基于地面高光谱遥感的大豆产量估算模型研究

为在田间管理中对作物产量进行估测,通过两年大田试验收集了大豆生殖生长期的高光谱数据及产量数据,基于各生育期一阶微分光谱反射率计算了7个光谱指数:比值指数(Ratio index, RI)、差值指数(Difference index, DI)、归一化光谱指数(Normalized difference vegetation index, NDVI)、土壤调整光谱指数(Soil-adjusted iegetation index, SAVI)、三角光谱指数(Triangular vegetation index, TVI)、改进红边归一光谱指数(Modified normalized difference index, mNDI)和改进红边比值光谱指数(Modified simple ratio, mSR),使用相关矩阵法将光谱指数与大豆产量数据进行相关性分析并提取最佳波长组合,随后将计算结果作为与大豆产量相关的最佳光谱指数,最后将各生育期筛选出的与大豆产量相关系数最高的5个光谱指数作为模型输入变量,利用支持向量机(Support vector machine, SVM)、随机森林(Random forest, RF)和反向神经网络(Back propagation neural network, BPNN)构建大豆产量估算模型并进行验证。结果表明,各生育期(全花期(R2)、全荚期(R4)和鼓粒期(R6))计算的光谱指数与产量的相关系数均高于0.6,相关性较好,其中全荚期的光谱指数FDmSR与大豆产量的相关系数最高,达到0.717;大豆产量最优估算模型的方法是输入变量为全荚期构建的一阶微分光谱指数和RF组合的建模方法,模型验证集R2为0.85,RMSE和MRE分别为272.80 kg/hm^(2)和5.12%。本研究成果可为基于高光谱遥感技术的作物产量估测提供理论依据和应用参考。

卫星热红外温度反演钢铁企业炼钢月产量估算模型

钢铁业是国民经济发展中重要组成部分,掌握钢铁企业月产量有利于开展宏观调控及合理分配资源。以钢铁企业的月产量为研究对象,运用景观格局指数的理论和方法,利用卫星热红外遥感数据表面温度反演后的分级结果,结合厂房矢量数据来获取表面温度异常值和热力景观分布参数,以此提出并建立钢铁企业炼钢月产量估算模型。再结合华中和华北两个典型钢铁企业实际月产量数据,根据最小二乘拟合分别求估算模型,模型的决定系数(R^(2))大于0.9。分析后验差检验结果可知,该估算模型精度等级为二级;且在95%的置信度下,实际产量值均落在估算值的置信区间内,综合反映本文提出的炼钢月产量估算模型精度较高。

基于多时相多参数融合的麦玉轮作小麦产量估算方法

为了进一步提高冬小麦产量预测的准确性,针对麦玉轮作体系缺乏直接把前茬作物信息纳入到当季作物的产量估算及管理中的研究状况,利用前茬玉米季中长势遥感信息及产量信息,融合小麦拔节期、灌浆期及成熟期长势遥感信息、播前施肥信息及土壤特性信息等多时相多模态数据,基于GPR算法,建立多时相多模态参数融合的麦玉轮作体系小麦产量估算模型,结果显示:基于多生育期的产量估算模型较单生育期最优产量估算模型性能有所提升,R2提高0.01~0.03。其中基于拔节期产量估算模型精度略低于多生育期产量估算模型,但精度相近。基于多模态参数融合的产量估算模型中,除玉米作物信息与土壤特性信息融合构建的产量估算模型,多模态参数融合的产量估算模型精度较相应低模态参数融合的产量估算模型精度高。四模态参数融合的GPR模型决定系数R^(2)为0.92,RMSE为213.75 kg/hm^(2),较其他模型,R^(2)提高0.02~0.41。对于小麦产量估算模型,各模态参数影响由大到小依次为施肥信息、小麦遥感信息、土壤特性信息、玉米作物信息。玉米作物信息对于多模态参数融合的小麦产量估算模型精度提升最小,R^(2)总体提升0.02~0.07。玉米作物信息在一定程度表征了收获后土壤肥力状况,是土壤特性信息的高空间分辨率补充,可以进一步提高量化土壤肥力的能力,与其他参数信息结合,提高了小麦产量估算精度,为麦玉轮作体系土壤-作物数据的综合利用及轮作体系的综合管理提供了科学依据和方法思路。

基于作物生长模型和遥感数据同化的区域玉米产量估算

为了将遥感观测到的玉米生长期间作物冠层方向反射波谱的时间序列变化信息用于区域玉米产量估算,该文将时间序列中分辨率成像光谱仪(moderate resolution imaging spectroradiometer,MODIS)数据和高空间分辨率LandsatTM遥感观测数据相结合,以叶面积指数(LAI)作为耦合作物生长模型(crop environment resource synthesis-Maize,CERES-Maize)和植被冠层反射率模型(scattering by arbitrarily inclined leaves,SAIL)的关键参数,提出了将耦合模型与时间序列遥感观测数据同化进行区域玉米产量估算的方案。该文选择吉林省榆树市为研究区,采用MODIS和LandsatTM2种尺度数据集,利用SCE-UA(shuffled complex evolution method developed at the University of Arizona)算法分别进行玉米产量同化估产研究,得到玉米单产空间分布的估计结果,结合遥感估算的种植面积求算榆树市玉米总产量。结果表明,与玉米统计总产量相比,2007、2008和2009年遥感数据同化估算的总产量误差分别为9.15%、14.99%和8.97%;与仅利用CERES-Maize模型模拟得到的产量误差相比,3a间遥感估算总产量的误差分别减小了7.49%、1.21%和5.23%,且采用MODIS和TM遥感数据估算的玉米产量表现了其空间差异性。利用榆树市3a间玉米产量的明显差异,分析了时序遥感数据对作物长势和产量变化信息的表达能力,同年份内时序归一化差值植被指数越大,对应的玉米产量越高;年际间遥感观测反射率的差异通过数据同化方法能够反映年际间玉米产量差的变化。该文提出的玉米估产方案为将来进一步结合多源遥感数据、植被冠层反射率模型与作物生长模型进行区域玉米估产研究提供了参考。

青海省牧草产量的遥感估算及其时空分布规律

以青海省为例，基于植被指数（NDVI）和地面实测资料建立了不同草地类型的牧草产量遥感估算模型。并利用地面实测资料对模型精度进行了检验。结果表明，所有模型拟合结果良好（R^2≥0．67），精度较高，能够对牧草产量进行动态监测；基于建立的牧草产量遥感估算模型，反演了青海省2004年5～8月基于像元尺度的月牧草产量分布图，并对牧草产量的空间分布特征、时间演变规律进行了详细分析。研究表明，青海省牧草产量空间分布主要与草地类型密切相关，同时也与其地貌、土壤和气候特征有关。而牧草产量的年内季节变化则主要与牧草生长及气候变化规律有关。

基于农田管理分区的制种玉米产量估算与限制因子评价

为了提升规模化农田不同管理分区的玉米产量,实现精准管理,该研究使用相关成分回归法(Correlated Component Regression,CCR),考虑地形因素(高程)、土壤理化性质(砂粒、粉粒、黏粒、容重、土壤含水率、土壤有机碳、全氮、全磷、速效氮、电导率)11个因子,评估规模化农田和聚类分析得到的3个管理区(M1、M2和M3)内产量的限制因子,并在不同分区内建立产量估算模型。模型验证结果表明:未分区的情况下,产量限制因子为土壤粉粒、砂粒、土壤有机碳、土壤含水率、速效氮和全氮,经验证,产量估算模型的决定系数(R^2)为0.70,标准均方根误差(Normalized Root Mean Square Error,nRMSE)为0.21。分区后,M1的产量限制因子为土壤粉粒、砂粒、黏粒、速效氮、电导率、全氮和全磷,M2的产量限制因子为土壤粉粒、砂粒和土壤含水率,M3的产量限制因子为高程、土壤砂粒、黏粒和电导率,产量估算模型的精度高(经验证,0.71<R2<0.83,0.16<nRMSE<0.18)。对农田进行分区管理,并根据各管理区内作物产量的限制因素制定分布式管理策略,可以更具针对性地提升作物产量。

Yield Estimation Model of Citrus Based on Spectral Data and Agronomic Parameters

With the development of precision agriculture, the research that applies Remote Sensing technology, especially 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 collect citrus canopy spectral, this article studied and analyzed the citrus of veget＆tion index and its relationship on yield, synthetically considered the influence of the agriculture pa- rameters on crop yield, and finally constructed the citrus yield estimation model based on the spectral data and agronomic parameters. Through the Significance Test and Samples＇ Test, olutained that the model＇s fitting degree was R=0.631, F= 13.201, P〈0.01 and the error rate of estimating accuracy was controlled 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.

Rice Yield Estimation by Integrating Remote Sensing with Rice Growth Simulation Model

Since remote sensing can provide information on the actual status of an agricultural crop, the integration between remote sensing data and crop growth simulation models has become an important trend for yield estimation and prediction.The main objective of this research was to combine a rice growth simulation model with remote sensing data to estimate rice grain yield for different growing seasons leading to an assessment of rice yield at regional levels. Integration between NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High Resolution Radiometer) data and the rice growth simulation model ORYZA1 to develop a new software, which was named as Rice-SRS Model, resulted in accurate estimates for rice yield in Shaoxing, China, with an estimation error reduced to 1.03% and 0.79% over-estimation and 0.79% under-estimation for early, single and late season rice, respectively. Selecting suitable dates for remote sensing images was an important factor which could influence estimation accuracy. Thus, given the different growing periods for each rice season, four images were needed for early and late rice, while five images were preferable for single season rice.Estimating rice yield using two or three images was possible, however, if images were obtained during the panicle initiation and heading stages.

GIS Based Soil Erosion Estimation Using EPM Method, Garmiyan Area, Kurdistan Region, Iraq

Using empirical model is one of the approaches of evaluating sediment yield. This research is aimed at predicting erosion and sedimentation in Garmiyan area at Kurdistan Region, Iraq used EPM （erosion potential model） incorporating into GIS （geographic information system） software. This basin area is about 1,620 km2. It has a range of vegetation, slope, geological, soil texture and land use types. The spatial distribution of gully erosion shows three main zones in the studied area （slight to moderate gully, high gully and sever fluvial erosion）. They form about 10%, 89% and 1% of gully erosion in the studied area respectively. The results of the EPM model show that the values of the coefficient of erosion Z are classified as moderate to high erosion intensity. They increase northward due to increasing of slope, elevation and rate of precipitation that generate Hortonian overland flow, which is due to high discharge and huge fluvial erosion power that cause ground surface erosion to produce large quantity of sediment. The results of GSP （spatial sediment rate） are increasing northward similar to Z due the same reasons, while the value of total sediment rate, shows different values for each watershed because they are mainly affected by the total watershed area.

Estimation of Grassland Production in Central and Eastern Mongolia from 2006 to 2015 via Remote Sensing

Mongolia is an important part of the Belt and Road Initiative"China-Mongolia-Russia Economic Corridor"and a region that has been severely affected by global climate change.Changes in grassland production have had a profound impact on the sustainable development of the region.Our study explored an optimal model for estimating grassland production in Mongolia and discovered its temporal and spatial distributions.Three estimation models were established using a statistical analysis method based on EVI,MSAVI,NDVI,and PsnNet from Moderate Resolution Imaging Spectroradiometer(MODIS)remote sensing data and measured data.A model evaluation and accuracy comparison showed that an exponential model based on MSAVI was the best simulation(model accuracy 78%).This was selected to estimate the grassland production in central and eastern Mongolia from 2006 to 2015.The results show that the grassland production in the study area had a significantly fluctuating trend for the decade study;a slight overall increasing trend was observed.For the first five years,the grassland production decreased slowly,whereas in the latter five years,significant fluctuations were observed.The grassland production(per unit yield)gradually increased from the southwest to northeast.In most provinces of the study area,the production was above 1000 kg ha with the largest production in Hentiy,at 3944.35 kg ha.The grassland production(total yield)varied greatly among the provinces,with Kent showing the highest production,2341.76x1〇4 t.Results also indicate that the trend in grassland production along the China-Mongolia railway was generally consistent with that of the six provinces studied.