Xizhi Lyu, Weimin Xing, Yuguo Han, Zhigong Peng, Baozhong Zhang, Muhammad Roman
{"title":"Establishment of soil moisture model based on hyperspectral data and growth parameters of winter wheat","authors":"Xizhi Lyu, Weimin Xing, Yuguo Han, Zhigong Peng, Baozhong Zhang, Muhammad Roman","doi":"10.25165/j.ijabe.20231603.7268","DOIUrl":null,"url":null,"abstract":"Large area of soil moisture status diagnosis based on plant canopy spectral data remains one of the hot spots of agricultural irrigation. However, the existing soil water prediction model constructed by the spectral parameters without considering the plant growth process will inevitably increase the prediction errors. This study carried out research on the correlations among spectral parameters of the canopy of winter wheat, crop growth process, and soil water content, and finally constructed the soil water content prediction model with the growth days parameter. The results showed that the plant water content of winter wheat tended to decrease during the whole growth period. The plant water content had the best correlations with the soil water content of the 0-50 cm soil layer. At different growth stages, even if the soil water content was the same, the plant water content and characteristic spectral reflectance were also different. Therefore, the crop growing days parameter was added to the model established by the relationships between characteristic spectral parameters and soil water content to increase the prediction accuracy. It is found that the determination coefficient (R2) of the models built during the whole growth period was greatly increased, ranging from 0.54 to 0.60. Then, the model built by OSAVI (Optimized Soil Adjusted Vegetation Index) and Rg/Rr, two of the highest precision characteristic spectral parameters, were selected for model validation. The correlation between OSAVI and soil water content, Rg/Rr, and soil water content were still significant (p<0.05). The R2, MAE, and RMSE validation models were 0.53 and 0.58, 3.19 and 2.97, 4.76 and 4.41, respectively, which was accurate enough to be applied in a large-area field. Furthermore, the upper and lower irrigation limit of OSAVI and Rg/Rr were put forward. The research results could guide the agricultural production of winter wheat in northern China. Keywords: Winter wheat, Canopy spectra, Growth process, Soil water content, Irrigation threshold, Soil moisture model prediction DOI: 10.25165/j.ijabe.20231603.7268 Citation: Lyu X Z, Xing W M, Han Y G, Peng Z G, Zhang B Z, Roman M. Establishment of soil moisture model based on hyperspectral data and growth parameters of winter wheat. Int J Agric & Biol Eng, 2023; 16(3): 160–168.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"142 1","pages":"0"},"PeriodicalIF":2.2000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Agricultural and Biological Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.25165/j.ijabe.20231603.7268","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
Large area of soil moisture status diagnosis based on plant canopy spectral data remains one of the hot spots of agricultural irrigation. However, the existing soil water prediction model constructed by the spectral parameters without considering the plant growth process will inevitably increase the prediction errors. This study carried out research on the correlations among spectral parameters of the canopy of winter wheat, crop growth process, and soil water content, and finally constructed the soil water content prediction model with the growth days parameter. The results showed that the plant water content of winter wheat tended to decrease during the whole growth period. The plant water content had the best correlations with the soil water content of the 0-50 cm soil layer. At different growth stages, even if the soil water content was the same, the plant water content and characteristic spectral reflectance were also different. Therefore, the crop growing days parameter was added to the model established by the relationships between characteristic spectral parameters and soil water content to increase the prediction accuracy. It is found that the determination coefficient (R2) of the models built during the whole growth period was greatly increased, ranging from 0.54 to 0.60. Then, the model built by OSAVI (Optimized Soil Adjusted Vegetation Index) and Rg/Rr, two of the highest precision characteristic spectral parameters, were selected for model validation. The correlation between OSAVI and soil water content, Rg/Rr, and soil water content were still significant (p<0.05). The R2, MAE, and RMSE validation models were 0.53 and 0.58, 3.19 and 2.97, 4.76 and 4.41, respectively, which was accurate enough to be applied in a large-area field. Furthermore, the upper and lower irrigation limit of OSAVI and Rg/Rr were put forward. The research results could guide the agricultural production of winter wheat in northern China. Keywords: Winter wheat, Canopy spectra, Growth process, Soil water content, Irrigation threshold, Soil moisture model prediction DOI: 10.25165/j.ijabe.20231603.7268 Citation: Lyu X Z, Xing W M, Han Y G, Peng Z G, Zhang B Z, Roman M. Establishment of soil moisture model based on hyperspectral data and growth parameters of winter wheat. Int J Agric & Biol Eng, 2023; 16(3): 160–168.
期刊介绍:
International Journal of Agricultural and Biological Engineering (IJABE, https://www.ijabe.org) is a peer reviewed open access international journal. IJABE, started in 2008, is a joint publication co-sponsored by US-based Association of Agricultural, Biological and Food Engineers (AOCABFE) and China-based Chinese Society of Agricultural Engineering (CSAE). The ISSN 1934-6344 and eISSN 1934-6352 numbers for both print and online IJABE have been registered in US. Now, Int. J. Agric. & Biol. Eng (IJABE) is published in both online and print version by Chinese Academy of Agricultural Engineering.