultural Sciences, 2024, 53(4): 37-46.
[4]	LIU Yue, ZHOU Meng, LUO Haiwei, DANG Chengcheng, HAO Rongrong, HU Yuting, YAN Peng, CHEN Longzhou, LIU Yangxuan, TIAN Xiaohai. Optimal Zinc Application Rate for Rice Yield and Quality Improvement in Jianghan Plain [J]. Journal of Henan Agricultural Sciences, 2024, 53(3): 43-52.
[5]	QU Xiaotian, WANG Ya’nan, XU Qianru, LI Xueyang, ZHANG Shenli, ZHANG Erqin, ZHANG Gaiping. Expression and Purification of H5N1 Subtype Avian Influenza Virus HA Protein in Rice Endosperm [J]. Journal of Henan Agricultural Sciences, 2024, 53(3): 125-132.
[6]	FAN Yifan, ZHANG Yanyan, WANG Yimei, LI Junzhou, DU Yanxiu, SUN Hongzheng, PENG Ting, ZHAO Quanzhi, ZHANG Jing. Effects of Temperature and Light Conditions on Yield and Quality of Indica Rice under Different Sowing Dates [J]. Journal of Henan Agricultural Sciences, 2024, 53(2): 17-27.
[7]	DU Xueli, KE Luyao, LIU Zhan, DAI Mingzhu, ZHOU Yu, LI Jianxing, XU Jiuliang. Effects of Foliar Magnesium on Yield，Quality and Contents of Anthocyanin and Metabolites of Purple Rice [J]. Journal of Henan Agricultural Sciences, 2024, 53(2): 28-36.
[8]	ZHAO Biao, FENG Xu, HE Fumeng, XU Yongqing, SHI Qihai, FENG Yanzhong, LI Fenglan. Preparation of EM Selenium‑Enriched Yeast Agent and Its Application in Selenium‑Enriched Rice Production [J]. Journal of Henan Agricultural Sciences, 2023, 52(8): 26-35.
[9]	XIE Wenxiao, JIANG Xiuying, LÜ Jun, PAN Zhengyan, HAN Yong, WANG Qingxin, LI Jianguo. Effect of Functional Leaves at Different Positions on Grain Filling Characteristics and Quality of Different Panicle Types of Rice Varieties [J]. Journal of Henan Agricultural Sciences, 2023, 52(8): 36-44.
[10]	ZHANG Licheng, LI Juan, ZHANG Mingqing, GU Zuchao. Cabbage Yield and Nutrient Utilization under Substitution of Organic Manure for Fertilizer in Vegetable‑Rice Rotation in Lateritic Red Soil [J]. Journal of Henan Agricultural Sciences, 2023, 52(8): 87-95.
[11]	DOU Dandan, SUN Jianjun, WANG Dexin, GUO Yuxi, GUO Xinhai, DING Chaoming. Effect of Sowing Date on Yield and Quality of Early and Middle Rice [J]. Journal of Henan Agricultural Sciences, 2023, 52(7): 12-23.
[12]	CHENG Zhiqiang, FANG Shenghui. Rice Phenotypic Parameters Extraction and Biomass Estimation Based on Three⁃Dimensional Model [J]. Journal of Henan Agricultural Sciences, 2023, 52(7): 144-153.
[13]	LEI Zhenshan, LI Meng, WEI Yunfei, LIU Qiuyuan, LIU Juan, WANG Fujuan, JI Xin. Effects of Nitrogen Fertilizer Management on Yield and Quality of Japonica Rice with Good Taste in Southern Henan [J]. Journal of Henan Agricultural Sciences, 2023, 52(6): 12-21.
[14]	HUANG Hai, QU Xiaojie, LIU Jinhai, PENG Zanwen. Expression Patterns of Genes of Four Transcription Factor Families in Different Ploidy Rice under Salt⁃Alkali Stress [J]. Journal of Henan Agricultural Sciences, 2023, 52(6): 22-33.
[15]	ZHANG Xiujin, ZHANG Ronghui, CAI Jinghang, WANG Guokun, CHAI Guanqun, HUANG Chengling, FAN Chengwu. Screening and Health Risk Assessment of Low Accumulation Rice Varieties in Nickel Polluted Paddy Fields [J]. Journal of Henan Agricultural Sciences, 2023, 52(6): 61-69.