利用MODIS数据和BP神经网络重构美国区域尺度大豆日光诱导叶绿素荧光  

Reconstruction of U.S.Regional-Scale Soybean SIF Based on MODIS Data and BP Neural Network

作　　者：姚建恩 刘海秋 杨曼 冯金赢 陈秀 张佩佩 YAO Jianen;LIU Haiqiu;YANG Man;FENG Jinying;CHEN Xiu;ZHANG Peipei(Anhui Agricultural University Imformation and Artificial Intelligence College,Hefei 230000,China)

机构地区：[1]安徽农业大学信息与人工智能学院,安徽合肥230000

出　　处：《智慧农业（中英文）》2024年第5期40-50,共11页Smart Agriculture

基　　金：安徽省重点研究与开发计划(2022l07020017);国家自然科学基金项目(61805001);安徽省自然科学基金项目(1808085QF218);安徽农业大学研究生创新基金项目(2021yjs-51)。

摘　　要：[目的和意义]原始星载日光诱导叶绿素荧光(Sunlight-induced Chlorophyll Fluorescence,SIF)数据存在足迹离散、时空分辨率低等缺陷,针对这些问题许多研究进行了SIF重构,但大多数重构后的新型SIF数据分辨率仍较低,难以应用到精细尺度农业领域,且部分高精度SIF重构数据并非基于原始卫星SIF数据重构。OCO-2 SIF原始数据空间分辨率高(1.29 km×2.25 km),植被异质性低,对区域尺度高分辨率作物SIF重构具备突出价值。[方法]选取美国区域尺度大豆为研究对象,利用原始OCO-2 SIF和MODIS产品进行高分辨率大豆SIF重构,通过组合多个卫星轨迹经过的大豆种植区,提高SIF样本总量,与增强植被指数(Enhanced Vegetation Index,EVI)、光合有效辐射分量(Fraction of Photosynthetically Active Radiation,FPAR)和土地表面温度(Land Surface Temperature,LST)等预测因子足迹匹配后构建多源遥感数据集,代入BP神经网络训练模型,进而生成区域尺度空间连续且具有较高时空分辨率(8 d、500 m)的重构SIF数据集(BPSIF)。[结果和讨论]加入EVI,FPAR和LST的SIF重构模型R^(2)达0.84,利用总初级生产力(Gross Primary Productivity,GPP)数据对BPSIF进行质量评价,OCO-2 SIF与GPP的Pearson相关系数为0.53,而BPSIF与GPP相关系数提升到0.8,表明本研究生成的BPSIF数据集更加可靠。[结论]研究成果有望为区域尺度大豆作物SIF研究提供理论依据和数据支撑。[Objective]Sunlight-induced chlorophyll fluorescence(SIF)data obtained from satellites suffer from issues such as low spatial and temporal resolution,and discrete footprint because of the limitations imposed by satellite orbits.To address these problems,obtaining higher resolution SIF data,most reconstruction studies are based on low-resolution satellite SIF.Moreover,the spatial resolution of most SIF reconstruction products is still not enough to be directly used for the study of crop photosynthetic rate at the regional scale.Although some SIF products boast elevated resolutions,but these derive not from the original satellite SIF data reconstruct but instead evolve from secondary reconstructions based on preexisting SIF reconstruction products.Satellite OCO-2(The Orbiting Carbon Obsevatory-2)equipped with a high-resolution spectrometer,OCO-2 SIF has higher spatial resolution(1.29×2.25 km)compared to other original SIF products,making it suitable in advancing the realm of high-resolution SIF data reconstruction,particularly within the context of regional-scale crop studies.[Methods]This research primarily exploration SIF reconstruct at the regional scale,mainly focused on the partial soybean planting regions nestled within the United States.The selection of MODIS raw data hinged on a meticulous consideration of environmental conditions,the distinctive physiological attributes of soybeans,and an exhaustive evaluation of factors intricately linked to OCO-2 SIF within these soybean planting regions.The primary tasks of this research encompassed reconstructing high resolution soybean SIF while concurrently executing a rigorous assessment of the reconstructed SIF's quality.During the dataset construction process,amalgamated SIF data from multiple soybean planting regions traversed by the OCO-2 satellite's footprint to retain as many of the available original SIF samples as possible.This approach provided the subsequent SIF reconstruction model with a rich source of SIF data.SIF data obtained beneath the satellite's

关 键 词：星载SIF数据 MODIS数据 BP神经网络 大豆SIF重构 

分 类 号：S-3[农业科学] S127