Establishment of soil moisture model based on hyperspectral data and growth parameters of winter wheat

IF 2.2 2区 农林科学 Q2 AGRICULTURAL ENGINEERING
Xizhi Lyu, Weimin Xing, Yuguo Han, Zhigong Peng, Baozhong Zhang, Muhammad Roman
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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.
基于冬小麦生长参数和高光谱数据的土壤水分模型建立
基于植物冠层光谱数据的大面积土壤水分状态诊断一直是农业灌溉研究的热点之一。然而,现有的仅利用光谱参数构建的土壤水分预测模型,在不考虑植物生长过程的情况下,不可避免地会增加预测误差。本研究对冬小麦冠层光谱参数与作物生长过程、土壤含水量之间的相关性进行了研究,最终构建了以生长日数为参数的土壤含水量预测模型。结果表明,在整个生育期,冬小麦植株含水量呈下降趋势。植株含水量与0 ~ 50 cm土层土壤含水量相关性最好。在不同生长阶段,即使土壤含水量相同,植株含水量和特征光谱反射率也不同。因此,在利用特征光谱参数与土壤含水量关系建立的模型中加入作物生长期参数,以提高预测精度。结果发现,在整个生长期建立的模型的决定系数(R2)都有很大的提高,在0.54 ~ 0.60之间。然后,选取精度最高的两个特征光谱参数OSAVI (Optimized Soil Adjusted Vegetation Index)和Rg/Rr建立的模型进行模型验证。OSAVI与土壤含水量、Rg/Rr、土壤含水量相关性仍显著(p<0.05)。R2、MAE和RMSE验证模型分别为0.53和0.58、3.19和2.97、4.76和4.41,具有足够的准确性,可以应用于大面积的领域。提出了OSAVI和Rg/Rr的灌溉上限和下限。研究结果对中国北方冬小麦的农业生产具有指导意义。关键词:冬小麦,冠层光谱,生长过程,土壤含水量,灌溉阈值,土壤水分模型预测[DOI: 10.25165/j.i jjabp .20231603.7268]引用本文:吕晓忠,邢文明,韩永刚,彭志刚,张宝忠,Roman M.基于冬小麦生长参数和高光谱数据的土壤水分模型建立农业与生物工程学报,2023;16(3): 160 - 168。
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来源期刊
CiteScore
4.30
自引率
12.50%
发文量
88
审稿时长
24 weeks
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.
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