CAJ | 학술논문

提出了利用雷达数据进行水稻估产的技术方法,并以ASAR数据为例,探讨了雷达数据在水稻估产中的可行性.首先利用ASAR数据进行水稻制图,从各时相ASAR数据中提取水稻后向散射系数.随后,基于像元尺度,采用同化方法,以LAI为结合点,将水稻作物模型ORYZA2000与半经验水稻后向散射模型结合,建立嵌套模型模拟水稻后向散射系数.选择水稻出苗期和播种密度为参数优化对象,利用全局优化算法SCE-UA对0RYZA2000模型重新初始化,使模拟的水稻后向散射系数值与实测值误差最小,并由优化后的ORYZA2000模型计算每个像元的水稻产量,生成水稻产量分布图.结果表明,水稻产量分布图能够描绘研究区水稻实际产量的分布趋势,但由于采用潜在生长条件模拟,模拟的水稻平均产量比实测平均值高约13%,验证点的水稻产量模拟值与实测值相对误差为11.2%.由于半经验水稻后向散射模型存在对LAI变化不够敏感和对水层的简化处理,增加了水稻估产的误差.但从总体上看,利用该方法进行区域水稻估产是可行的,并为多云多雨地区的水稻遥感监测提供了重要参考.
제출료이용뢰체수거진행수도고산적기술방법,병이ASAR수거위례,탐토료뢰체수거재수도고산중적가행성.수선이용ASAR수거진행수도제도,종각시상ASAR수거중제취수도후향산사계수.수후,기우상원척도,채용동화방법,이LAI위결합점,장수도작물모형ORYZA2000여반경험수도후향산사모형결합,건립감투모형모의수도후향산사계수.선택수도출묘기화파충밀도위삼수우화대상,이용전국우화산법SCE-UA대0RYZA2000모형중신초시화,사모의적수도후향산사계수치여실측치오차최소,병유우화후적ORYZA2000모형계산매개상원적수도산량,생성수도산량분포도.결과표명,수도산량분포도능구묘회연구구수도실제산량적분포추세,단유우채용잠재생장조건모의,모의적수도평균산량비실측평균치고약13%,험증점적수도산량모의치여실측치상대오차위11.2%.유우반경험수도후향산사모형존재대LAI변화불구민감화대수층적간화처리,증가료수도고산적오차.단종총체상간,이용해방법진행구역수도고산시가행적,병위다운다우지구적수도요감감측제공료중요삼고.
In this paper, a practical scheme for assimilation of multi-temporal and multi-polarization ENVISAT ASAR data in rice crop model to map rice yield is presented. To achieve this, rice distribution information is obtained first by rice mapping method to retrieve rice fields from ASAR images, and then an assimilation method is applied to use the temporal single-polarized rice backscatteriug coefficients which are grouped for each rice pixel 'to re-initialize ORYZA2000. The assimilation method consists of re-initializing the model with optimal input parameters allows a better temporal agreement between the rice backscattering coefficients retrieved from ASAR data and the ones simulated by a coupled model, i. e. , the combination of ORYZA2000 and a semi-empirical rice backscatter model through LAI. The SCE-UA optimization algorithm is employed to determine the optimal set of input parameters. After the re-initialization, rice yield for each rice pixel is calculated, and the yield map over the area of interest is finally produced. The scheme is applied over Xinghua study area located in the middle of Jiangsu Province of China during the 2006 rice season. The result shows that the obtained rice yield map generally overestimates the actual rice production by 13% , with a relative error of 11.2% at validation sites, but the tendency of rice growth status and spatial variation of the rice yield are well predicted and highly consistent with the actual production variation.