基于大气温度和水汽含量改进HJ-1B地表温度反演

为了提高地面气象站稀少地区地表温度遥感反演的精度,本文基于多源遥感数据的优势,首先利用MODIS影像获取研究区像元尺度上平均大气水汽含量;然后利用同时相的HJ-1B影像估算区域地表比辐射率,再采用温度-植被指数法获取近地表大气温度;最后将以上3个参数输入单窗体算法,改进其地表温度反演的精度。研究结果表明,改进单窗体算法反演地表温度与地面实测温度的偏差小于1 K,为地面气象站点稀少的植被覆盖区域提供了一种可行的精确遥感反演地表温度方法。     

水稻冠层氮素含量光谱反演的随机森林算法及区域应用

利用地面实测数据构建高精度的水稻冠层氮素含量光谱反演点模型并将其进行尺度转换,实现了水稻冠层氮素含量准实时、大区域监测。以氮素光谱敏感指数作为输入变量,冠层氮素含量数据为输出变量,利用随机森林算法构建水稻冠层氮素含量高光谱反演模型,并用苏州市水稻农田验证区数据,检验模型的普适性和有效性;利用准同步的Hyperion数据,采用对输入、输出变量进行线性变换的简单尺度转换方法实现了点模型的区域应用。结果表明:基于随机森林算法的水稻冠层氮素含量高光谱反演模型可解释、所需样本少、不会过拟合、精度高(模型在实验区的预测精度为R2=0.82,验证区检验精度为R2=0.73)且具有普适性;点模型基于高光谱遥感卫星影像和尺度转换进行区域应用,精度较高(R2=0.81)。     

基于Triple-Collocation的地面观测与卫星遥感数据融合的雪深反演

针对单一被动微波遥感反演雪深的精度和空间分辨率不足的问题,提出了一种星-地多源数据融合的雪深反演方法。以北疆每日站点观测雪深、AMSR-E遥感反演雪深和SSM/I遥感反演雪深数据为研究对象,首先利用地统计方法结合地面站点观测数据估计北疆区域的雪深,然后采用Triple-Collocation方法分别估计三个雪深数据的误差方差,最后结合最小二乘原理实现星-地雪深观测数据的融合。对融合雪深与地面雪深观测数据进行验证分析,结果显示,与AMSR-E和SSM/I遥感反演雪深相比,融合的雪深与地面观测雪深的相关性更高;融合的雪深的精度有一定程度的提高。实验结果证明了多源数据融合方法的有效性。     

多光谱多角度遥感数据综合反演叶面积指数方法研究

叶面积指数是陆地生态系统的一个十分重要的结构参数。用遥感数据求取叶面积指数可以利用光谱的信息,比如通过植被指数来拟合一个经验关系,但很多植被指数明显受土壤背景的影响,对于有明显行结构的农作物,土壤的影响很难消除,植被指数的方法误差较大。多角度遥感包含了大量的地面目标的立体结构信息,具备求解植被特征参数的潜力,但通常多角度遥感反演对光谱信息的利用不足。与以往的反演方法相区别,该文利用行播作物二向反射模型,将多角度与多光谱数据结合进行行播作物LAI反演实验,并对反演算法进行了详细的敏感性分析实验,结果表明采用多角度、多光谱遥感数据相结合的方法可以有效反演行播作物的叶面积指数。     

臭氧卫星遥感六十年进展

臭氧已成为中国继PM_2.5之后多地的首要污染物,臭氧污染防治是中国“十四五”及未来大气污染防治的重点。本文回顾了近60年来国内外臭氧卫星观测方面的主要进展,包括卫星探测载荷和臭氧相关的反演应用技术等,分为3个阶段总结了卫星载荷天底、临边和掩星3种探测方式的发展历程。臭氧卫星遥感反演算法和监测应用也随着载荷的发展在不断更新,本文重点介绍了臭氧柱总量和垂直廓线卫星遥感反演算法、近地面臭氧及其前体物观测、平流层臭氧入侵观测和区域传输、臭氧卫星观测数据的精度验证等方面的重要进展。对比国际臭氧卫星遥感监测,中国臭氧监测卫星发展滞后,虽然国家民用空间基础设施规划中陆续发射的高光谱观测卫星、大气环境监测卫星具有初步的臭氧监测能力,但在卫星载荷在功能、性能等方面还有不小差距,比如空间分辨率、信噪比等方面。在算法反演和监测应用方面,目前臭氧柱总量反演精度较高,还存在对流层中低层和近地面臭氧浓度反演精度不够,臭氧污染评估及成因分析不足,如近地面臭氧污染迁移转化过程、平流层臭氧侵入识别分析等问题,是下一步要重点关注的方向。     

利用多源遥感数据反演城市地表温度

目前利用单通道热红外遥感数据反演地表温度的方法有大气校正法、单窗算法和普适性单通道算法,使用这3种算法反演地表温度时的一个关键问题就是需要获取大气参数。目前大气参数的获取主要根据近地表(地表2m左右的高度)的大气水分含量或湿度和平均气温的观测值来估计,这种方法只能获得个别点上的数据,而无法获取面上像元尺度的大气参数。本文利用多源遥感数据的优势,首先利用MODIS近红外数据,在像元尺度上获取温度反演中所需大气参数——大气水分含量,再利用同时相的Landsat ETM 影像,采用Jim啨nez-Mu oz和Sobrino的普适性单通道算法反演地表温度。研究结果表明,多源遥感数据的综合应用是城市地表温度反演的有效途径与方法,可获得较合理的地表温度反演结果。     

基于WOFOST模型与UAV数据的玉米生长后期地上生物量估算

地上生物量能够有效反映作物的生长状态，其信息的实时估算对产量预测和农田生产管理都有重要意义。作物生长模型因其详尽的生理生化基础和对生长过程数字化描述能力，成为生物量估算的理想模型。近年来，研究人员利用数据同化算法将时间序列遥感数据同化到作物生长模型中，实现了作物模型由基于气象站的点模拟到区域尺度面模拟的外推，使生物量模拟结果同时具备大范围和机理性两个方面的特点。这一模式下，时间序列的遥感数据质量将对生物量模拟精度产生直接影响，作物生长后期受到光谱饱和的影响，遥感数据的作物冠层信息获取能力会出现明显下降，因此有必要对该阶段遥感数据和作物模型的结合方式进行优化，提升生物量模拟精度。本文针对东北地区春玉米生物量遥感估算存在的问题，提出了利用WOFOST作物模型结合无人机（UAV）遥感数据实现作物生长后期生物量准确估算的新思路。新思路首先利用多光谱遥感数据获取WOFOST模型具备较高空间异质性的土壤速效养分参数以提升模型的空间信息模拟能力，使其能在一定程度上摆脱点尺度模拟的限制。同时，结合集合卡尔曼滤波算法将生长前期无人机（UAV）遥感数据同化到模型中，以缩短模型单独运行时间，减少模型运行过程中的参数误差累积，实现无遥感数据参与下的短期作物生长模拟，并输出生长后期相应的生物量模拟结果。最后，本文利用地面实测数据对新方法的生物量模拟精度进行了评价。结果表明，与全生育期数据同化相比，新方法的生物量估算精度有了明显的提升（全生育期同化：R² = 0.45，RMSE = 4254.30 kg/ha；新方法：R²= 0.86，RMSE = 2216.79 kg/ha）。     

基于物候信息的山东省冬小麦长势遥感监测

作物长势监测是农情监测的核心内容之一,处在不同生育期的作物长势存在显著差异。为了提高大范围作物长势遥感监测的精度,利用2001―2015年间获取的MOD09A1数据,以山东省冬小麦为研究对象,在逐年提取冬小麦抽穗期基础上,探讨研究区近15 a间冬小麦抽穗期长势时空格局。研究表明,与归一化差值植被指数(normalized difference vegetation index,NDVI)相比,基于增强型植被指数(enhanced vegetation index,EVI)提取的冬小麦抽穗期与地面观测数据有更好的一致性。研究区冬小麦抽穗期主要集中在4月中、下旬,并从南向北、自西向东逐渐推迟;与NDVI,EVI和归一化差值红外指数(normalized difference infrared index,NDII)相比,产品改进–NDVI(product improve–NDVI,PI_NDVI)更能反映冬小麦的实际长势。基于该指数监测冬小麦长势,2001―2015年间山东省冬小麦抽穗期长势整体呈上升趋势;但年际间波动较大,相同年份不同区域的冬小麦长势存在明显差异;而大部分区域长势状况比较一致,与多年平均状况持平。研究结果与已有的相关研究较为一致,基于遥感进行大范围和长时间作物长势监测的思路可以为以后研究提供一定的参考。     

基于电磁感应的干旱区土壤盐渍化定量遥感研究

以南疆典型干旱区Landsat 7 ETM+遥感图像为数据源,利用决策树分类法提取农业用地,并对农业用地进行移动式电磁感应调查(简称磁感调查)和光谱特征提取,同时分析磁感数据和图像光谱特征与土壤盐分含量的相关性,从而建立土壤盐分的定量反演模型。研究结果表明:土地利用类型决策树的分类精度达到93.75%,Kappa系数达0.915 4;经多元逐步回归分析,磁感调查获得的土壤盐分含量与差值植被指数(DVI)、ETM+图像第二波段像元值(B2)以及比值植被指数(RVI)间具有显著相关性,由此建立的遥感反演模型可用于土壤盐分含量的定量反演。经89个样点检验,基于磁感调查的土壤盐分遥感反演精度虽低于基于磁感调查的地统计空间分析的精度,但遥感定量反演值与磁感调查实测值仍具有良好的相关性,而且精度较高,因此利用本文方法进行土壤盐渍化大面积监测是快速有效的途径。     

作物种植成数的遥感监测精度评价

以河南开封和山西太谷地区作为研究区域 ,选用LandsatTM作为农作物种植面积遥感监测的数据源。利用LandsatTM提取河南开封实验区 2 0 0 1年的夏季作物和山西太谷地区 2 0 0 3年秋季作物的作物种植成数。同时 ,利用IKONOS ,QuickBird高分辨率遥感影像 ,通过地面调查进行了地面作物填图和分类 ,同样得到实验区的农作物种植成数。最后通过两种结果对比 ,表明开封实验区夏季作物的监测精度达到 99%以上 ,太谷实验区秋季作物的监测精度达到 97%以上 ,由此推断 ,表明利用LandsatTM监测农作物种植成数的精度能够满足中国农情遥感监测的运行化要求     

Monitoring winter wheat growth in North China by combining a crop model and remote sensing data

Both of crop growth simulation models and remote sensing method have a high potential in crop growth monitoring and yield prediction. However, crop models have limitations in regional application and remote sensing in describing the growth process. Therefore, many researchers try to combine those two approaches for estimating the regional crop yields. In this paper, the WOFOST model was adjusted and regionalized for winter wheat in North China and coupled through the LAI to the SAIL–PROSPECT model in order to simulate soil adjusted vegetation index (SAVI). Using the optimization software (FSEOPT), the crop model was then re-initialized by minimizing the differences between simulated and synthesized SAVI from remote sensing data to monitor winter wheat growth at the potential production level. Initial conditions, which strongly impact phenological development and growth, and which are hardly known at the regional scale (such as emergence date or biomass at turn-green stage), were chosen to be re-initialized. It was shown that re-initializing emergence date by using remote sensing data brought simulated anthesis and maturity date closer to measured values than without remote sensing data. Also the re-initialization of regional biomass weight at turn-green stage led that the spatial distribution of simulated weight of storage organ was more consistent to official yields. This approach has some potential to aid in scaling local simulation of crop phenological development and growth to the regional scale but requires further validation.     

Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data

With the availability of high frequent satellite data, crop phenology could be accurately mapped using time-series remote sensing data. Vegetation index time-series data derived from AVHRR, MODIS, and SPOT-VEGETATION images usually have coarse spatial resolution. Mapping crop phenology parameters using higher spatial resolution images (e.g., Landsat TM-like) is unprecedented. Recently launched HJ-1 A/B CCD sensors boarded on China Environment Satellite provided a feasible and ideal data source for the construction of high spatio-temporal resolution vegetation index time-series. This paper presented a comprehensive method to construct NDVI time-series dataset derived from HJ-1 A/B CCD and demonstrated its application in cropland areas. The procedures of time-series data construction included image preprocessing, signal filtering, and interpolation for daily NDVI images then the NDVI time-series could present a smooth and complete phenological cycle. To demonstrate its application, TIMESAT program was employed to extract phenology parameters of crop lands located in Guanzhong Plain, China. The small-scale test showed that the crop season start/end derived from HJ-1 A/B NDVI time-series was comparable with local agro-metrological observation. The methodology for reconstructing time-series remote sensing data had been proved feasible, though forgoing researches will improve this a lot in mapping crop phenology. Last but not least, further studies should be focused on field-data collection, smoothing method and phenology definitions using time-series remote sensing data.     

统计数据总量约束下全局优化阈值的冬小麦分布制图

大范围、长时间和高精度农作物空间分布基础农业科学数据的准确获取对资源、环境、生态、气候变化和国家粮食安全等问题研究具有重要现实意义和科学意义。本文针对传统阈值法农作物识别过程中阈值设置存在灵巧性差和自动化程度低等弱点,以中国粮食主产区黄淮海平原内河北省衡水市景县为典型实验区,首次将全局优化算法应用于阈值模型中阈值优化选取,开展了利用全局优化算法改进基于阈值检测的农作物分布制图方法创新研究。以冬小麦为研究对象,国产高分一号(GF-1)为主要遥感数据源,在作物面积统计数据为总量控制参考标准和全局参数优化的复合型混合演化算法SCE-UA (Shuffled Complex Evolution-University of Arizona)支持下,提出利用时序NDVI数据开展阈值模型阈值参数自动优化的冬小麦空间分布制图方法。最终,获得实验区冬小麦阈值模型最优参数,并利用优化后的阈值参数对冬小麦空间分布进行提取。通过地面验证表明,利用本研究所提方法获取的冬小麦识别结果分类精度均达到较高水平。其中冬小麦识别结果总量精度达到了99.99%,证明本研究所提阈值模型参数优化方法冬小麦提取分类结果总量控制效果良好;同时,与传统的阈值法、最大似然和支持向量机等分类方法相比,本研究所提阈值模型参数优化法区域冬小麦作物分类总体精度和Kappa系数分别都有所提高,其中,总体精度分别提高4.55%、2.43%和0.15%,Kappa系数分别提高0.12、0.06和0.01,这体现出SCE-UA全局优化算法对提高阈值模型冬小麦空间分布识别精度具有一定优势。以上研究结果证明了利用本研究所提基于作物面积统计数据总量控制以及SCE-UA全局优化算法支持下阈值模型参数优化作物分布制图方法的有效性和可行性,可获得高精度冬小麦作物空间分布制图结果,这对提高中国冬小麦空间分布制图精度和自动化水平具有一定意义,也可为农作物面积农业统计数据降尺度恢复重建和大范围区域作物空间分布制图研究提供一定技术参考。     

In-season crop classification using elements of the Kennaugh matrix derived from polarimetric RADARSAT-2 SAR data

Accurate spatio-temporal classification of crops is of prime importance for in-season crop monitoring. Synthetic Aperture Radar (SAR) data provides diverse physical information about crop morphology. In the present work, we propose a day-wise and a time-series approach for crop classification using full-polarimetric SAR data. In this context, the 4 × 4 real Kennaugh matrix representation of a full-polarimetric SAR data is utilized, which can provide valuable information about various morphological and dielectric attributes of a scatterer. The elements of the Kennaugh matrix are used as the parameters for the classification of crop types using the random forest and the extreme gradient boosting classifiers.The time-series approach uses data patterns throughout the whole growth period, while the day-wise approach analyzes the PolSAR data from each acquisition into a single data stack for training and validation. The main advantage of this approach is the possibility of generating an intermediate crop map, whenever a SAR acquisition is available for any particular day. Besides, the day-wise approach has the least climatic influence as compared to the time series approach. However, as time-series data retains the crop growth signature in the entire growth cycle, the classification accuracy is usually higher than the day-wise data.Within the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative, in situ measurements collected over the Canadian and Indian test sites and C-band full-polarimetric RADARSAT-2 data are used for the training and validation of the classifiers. Besides, the sensitivity of the Kennaugh matrix elements to crop morphology is apparent in this study. The overall classification accuracies of 87.75% and 80.41% are achieved for the time-series data over the Indian and Canadian test sites, respectively. However, for the day-wise data, a ∼6% decrease in the overall accuracy is observed for both the classifiers.     

Estimating inter-annual variability in winter wheat sowing dates from satellite time series in Camargue,France

Crop simulation models are commonly used to forecast the performance of cropping systems under different hypotheses of change. Their use on a regional scale is generally constrained, however, by a lack of information on the spatial and temporal variability of environment-related input variables (e.g., soil) and agricultural practices (e.g., sowing dates) that influence crop yields. Satellite remote sensing data can shed light on such variability by providing timely information on crop dynamics and conditions over large areas. This paper proposes a method for analyzing time series of MODIS satellite data in order to estimate the inter-annual variability of winter wheat sowing dates. A rule-based method was developed to automatically identify a reliable sample of winter wheat field time series, and to infer the corresponding sowing dates. The method was designed for a case study in the Camargue region (France), where winter wheat is characterized by vernalization, as in other temperate regions. The detection criteria were chosen on the grounds of agronomic expertise and by analyzing high-confidence time-series vegetation index profiles for winter wheat. This automatic method identified the target crop on more than 56% (four-year average) of the cultivated areas, with low commission errors (11%). It also captured the seasonal variability in sowing dates with errors of ±8 and ±16 days in 46% and 66% of cases, respectively. Extending the analysis to the years 2002–2012 showed that sowing in the Camargue was usually done on or around November 1st (±4 days). Comparing inter-annual sowing date variability with the main local agro-climatic drivers showed that the type of preceding crop and the weather conditions during the summer season before the wheat sowing had a prominent role in influencing winter wheat sowing dates.     

The assimilation of spectral sensing and the WOFOST model for the dynamic simulation of cadmium accumulation in rice tissues

The accurate detection of heavy metal-induced stress on crop growth is important for food security and agricultural, ecological and environmental protection. Spectral sensing offers an efficient and undamaged observation tool to monitor soil and vegetation contamination. This study proposed a methodology for dynamically estimating the total cadmium (Cd) accumulation in rice tissues by assimilating spectral information into WOFOST (World Food Study) model. Based on the differences among ground hyperspectral data of rice in three experiments fields under different Cd concentration levels, the spectral indices MCARI1, NREP and RH were selected to reflect the rice stress condition and dry matter production of rice. With assimilating these sensitive spectral indices into the WOFOST + PROSPECT + SAIL model to optimize the Cd pollution stress factor f_wi, the dynamic dry matter production processes of rice were adjusted. Based on the relation between dry matter production and Cd accumulation, we dynamically simulating the Cd accumulation in rice tissues. The results showed that the method performed well in dynamically estimating the total amount of Cd accumulation in rice tissues with R² over 85%. This study suggests that the proposed method of integrating the spectral information and the crop growth model could successfully dynamically simulate the Cd accumulation in rice tissues.     

Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data

Crop identification is the basis of crop monitoring using remote sensing. Remote sensing the extent and distribution of individual crop types has proven useful to a wide range of users, including policy-makers, farmers, and scientists. Northern China is not merely the political, economic, and cultural centre of China, but also an important base for grain production. Its main grains are wheat, maize, and cotton. By employing the Fourier analysis method, we studied crop planting patterns in the Northern China plain. Then, using time-series EOS-MODIS NDVI data, we extracted the key parameters to discriminate crop types. The results showed that the estimated area and the statistics were correlated well at the county-level. Furthermore, there was little difference between the crop area estimated by the MODIS data and the statistics at province-level. Our study shows that the method we designed is promising for use in regional spatial scale crop mapping in Northern China using the MODIS NDVI time-series.     

A Comparative Analysis of Phenological Curves for Major Crops in Kansas

The goal of this research was to conduct an initial investigation into whether a time-series NDVI reference curve library for crops over a growing season for one year could be used to map crops for a different year. Time-series NDVI libraries of curves for 2001 and 2005 were investigated to ascertain whether or not the 2001 dataset could be used to map crops for 2005. The 2005 16-day composite MODIS 250 m NDVI data were used to extract NDVI values from 1,615 field sites representing alfalfa, corn, sorghum, soybeans, and winter wheat. A k-means cluster analysis of NDVI values from the field sites was performed to identify validation sites with time-series NDVI spectral profiles characteristic of the major crop types grown in Kansas. After completing the field site refinement process, there were 1,254 field sites retained for further analysis, referred to as "final" field sites. The methods employed to evaluate whether the MODIS-based NDVI profiles for major crops in Kansas are stable from year-to-year involved both graphical and statistical analyses. First, the time-series NDVI values for 2005 from the final field sites were aggregated by crop type and the crop NDVI profiles were then visually assessed and compared to the profiles of 2001 to ascertain if each crop's unique phenological pattern was consistent between the two years. Second, separability within each crop class in the time-series NDVI data between 2001 and 2005 was investigated numerically using the Jeffries-Matusita (JM) distance statistic. The results seem to suggest that time-series NDVI response curves for crops over a growing period for one year of valid ground reference data may be useful for mapping crops for a different year when minor temporal shifts in the NDVI values (resulting from inter-annual climate variations or changes in agricultural management practices) are taken into account.