水稻单产遥感估测建模研究

本文系统扼要地总结了统计、气象、农学和光谱等估产模型,以及水稻遥感估产农学机理等主要研究结果,并在建立的多种估产模型中,重点介绍了具有应用前景的光谱估产与水稻生长模拟估产的复合模型。     

基于多时相MODIS-RVI的玉米遥感估产研究

为了获得更加宏观高效的农作物估产模型,以吉林省德惠市为研究区,以MODIS为数据源,进行了玉米估产模型研究。通过分析比值植被指数(RVI)与玉米产量之间的相关关系,建立玉米单产预测模型。研究表明,利用多时相的RVI对玉米点进行遥感回归估产可得到较好的估算效果,模型相关系数可达0.825,均方根误差为7.61,验证点的实际产量与理论产量间的相对误差均在10%以内,对吉林省德惠市玉米估产模型研究具有一定的指导意义。     

关键时相长势—环境和景观特征对河北省县级尺度冬小麦单产估算精度影响分析

区域尺度上精准、快速的作物单产估算可以有效地为国家粮食安全相关政策的制定提供数据支撑。本文针对县级估产时相和特征类型选择问题,基于遥感、气象和统计等多源数据,通过不同时相和特征要素之间的组合分析来探索其对于县级尺度冬小麦单产估算的影响。特征要素主要考虑作物长势、环境(水分和光温条件)和农田景观3个类型;时相主要考虑由冬小麦生长过程NDVI (Normalized Difference Vegetation Index)曲线特征提取的5个关键时段(P1—P5)。利用不同时相与类型特征的组合与统计单产构建随机森林回归模型,根据精度评价结果分析各组合的优劣。2014年—2017年的数据用来建模,2018年数据用来验证。对于单时相,P2、P3、P4的表现明显好于P1和P5;多时相的准确度明显优于单时相,其中P2、P4的组合效果最佳。对于不同类型的特征要素,作物长势特征参量对估产精度的影响最大,而水分影响和光温条件等环境因子的加入对估产准确性并没有明显提升,农田景观参数的加入能够有效提升估产的准确性。在最优组合的基础上,剔除冗余变量优选出5个重要的指标因子(PROP、NDVI＿P2、B2＿P2、...     

基于遥感空间特性的广东省水稻单产快速预估

分析了目前水稻遥感估产的技术现状,基于遥感数据的空间特性,提出了一种快速预测水稻单产的方法,估产试验表明该方法简单实用,具有推广意义.     

基于光能利用率模型的玉米遥感估产研究

农作物长势监测和产量预测对于国家制定相关粮食政策、农业发展等都具有重要的意义,如何获得高效、宏观、精确的估产方法一直是学者关注的重点问题。以吉林省德惠市的玉米作为研究对象,利用光能利用率模型对玉米进行产量估算的研究,并且使用空间数据插值方法中的反距离权重法获得了每月平均温度数据的格网数据。通过玉米的净初级生产力NPP的累计值以及玉米的收获指数来获得最终的玉米产量值,利用验证点实测产量值与估算值的相关性和相对误差进行精度验证,相关系数R^2为0.649 9,平均相对误差值为1.676%,证明基于光能利用率模型的玉米估产在研究区具有一定的可行性。     

小麦单产的遥感—气象综合模式研究

本文以北京顺义县为例,以气象因子与垂直植被指数(PVI)作为参数,用灰色模型G(0,2)和逐段订正模型即阶乘模型,建立冬小表遥感信息-气象因子综合模型。计算结果表明,改进后的综合模型其平均精度比单纯的遥感信息模型提高近7％,个别年份达到10％以上。     

美国农业遥感技术应用现状简介

根据1996年中美农业科技交流协议,笔者于1996年7月10日至1996年8月1日带队到美国,对美国大面积农作物遥感估产和农业遥感应用研究情况进行了为期3周的考察。     

基于Sentinel-2和MODIS数据的宣恩县水稻遥感估产研究

基于作物的波谱反射特征,利用公开的多源遥感数据和相关技术能够实现农作物种植面积提取和产量预估,为作物长势监测等农业需求提供科学决策依据。本文首先基于Sentinel-2卫星影像,结合基于人工目视解译的监督分类、基于规则的面向对象分类和基于专家知识的决策树分类3种影像分类方法综合确定县级研究区的水稻种植范围,再选取水稻生育物候期内的多时相多光谱MODIS13Q1影像产品,建立影像提取出的植被指数EVI与水稻年产量之间的多元统计回归模型并应用于年产量预估,估产结果精度均达94%以上,符合实际需求。该模型可用性较强,对县域农作物遥感估产应用具有一定的指导意义。     

利用MODIS遥感影像获取近地层气温的方法研究

由于冠层叶片群体效应,在1km的空间尺度上遥感获取浓密植被陆面温度与气温近似相等。根据这个原理对利用遥感手段获取气温进行了尝试,提出利用NDVI-Ts空间获取气温的方法,计算气温空间分布模式,同时对Prihodko和Goward提出的气温遥感获取模型(简称P-G模型)进行试验并与NDVI-Ts空间法进行了对比。根据Parton和Logan提出的气温尺度转换模型,利用气象站观测最高气温和最低气温获取Terra卫星过境时刻气温作为“测定值”,对遥感获取的气温进行检验,得到以下结论:P-G模型计算气温与观测结果相比偏高,而NDVI-Ts法计算结果偏低,但是其总体误差范围相当,大约为 4℃;与P-G模型相比,尽管NDVI-Ts空间法获得的气温在精度上对P-G模型没有多大的改善,但这种方法能够更加充分利用遥感获取的信息,而且在计算机运算效率上也有很大的改进,NDVI-Ts空间法相对于P-G模型具有一定优势。     

基于MODIS植被指数的塔河流域植被时空变化特征研究

基于获取的塔河流域2000~2014年历年4~10月间逐月MODIS植被指数产品,采用时间序列谐波分析法(HANTS)对最大值合成的逐月NDVI时间序列数据进行了重建,用趋势线分析法对塔河流域近15年生长季(4~10月)MODIS NDVI的时间变化进行计算,用一元线性回归趋势法计算得到了塔河流域近15年生长季(4~10月)NDVI变化趋势的空间分布。结合植被类型分布图对计算得到的实验结果进行了研究分析,总结了塔河流域多年植被覆盖的时空分布及其变化规律,成果可为塔河流域综合治理及生态环境评价提供依据。     

An analysis of cropland mask choice and ancillary data for annual corn yield forecasting using MODIS data

The Midwestern United States is one of the world’s most important corn-producing regions. Monitoring and forecasting of corn yields in this intensive agricultural region are important activities to support food security, commodity markets, bioenergy industries, and formation of national policies. This study aims to develop forecasting models that have the capability to provide mid-season prediction of county-level corn yields for the entire Midwestern United States. We used multi-temporal MODIS NDVI (normalized difference vegetation index) 16-day composite data as the primary input, with digital elevation model (DEM) and parameter-elevation relationships on independent slopes model (PRISM) climate data as additional inputs. The DEM and PRISM data, along with three types of cropland masks were tested and compared to evaluate their impacts on model predictive accuracy. Our results suggested that the use of general cropland masks (e.g., summer crop or cultivated crops) generated similar results compared with use of an annual corn-specific mask. Leave-one-year-out cross-validation resulted in an average R² of 0.75 and RMSE value of 1.10 t/ha. Using a DEM as an additional model input slightly improved performance, while inclusion of PRISM climate data appeared not to be important for our regional corn-yield model. Furthermore, our model has potential for real-time/early prediction. Our corn yield esitmates are available as early as late July, which is an improvement upon previous corn-yield prediction models. In addition to annual corn yield forecasting, we examined model uncertainties through spatial and temporal analysis of the model's predictive error distribution. The magnitude of predictive error (by county) appears to be associated with the spatial patterns of corn fields in the study area.     

Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China

The significance of crop yield estimation is well known in agricultural management and policy development at regional and national levels. The primary objective of this study was to test the suitability of the method, depending on predicted crop production, to estimate crop yield with a MODIS-NDVI-based model on a regional scale. In this paper, MODIS-NDVI data, with a 250 m resolution, was used to estimate the winter wheat (Triticum aestivum L.) yield in one of the main winter-wheat-growing regions. Our study region is located in Jining, Shandong Province. In order to improve the quality of remote sensing data and the accuracy of yield prediction, especially to eliminate the cloud-contaminated data and abnormal data in the MODIS-NDVI series, the Savitzky–Golay filter was applied to smooth the 10-day NDVI data. The spatial accumulation of NDVI at the county level was used to test its relationship with winter wheat production in the study area. A linear regressive relationship between the spatial accumulation of NDVI and the production of winter wheat was established using a stepwise regression method. The average yield was derived from predicted production divided by the growing acreage of winter wheat on a county level. Finally, the results were validated by the ground survey data, and the errors were compared with the errors of agro-climate models. The results showed that the relative errors of the predicted yield using MODIS-NDVI are between −4.62% and 5.40% and that whole RMSE was 214.16 kg ha⁻¹ lower than the RMSE (233.35 kg ha⁻¹) of agro-climate models in this study region. A good predicted yield data of winter wheat could be got about 40 days ahead of harvest time, i.e. at the booting-heading stage of winter wheat. The method suggested in this paper was good for predicting regional winter wheat production and yield estimation.     

Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands,USA

Data fused from distinct but complementary satellite sensors mitigate tradeoffs that researchers make when selecting between spatial and temporal resolutions of remotely sensed data. We integrated data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra satellite and the Operational Land Imager sensor aboard the Landsat 8 satellite into four regression-tree models and applied those data to a mapping application. This application produced downscaled maps that utilize the 30-m spatial resolution of Landsat in conjunction with daily acquisitions of MODIS normalized difference vegetation index (NDVI) that are composited and temporally smoothed. We produced four weekly, atmospherically corrected, and nearly cloud-free, downscaled 30-m synthetic MODIS NDVI predictions (maps) built from these models. Model results were strong with R² values ranging from 0.74 to 0.85. The correlation coefficients (r ≥ 0.89) were strong for all predictions when compared to corresponding original MODIS NDVI data. Downscaled products incorporated into independently developed sagebrush ecosystem models yielded mixed results. The visual quality of the downscaled 30-m synthetic MODIS NDVI predictions were remarkable when compared to the original 250-m MODIS NDVI. These 30-m maps improve knowledge of dynamic rangeland seasonal processes in the central Great Basin, United States, and provide land managers improved resource maps.     

ASAR数据与水稻作物模型同化制作水稻产量分布图

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.     

Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data

Forest cover plays a key role in climate change by influencing the carbon stocks, the hydrological cycle and the energy balance. Forest cover information can be determined from fine-resolution data, such as Landsat Enhanced Thematic Mapper Plus (ETM+). However, forest cover classification with fine-resolution data usually uses only one temporal data because successive data acquirement is difficult. It may achieve mis-classification result without involving vegetation growth information, because different vegetation types may have the similar spectral features in the fine-resolution data. To overcome these issues, a forest cover classification method using Landsat ETM+ data appending with time series Moderate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data was proposed. The objective was to investigate the potential of temporal features extracted from coarse-resolution time series vegetation index data on improving the forest cover classification accuracy using fine-resolution remote sensing data. This method firstly fused Landsat ETM+ NDVI and MODIS NDVI data to obtain time series fine-resolution NDVI data, and then the temporal features were extracted from the fused NDVI data. Finally, temporal features combined with Landsat ETM+ spectral data was used to improve forest cover classification accuracy using supervised classifier. The study in North China region confirmed that time series NDVI features had significant effects on improving forest cover classification accuracy of fine resolution remote sensing data. The NDVI features extracted from time series fused NDVI data could improve the overall classification accuracy approximately 5% from 88.99% to 93.88% compared to only using single Landsat ETM+ data.     

基于RS和GIS的农作物估产方法研究进展

实时获取农作物长势及产量等信息对于现代农业的发展具有重要意义。近年来,随着遥感技术(remote sensing,RS)和地理信息系统(geographic information system,GIS)广泛应用于农作物估产领域,相继出现了一些较为实用的估产方法,主要有结合辅助数据的估产方法、基于植被指数的估产方法、基于特定模型的估产方法和基于农作物估产平台(软件)的开发等。其中,基于植被指数的估产方法又分为单一和多植被指数估产2类方法。在对近年来该领域大量文献深入研究的基础上,着重就几类热点方法展开论述,并对每类方法的优势和缺陷进行了评述,最后对该领域需要进一步研究的方向进行了探讨和展望,以期为后续研究提供参考。     

Integration of maximum crop response with machine learning regression model to timely estimate crop yield

Timely and reliable estimation of regional crop yield is a vital component of food security assessment, especially in developing regions. The traditional crop forecasting methods need ample time and labor to collect and process field data to release official yield reports. Satellite remote sensing data is considered a cost-effective and accurate way of predicting crop yield at pixel-level. In this study, maximum Enhanced Vegetation Index (EVI) during the crop-growing season was integrated with Machine Learning Regression (MLR) models to estimate wheat and rice yields in Pakistan's Punjab province. Five MLR models were compared using a fivefold cross-validation method for their predictive accuracy. The study results revealed that the regression model based on the Gaussian process outperformed over other models. The best performing model attained coefficient of determination (R2), Root Mean Square Error (RMSE, t/ ha), and Mean Absolute Error (MAE, t/ha) of 0.75, 0.281, and 0.236 for wheat; 0.68, 0.112, and 0.091 for rice, respectively. The proposed method made it feasible to predict wheat and rice 6– 8 weeks before the harvest. The early prediction of crop yield and its spatial distribution in the region can help formulate efficient agricultural policies for sustainable social, environmental, and economic progress.     

Vegetation monitoring for Guatemala: a comparison between simulated VIIRS and MODIS satellite data

The advanced very high resolution radiometer (AVHRR) and moderate resolution imaging spectroradiometer (MODIS) data are being widely used for vegetation monitoring across the globe. However, sensors will discontinue collecting these data in the near future. National Aeronautics and Space Administration is planning to launch a new sensor, visible infrared imaging radiometer suite (VIIRS), to continue to provide satellite data for vegetation monitoring. This article presents a case study of Guatemala and compares the simulated VIIRS-Normalized Difference Vegetation Index (NDVI) with MODIS-NDVI for four different dates each in 2003 and 2005. The dissimilarity between VIIRS-NDVI and MODIS-NDVI was examined on the basis of the percent difference, the two-tailed student's t-test, and the coefficient of determination, R ². The per cent difference was found to be within 3%, the p-value ranged between 0.52 and 0.99, and R ² exceeded 0.88 for all major types of vegetation (basic grains, rubber, sugarcane, coffee and forests) found in Guatemala. It was therefore concluded that VIIRS will be almost equally capable of vegetation monitoring as MODIS.     

Geoinformatics based approach for estimating the sediment yield of the mountainous watersheds in Kashmir Himalaya,India

Sedimentation of water bodies is governed by the erosional processes occurring at the watershed level. In this research, a method is proposed for assessing the sediment yield of the mountainous watersheds surrounding the Wular lake in Kashmir Himalaya, using geoinformatics and geostatistics. This method is empirical and semi-quantitative in approach and takes into account the weightage-based influence of the parameters governing the watershed sediment yield. The results of this study reveal that out of the six surrounding watersheds of the Wular Lake, Madhumati watershed with the highest sediment yield index, SYI (39.78) drains maximum sediments into the Lake followed by Arin (39.27), Ferozpur (34.30), Wular II (32.53), Wular I (24.65) and Gundar (23.43) in the event of a same intensity storm. The proposed method is reasonably a better approach in the data-scarce Himalayan region and shall be a useful tool for watershed management in other regions with similar geographic setting.