CAJ | 학술논문

随着精准农业的发展，应用遥感技术,特别是高光谱遥感,实现农作物管理、监测和估产的研究也随之展开。当前,水稻、小麦等1年生作物的生长监测与估产方法研究发展迅速，一些研究成果已经开始为生产服务。但是对于多年生经济作物产量预测的研究很少。以多年生柑橘树为研究对象，利用 ASD手持光谱仪采集柑橘林冠层光谱，研究分析了柑橘光谱植被指数及其与产量的关系，并综合考虑农学因子对产量的影响，构建了基于高光谱数据和农学参数的柑橘估产模型。通过显著性检验和样本检验，得出该模型的拟合程度R=0.631，F=13.201（P<0.01）,误差率控制在3%~16%,说明该模型具有统计学意义和可靠性，同时也证明了高光谱遥感技术在柑橘产量估算研究中的巨大潜力。该研究是高光谱遥感技术在柑橘估产领域中的一次应用与探索。
수착정준농업적발전，응용요감기술,특별시고광보요감,실현농작물관리、감측화고산적연구야수지전개。당전,수도、소맥등1년생작물적생장감측여고산방법연구발전신속，일사연구성과이경개시위생산복무。단시대우다년생경제작물산량예측적연구흔소。이다년생감귤수위연구대상，이용 ASD수지광보의채집감귤림관층광보，연구분석료감귤광보식피지수급기여산량적관계，병종합고필농학인자대산량적영향，구건료기우고광보수거화농학삼수적감귤고산모형。통과현저성검험화양본검험，득출해모형적의합정도R=0.631，F=13.201（P<0.01）,오차솔공제재3%~16%,설명해모형구유통계학의의화가고성，동시야증명료고광보요감기술재감귤산량고산연구중적거대잠력。해연구시고광보요감기술재감귤고산영역중적일차응용여탐색。
With the development of precision agriculture, the research that applies Remote Sensing technology, especial y hyperspectral remote sensing, to realize crop management, monitoring and yield estimation, has been concerned. Nowadays, the growth-monitoring and yield-estimating methods in rice, wheat and other annual crops develop rapidly with some achievements having already been put into service. But the yield estimation research on perennial economic crops is few. Taking peren-nial citrus trees as the research object, using ASD spectrometer to col ect citrus canopy spectral, this article studied and analyzed the citrus of vegetation index and its relationship on yield, synthetical y considered the influence of the agriculture pa-rameters on crop yield, and final y constructed the citrus yield estimation model based on the spectral data and agronomic parameters. Through the Significance Test and Samples’ Test, obtained that the model’s fitting degree was R=0.631, F=13.201, P<0.01 and the error rate of estimating accuracy was control ed in the range 3%-16%, proving that the model has statistical signification and reliability. It concluded that hyperspectral acquired from citrus canopy has substantial potential for citrus yield estimation. This study is an application and exploration of Hyperspectral Remote Sensing technology in the citrus yield estimation.