依安县甜菜小垄密植高产高糖群体结构研究与推广

项目研究结果表明：甜菜产量和含糖率与密度呈曲线相关,相关指数分别γi=0．93和1．=0．91。甜菜产量和含糖率均以7．0万株／hm^2密度最高,相应为54．2t／hm^2和19．83％。甜菜高产高糖群体的主要生理参数为：7月中旬叶面积指数应达3．5以上,最大值以5．2左右为宜,总光合势以3550km^2/hm^2·d为宜,生育期平均净光合生产率应达到8．1g／m^2·d左右,经济产量光能利用率应达到0．75％以上。     

水文模型植被参数获取方法及应用研究

引入Spot/Vegetation(简称SPOT-VGT)归一化植被指数NDVI遥感资料,建立了叶面积指数LAI-NDVI模型,进行大尺度陆面水文模型VIC植被参数获取方法研究,将结果应用于黑河莺落峡流域水文模拟,并分析了植被参数对陆面水文循环要素的影响.结果表明:采用SPOT-VGT遥感资料建立的LAI-NDVI模型计算的叶面积指数较好地反映了实际叶面积指数的空间分布和时间变化,用此方法获取的植被参数结果优于模型推荐的查表法.     

Abis接口用户位置信息智能学习方法

在对Abis接口的信令进行解析分析时,绝大部分信令消息本身解码结果不携带位置区、小区等信息,而这些信息是后续应用分析所必须的维度。因此需要对不含位置小区信息的解码结果补全位置区、小区信息。这也是Abis接口信令监测系统所要解决的核心问题之一。通过Abis接口特殊的链路标识和系统消息的关联,可以得到信令消息当前小区的位置区、小区。通过关联呼叫接续过程中在小区间的切换过程,基于概率论,经过一段时间的自学习,可以学习邻区的位置区和小区号以及邻区的BCCH频点。     

470MHz无线信号在混交林中的传播特性及模型构建

LoRa无线通信技术因其传输距离较远、功耗较低,满足了混交林中大规模节点传输汇聚的要求,可作为林业物联网数据采集的主要通信方式来使用.利用接收信号强度指示(Received Signal Strength Indication,RSSI)分析了470 MHz无线信号传输中的电波路径损耗问题,通过对测得的叶面积指数(Le...     

Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield

This study presents a method to assimilate leaf area index retrieved from ENVISAT ASAR and MERIS data into CERES-Wheat crop growth model with the objective to improve the accuracy of the wheat yield predictions at catchment scale. The assimilation method consists in re-initialising the model with optimal input parameters allowing a better temporal agreement between the LAI simulated by the model and the LAI estimated by remote sensing data. A variational assimilation algorithm has been applied to minimise the difference between simulated and remotely-sensed LAI and to determine the optimal set of input parameters. After the re-initialisation, the wheat yield maps have been obtained and their accuracy evaluated.The method has been applied over Matera site located in Southern Italy and validated by using the dataset of an experimental campaign carried out during the 2004 wheat growing season.Results indicate that, LAI maps retrieved from MERIS and ASAR data can be effectively assimilated into CERES-Wheat model thus leading to accuracies of the yield maps ranging from 360 kg/ha to 420 kg/ha.     

新疆棉花LAI和叶绿素密度的高光谱估算研究

利用非成像高光谱仪,对棉花(2品种4水平种植密度)冠层5个关键生育时期进行光谱测定,分析棉花反射光谱及微分光谱生育期的变化规律,并对棉花冠层叶面积指数(LAI)、叶绿素密度(CH.D)与光谱数据进行回归分析,结果表明,用归一化差值植被指数(NDVI)与LAI建立的对数模型能够较好地估测棉花冠层的LAI(r=0.9123**,n=20);近红外729 nm波段处一阶微分光谱数值与CH.D高度相关(r=0.9372**,n=20),用此波段建立的CH.D估算模型,精度达84.3%,标准差为0.234g.m-2,RMSE=0.1569。研究表明,可以用高光谱数据对新疆棉花冠层LAI和CH.D进行遥感估算。     

Comparison and evaluation of Medium Resolution Imaging Spectrometer leaf area index products across a range of land use

Leaf area index (LAI) is a commonly required parameter when modelling land surface fluxes. Satellite based imagers, such as the 300 m full resolution (FR) Medium Spectral Resolution Imaging Spectrometer (MERIS), offer the potential for timely LAI mapping. The availability of multiple MERIS LAI algorithms prompts the need for an evaluation of their performance, especially over a range of land use conditions. Four current methods for deriving LAI from MERIS FR data were compared to estimates from in-situ measurements over a 3 km × 3 km region near Ottawa, Canada. The LAI of deciduous dominant forest stands and corn, soybean and pasture fields was measured in-situ using digital hemispherical photography and processed using the CANEYE software. MERIS LAI estimates were derived using the MERIS Top of Atmosphere (TOA) algorithm, MERIS Top of Canopy (TOC) algorithm, the Canada Centre for Remote Sensing (CCRS) Empirical algorithm and the University of Toronto (UofT) GLOBCARBON algorithm. Results show that TOA and TOC LAI estimates were nearly identical (R² > 0.98) with underestimation of LAI when it is larger than 4 and overestimation when smaller than 2 over the study region. The UofT and CCRS LAI estimates had root mean square errors over 1.4 units with large (∼ 25%) relative residuals over forests and consistent underestimates over corn fields. Both algorithms were correlated (R² > 0.8) possibly due to their use of the same spectral bands derived vegetation index for retrieving LAI. LAI time series from TOA, TOC and CCRS algorithms showed smooth growth trajectories however similar errors were found when the values were compared with the in-situ LAI. In summary, none of the MERIS LAI algorithms currently meet performance requirements from the Global Climate Observing System.