数字农科院2.0

Inversion of Crop Water Content Using Multispectral Data and Machine Learning Algorithms in the North China Plain

文献类型： 外文期刊

作者： Zhenghao Zhang;Gensheng Dou;Xin Zhao;Yang Gao;Saisai Liu;Anzhen Qin

作者机构：

关键词： multispectral remote sensing;random forest;unmanned aerial vehicle;vegetation index;winter wheat

期刊名称： Agronomy

ISSN： 2073-4395

年卷期： 2024 年 14 卷 10 期

页码：

收录情况： SCIE（2024版）

摘要： (1) Background: Accurate inversion of crop water content is key to making an intelligent irrigation decision. However, little effort has been devoted to accurately estimating the crop water content of winter wheat in the North China Plain. (2) Method: The crop water content of winter wheat was measured at jointing, flowering and grain-filling stages, respectively. UAV-based multispectral remote sensing images were used to calculate thirteen vegetation indices, including SAVI, EVI, R-M, NDRE, OSAVI, GOSAVI, REOSAVI, GBNDVI, NDVI, RVI, DVI, GNDVI, and TVI. Five machine learning (ML) algorithms (i.e., MLR, RF, PLSR, ElasticNet, and ridge regression) were adopted to estimate the crop water content of winter wheat at the three growth stages. The benchmark datasets, which include CWC as well as vegetation indices calculated based on spectral indices, were adopted to validate the performance of the ML models. (3) Results: The correlation coefficients ranged from 0.64 to 0.82 at different growth stages. The optimal vegetation indices were GNDVI for the jointing stage, NDRE for the flowering and the grain-filling stage, respectively. Among the five machine learning methods, random forest (RF) showed the best performance across the three growth stages, with its coefficient of determination (R2) of 0.80, or an increase by 20.1% than those of other models. In addition, the RMSE and RPD of the RF model at the flowering stage were 3.00% and 2.01, which significantly outperformed other models and growth stages. (4) Conclusion: This study may provide theoretical support and technical guidance for monitoring current water status in wheat crops, which is useful to develop a precise irrigation prescription map for local farmers. (5) Limitation: The main limitation of this study is that the sample size is relatively small and may not fully reflect the characteristics of the target groups. At the same time, subjectivity and bias may exist in the data collection, which may have a certain impact on the accuracy of the results. Future studies could consider expanding sample sizes and improving data collection methods to overcome these limitations.

分类号：

  • 相关文献

[1]Time Series Field Estimation of Rice Canopy Height Using an Unmanned Aerial Vehicle-Based RGB/Multispectral Platform. Ziqiu Li,Xiangqian Feng,Juan Li,Danying Wang,Weiyuan Hong,Jinhua Qin,Aidong Wang,Hengyu Ma,Qin Yao,Song Chen. 2024

[2]Multi-modal feature integration from UAV-RGB imagery for high-precision cotton phenotyping: A paradigm shift toward cost-effective agricultural remote sensing. Xiaoyu Zhi,Qiaomin Chen,Yingchun Han,Beifang Yang,Yaru Wang,Fengqi Wu,Shiwu Xiong,Yahui Jiao,Yunzhen Ma,Shilong Shang,Tao Lin,Yaping Lei,Yabing Li. 2025

[3]Estimating Fraction of Absorbed Photosynthetically Active Radiation of Winter Wheat Based on Simulated Sentinel-2 Data under Different Varieties and Water Stress. Sun Z.,Sun L.,Liu Y.,Li Y.,Crusiol L.G.T.,Chen R.,Wuyun D.. 2024

[4]Dynamic Harvest Index Estimation of Winter Wheat Based on UAV Hyperspectral Remote Sensing Considering Crop Aboveground Biomass Change and the Grain Filling Process. Jianqiang Ren,Ningdan Zhang,Xingren Liu,Shangrong Wu,Dandan Li. 2022

[5]Long-Term Inorganic Plus Organic Fertilization I.ncreases Yield And Yield S tability Of Winter Wheat. Chen, H, Deng, AX, Zhang, WJ, Li, W, Qi, YQ, Yang, TM, Zheng, CY, Cao, CF, Chen, F. 2018

[6]Effects of vegetation indices to the spatial changes of desert environment using EOS/MODIS data: A case study to Sangong inland arid ecosystem. Lu, Liping,Qin, Zhihao,Qin, Zhihao,Gao, Maofang,Zhao, Chengyi,Li, Wenjuan. 2006

[7]Comparison of two methods for monitoring leaf total chlorophyll content (LTCC) of wheat using field spectrometer data. Jin, X.,Wang, K.,Li, S.,Jin, X.,Diao, W.,Xiao, C.,Wang, K.,Li, S.,Wang, F.,Chen, B..

[8]Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method. Liang, Liang,Zhang, Lianpeng,Lin, Hui,Liang, Liang,Zhao, Shuhe,Liang, Liang,Di, Liping,Deng, Meixia,Qin, Zhihao.

[9]Remote sensing of crop production in China by production efficiency models: models comparisons, estimates and uncertainties. Tao, FL,Yokozawa, M,Zhang, Z,Xu, YL,Hayashi, Y.

[10]New fast detection method of forest fire monitoring and application based on FY-1D/MVISR data. Feng, Jianzhong,Tang, Huajun,Zhou, Qingbo,Chen, Zhongxin,Bai, Linyan,Feng, Jianzhong. 2008

[11]Spectral Reflectance and Vegetation Index Changes in Deciduous Forest Foliage Following Tree Removal: Potential for Deforestation Monitoring. Peng, D.,Hu, Y.,Li, Z..

[12]Comparison of vegetation indices and red-edge parameters for estimating grassland cover from canopy reflectance data. Liu, Zhan-Yu,Huang, Jing-Feng,Wu, Xin-Hong,Dong, Yong-Ping. 2007

[13]Remote sensing monitoring of wheat leaf rust based on UAV multispectral imagery and the BPNN method. Ju, Chengxin,Chen, Chen,Li, Rui,Zhao, Yuanyuan,Zhong, Xiaochun,Sun, Ruilin,Liu, Tao,Sun, Chengming. 2023

[14]Replacing The Red Band With T.he Red-Swir Band (0.74(Red)+0.26(Swir)) C an Reduce The Sensitivity Of Vegetation Indices To Soil Background. Chen, XH, Guo, ZF, Chen, J, Yang, W, Yao, YM, Zhang, CS, Cui, XH, Cao, X. 2019

[15]Monitoring the Rice Panicle Blast Control Period Based on UAV Multispectral Remote Sensing and Machine Learning. Bin Ma,Guangqiao Cao,Chaozhong Hu,Cong Chen. 2023

[16]Estimating wheat fractional vegetation cover using a density peak k-means algorithm based on hyperspectral image data. Da zhong LIU,Fei fei YANG,Sheng ping LIU. 2021

[17]The superiority of the normalized difference phenology index (NDPI) for estimating grassland aboveground fresh biomass. Dawei Xu,Cong Wang,Jin Chen,Miaogen Shen,Beibei Shen,Ruirui Yan,Zhenwang Li,Arnon Karnieli,Jiquan Chen,Yuchun Yan,Xu Wang,Baorui Chen,Dameng Yin,Xiaoping Xin. 2021

[18]The superiority of the normalized difference phenology index (NDPI) for estimating grassland aboveground fresh biomass. Dawei Xu,Cong Wang,Jin Chen,Miaogen Shen,Beibei Shen,Ruirui Yan,Zhenwang Li,Arnon Karnieli,Jiquan Chen,Yuchun Yan,Xu Wang,Baorui Chen,Dameng Yin,Xiaoping Xin. 2021

[19]Quantifying effect of tassels on near-ground maize canopy RGB images using deep learning segmentation algorithm. Shao, Mingchao,Nie, Chenwei,Cheng, Minghan,Yu, Xun,Bai, Yi,Ming, Bo,Song, Hongli,Jin, Xiuliang. 2021

[20]The spatiotemporal change of cropland and its impact on vegetation dynamics in the farming-pastoral ecotone of northern China. Deji Wuyun,Liang Sun,Zhongxin Chen,Anhong Hou,Luís Guilherme Teixeira Crusiol,Lifeng Yu,Ruiqing Chen,Zheng Sun. 2022

作者其他论文 更多>>