文献类型：期刊文献

中文题名：作物水分胁迫指数模型中下限温度的参数化:以栓皮栎人工林为例

英文题名：Parametrization of lower limit temperature in crop water stress index model:A case study of Quercus variabilis plantation

作者：巴音吉[1] 刘琳奇[2] 彭青[3] 张功[3] 陆森[4,5] 罗坤水[2] 张劲松[4,5]

第一作者：巴音吉

机构：[1]中国地质调查局烟台海岸带地质调查中心,山东烟台264000;[2]江西省林业科学院/江西南昌城市生态系统定位观测研究站,南昌330013;[3]江西农业大学,南昌330013;[4]中国林业科学研究院林业研究所,北京100091;[5]河南小浪底森林生态系统国家野外科学观测研究站,河南济源454650

年份：2024

卷号：35

期号：7

起止页码：1866-1876

中文期刊名：应用生态学报

外文期刊名：Chinese Journal of Applied Ecology

收录：CSTPCD;;Scopus;北大核心:【北大核心2023】；CSCD:【CSCD2023_2024】；PubMed;

基金：中国地质调查局项目(DD20220885);江西省林业科学院基础研究与人才科研专项(2024520801)资助。

语种：中文

中文关键词：作物水分胁迫指数;BP神经网络模型;水分诊断;下限温度

外文关键词：crop water stress index;BP neural network model;water diagnosis;lower limit temperature

分类号：S792.18

摘要：作物水分胁迫指数(CWSI)模型中的下限温度指水分充足时的冠层温度(T_(c))或冠气温差(dT),对模型量化植被水分状况精度有较大影响。目前,基于数据驱动方法直接估算下限温度已在大田作物中取得成功,但尚未见其在森林生态系统中的适用性报道。本研究以太行山南麓栓皮栎人工林为研究对象,连续同步观测T_(c)和气象数据,评估使用多元线性回归模型和BP神经网络模型估算下限温度的可行性,以及CWSI指示人工林水分状况的精度。结果表明:在不具备灌溉条件的森林生态系统中,将以土壤含水量、风速、净辐射、饱和水汽压差、气温作为输入参数的多元线性回归模型与BP神经网络模型中的土壤水分条件设为饱和,即可获得下限温度;将下限温度与理论公式确定的上限温度结合,对实测T_(c)或dT进行归一化获得CWSI,可实现对栓皮栎人工林水分状况的无损、快速、自动诊断。其中,基于BP神经网络模型确定的水分充足条件下的dT作为下限温度,并与上限温度结合获得CWSI,最适合精准量化人工林水分状况,与实测水分状况之间的决定系数、均方根误差和一致性指数分别为0.81、0.08和0.90。本研究结果可为森林生态系统水分状况的高效、精准监测提供参考方法。
The lower limit temperature in the crop water stress index(CWSI)model refers to the canopy temperature(T_(c))or the canopy-air temperature differences(dT)under well-watered conditions,which has significant impacts on the accuracy of the model in quantifying plant water status.At present,the direct estimation of lower limit temperature based on data-driven method has been successfully used in crops,but its applicability has not been tes-ted in forest ecosystems.We collected continuously and synchronously T_(c) and meteorological data in a Quercus variabilis plantation at the southern foot of Taihang Mountain to evaluate the feasibility of multiple linear regression model and BP neural network model for estimating the lower limit temperature and the accuracy of the CWSI indicating water status of the plantation.The results showed that,in the forest ecosystem without irrigation conditions,the lower limit temperature could be obtained by setting soil moisture as saturation in the multiple linear regression mo-del and the BP neural network model with soil water content,wind speed,net radiation,vapor pressure deficit and air temperature as input parameters.Combining the lower limit temperature and the upper limit temperature determined by the theoretical equation to normalize the measured T_(c) and dT could realize the non-destructive,rapid,and automatic diagnosis of the water status of Q.variabilis plantation.Among them,the CWSI obtained by combining the lower limit temperature determined by the dT under well-watered condition calculated by the BP neural network model and the upper limit temperature was the most suitable for accurate monitoring water status of the plantation.The coefficient of determination,root mean square error,and index of agreement between the calculated CWSI and measured CWSI were 0.81,0.08,and 0.90,respectively.This study could provide a reference method for efficient and accurate monitoring of forest ecosystem water status.