FY-3250m分辨率数据的华北平原冬小麦提取

冬小麦是中国最主要的粮食作物之一,利用遥感技术提取冬小麦种植区是遥感应用研究的一个重要方向。2008年以来发射的系列风云三号(FY-3)卫星均携带着中分辨率光谱成像仪(MERSI),该传感器有5个250 m分辨率的波段,波段范围包括可见光、近红外和热红外,观测数据包含丰富的地表信息,为大范围冬小麦种植区提取提供了新的数据源。首先,选取生长季前期多幅高质量的MERSI数据,采用分层提取的方法,对于不同的层次选用与待提取类别最为敏感的特征波段来构建相应的决策树,从而将每一幅影像中冬小麦种植区提取出来,然后,将多幅数据融合为一幅生长季内的冬小麦种植区图。最后,使用野外实地调查的数据进行精度验证,面积提取精度为90.8%。结果表明,在春季返青后,即可做出当季冬小麦种植分布图,为农情监测提供及时的信息支撑。     

利用AMSR-E数据反演华北平原冬小麦单散射反照率

首先,基于冬小麦不同生育期的地面实测参数,构建了组成冬小麦冠层的、包括不同尺寸和含水量的介电散射体模拟数据库,并在此基础上建立冬小麦单散射反照率和光学厚度分别在C(6.925 GHz)和X(10.65 GHz)波段之间的依赖关系。然后,根据一阶参数化模型推导得到的微波植被指数MVIs(Microwave Vegetation Indices)的物理表达式,结合AMSR-E被动微波亮温数据,反演了华北平原地区冬小麦不同生育期的单散射反照率。与MODIS日归一化差异植被指数NDVI的对比结果显示:冬小麦单散射反照率与NDVI随时间的变化趋势大致相同,但在冬小麦的抽穗期到乳熟期,NDVI呈现饱和趋势,而单散射反照率对小麦的生长变化仍旧比较敏感,在指示冬小麦生长方面具有一定优势。     

Effects of water and heat on growth of winter wheat in the North China Plain

The North China Plain (NCP) was selected as the study area and the effects of water and heat were analysed to determine the dominant factor affecting winter wheat growth. The mean, minimum and maximum temperatures, precipitation and soil moisture data were selected to analyse the correlations between the leaf area index (the growth indicator) and these factors using long time series half-monthly data (2–5 months) (from 1982 to 2010). The results showed that temperature was the main factor affecting the growth of winter wheat in the NCP. The growth of winter wheat had weak correlations with precipitation and soil moisture and the influence of water on winter wheat growth was smaller than the influence of heat. In the northern part of the NCP, mainly including the north-west region of Shandong Province and the southern region of Hebei Province, irrigation was necessary in late February and early March.     

自动化样本生成策略用于冬季作物制图——以兰陵县为例

准确地获取作物空间分布是作物生长监测和产量预测的前提。目前,遥感图像处理需要足够的人工采集的训练样本,因此,大规模作物分布的自动获取仍然是一个挑战。以高效、经济的方式获得足够的训练样本成为作物制图的关键因素之一。因此,本文结合冬季作物物候特征与Sentinel-2时间序列影像,提出了一种自动化样本生成策略用于冬季作物制图。首先,利用归一化植被指数（NDVI）时间序列曲线进行冬季作物的判别;然后,通过时间序列曲线相似性度量的方法,判断样本点与标准的绿色叶绿素植被指数（GCVI）时间序列曲线的差距,从而为未知样本赋予正确的标签;最后,利用获取的样本训练随机森林模型,实现研究区域的冬季作物提取。最终精度评定结果：总体精度（OA）为98.46%,Kappa为0.973,表明该方法对于快速实现冬季作物自动制图的有效性。     

基于遥感数据的多年平均物候不确定性研究

多年平均物候能够反映植被生长发育节律的均衡状态,是植被物候模拟与预测的关键参数之一。遥感已广泛用于地表物候监测,是空间多年平均物候信息的重要来源。然而,基于遥感的多年平均物候存在不同计算方法,如先确定每年时序曲线的物候点再求平均值（平均法）,以及先求多年平均时序曲线再确定物候点（参考曲线法）。上述方法的结果可能存在差异,但目前尚缺乏对这一不确定性及其影响的认识。针对该问题,本研究利用2001年—2016年遥感植被指数数据,分别在平均法和参考曲线法下提取中国森林生长季起始时间的多年平均值（■）,比较■的差异（■）及其空间异质性;进一步选取物候研究中常用指标,即以■为基础的温度“季前时长PD (Preseason Duration)”,分析■不同计算方法对物候—气候关系的潜在影响。结果表明,（1）不同方法下的■差异显著,总体上平均法小于参考曲线法（-2.6±2.2 d,占88%）,其中存在8.0%和6.0%的有效像元其动态平均法和固定平均法小于参考曲线法超过7 d,主要分布在东南丘陵地区。（2）■具有显著的空间异质性,主要表现为随年均温的升高而减小（Slope=0.07 d/℃,P<...     

遥感定量反演华北平原地区冬小麦含水量

针对现有植被水分反演算法在华北平原地区适用性差、反演精度低、不能实施有效监测的问题,该文基于地面实测冬小麦植被含水量(VWC)数据,基于归一化型和比值型植被水分指数这两种常见的指数类型,提出调节植被水分指数以削弱土壤背景的影响,使用多个波段反射率数据反演VWC,提高拟合精度80%以上,发展适用于华北平原的农作物水分含量反演模型。拟合冬小麦植被含水量的决定系数为0.51,均方根误差为0.95(kg·m^-2)。结果表明:调节植被水分指数能够削弱土壤背景影响,大幅度提高植被水分反演精度;同一种指数计算形式中,在水汽吸收谷内,基于更长波段反射率的植被水分指数反演精度更高;归一化型和比值型植被水分指数在反演精度方面无明显优劣,归一化型植被水分指数反演精度。     

Land surface phenology from VEGETATION and PROBA-V data. Assessment over deciduous forests

Land surface phenology has been widely retrieved although no consensus exists on the optimal satellite dataset and the method to extract phenology metrics. This study is the first comprehensive comparison of vegetation variables and methods to retrieve land surface phenology for 1999–2017 time series of Copernicus Global Land products derived from SPOT-VEGETATION and PROBA-V data. We investigated the sensitivity of phenology to (I) the input vegetation variable: normalized difference vegetation index (NDVI), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fraction of vegetation cover (FCOVER); (II) the smoothing and gap filling method for deriving seasonal trajectories; and (III) the method to extract phenological metrics: thresholds based on a percentile of the annual amplitude of the vegetation variable, autoregressive moving averages, logistic function fitting, and first derivative methods. We validated the derived satellite phenological metrics (start of the season (SoS) and end of the season (EoS)) using available ground observations of Betula pendula, B. alleghaniensis, Acer rubrum, Fagus grandifolia, and Quercus rubra in Europe (Pan-European PEP725 network) and the USA (National Phenology Network, USA-NPN). The threshold-based method applied to the smoothed and gap-filled LAI V2 time series agreed best with the ground phenology, with root mean square errors of ˜10 d and ˜25 d for the timing of SoS and EoS respectively. This research is expected to contribute for the operational retrieval of land surface phenology within the Copernicus Global Land Service.     

Development and implementation of a multiscale biomass model using hyperspectral vegetation indices for winter wheat in the North China Plain

Crop monitoring during the growing season is important for regional management decisions and biomass prediction. The objectives of this study were to develop, improve and validate a scale independent biomass model. Field studies were conducted in Huimin County, Shandong Province of China, during the 2006–2007 growing season of winter wheat (Triticum aestivum L.). The field design had a multiscale set-up with four levels which differed in their management, such as nitrogen fertilizer inputs and cultivars, to create different biomass conditions: small experimental fields (L1), large experimental fields (L2), small farm fields (L3), and large farm fields (L4). L4, planted with different winter wheat varieties, was managed according to farmers’ practice while L1 through L3 represented controlled field experiments. Multitemporal spectral measurements were taken in the fields, and biomass was sampled for each spectral campaign. In addition, multitemporal Hyperion data were obtained in 2006 and 2007. L1 field data were used to develop biomass models based on the relation between the winter wheat spectra and biomass: several published vegetation indices, including NRI, REP, OSAVI, TCI, and NDVI, were investigated. A new hyperspectral vegetation index, which uses a four-band combination in the NIR and SWIR domains, named GnyLi, was developed. Following the multiscale concept, the data of higher levels (L2 through L4) were used stepwise to validate and improve the models of the lower levels, and to transfer the improved models to the next level. Lastly, the models were transferred and validated at the regional scale using Hyperion images of 2006 and 2007. The results showed that the GnyLi and NRI models, which were based on the NIR and SWIR domains, performed best with R² > 0.74. All the other indices explained less than 60% model variability. Using the Hyperion data for regionalization, GnyLi and NRI explained 81–89% of the biomass variability. These results highlighted that GnyLi and NRI can be used together with hyperspectral images for both plot and regional level biomass estimation. Nevertheless, additional studies and analyses are needed to test its replicability in other environmental conditions.     

Sentinel-2数据的冬小麦地上干生物量估算及评价

作物生物量快速精确的监测对于农业资源的合理利用与农田的精准管理具有重要意义。近年来,遥感技术因其独特的优势已被广泛用于作物生物量的估算中。本文主要针对不同宽波段植被指数在冬小麦生物量(文中的生物量均是指地上干生物量)估算方面的表现进行探索。首先利用欧洲空间局最新的Sentinel-2A卫星数据提取出17种常见的植被指数,之后分别构建其与相应时期内采集的冬小麦地上生物量间的最优估算模型,通过分析两者间的相关性与敏感性,获取适宜进行生物量估算的指数。最后,绘制了研究区的生物量空间分布图。结果表明,所选的植被指数均与生物量显著相关。其中,红边叶绿素指数(CI_(re))与生物量的估算精度最高(决定性系数R~2为0.83;均方根误差RMSE为180.29 g·m~(–2))。虽然相关性较高,但部分指数,如归一化差值植被指数(NDVI)等在生物量较高时会出现饱和现象,从而导致生物量的低估。而加入红边波段的指数不仅能够延缓指数的饱和趋势,而且能够提高反演精度。此外,通过敏感性分析发现,归一化差值指数和比值指数分别在作物生长的早期和中后期对生物量的变化保持较高的敏感性。由于红边比值指数(SR_(re))和MERIS叶绿素敏感指数(MTCI)在冬小麦全生长季内一直对生物量的变化保持高灵敏性,二者是生物量估算中最为稳定的指数。     

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.     

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.     

Analysis of differences in vegetation phenology cycle of abandoned farmland,using harmonic analysis of time-series vegetation indices data: the case of Gwangyang City,South Korea

ABSTRACT

Globally, countries have experienced substantial increases in farmland abandonment. Although vegetation phenology is a key factor for the classification of land use, understanding of the phenological change of abandoned farmland is lacking. Using harmonic analysis of NDVI and NDWI extracted from Landsat imagery, this study investigates the distinctive phenological characteristics of abandoned farmland, which contrasts with that of three other agricultural types (paddy, agricultural field, orchard) in the study site of Gwangyang City in Jeollanam Province, South Korea. The results suggest that abandoned farmland has higher overall greenness coverage and overall water content in vegetation than the other uses. In terms of both indices, abandoned farmlands changed with relatively less fluctuation than those of other uses, suggesting the existence of constant and unmanaged vegetation from ecological succession, which differs from crop fields that undergo cultivation procedures. The significant harmonic components differed among agricultural types and vegetation indices. In paddy, NDVI was explained with multiple, higher-order harmonic components, while in other types only first-order components met the 5% statistical significance level. With NDWI, land types were more clearly discernible, because of the different cultivation procedures involving water: wet-field method (paddy), dryland farming (orchard, agricultural field), and no cultivation (abandoned farmland). The analysis confirms that harmonic analysis could be useful in discerning abandoned farmland among areas of active agricultural use and shows that the statistical significance of harmonic terms can be employed as indicators of different agricultural types. The observed pattern of the geographic distribution of abandoned farmland has policy implications for the promotion of sustainable reuse of marginal farmland.     

用NOAA气象卫星AVHRR的定量资料计算冬小麦种植面积的两种方法

本文提出了用NOAA气象卫星AVHRR的定量资料计算冬小麦种植面积的方法,即绿度指数法和单通道法。绿度指数法是根据冬小麦在特定生育期内绿度值基本上保持为常数的特点,用几块巳知样地的种植面积,推算整体种植面积;单通道法是只使用AVHRR第二通道的反照率来计算冬小麦种植面积。这两种方法的优点在于不需考虑大气削弱的订正处理问题,从而使计算得以简化。     

融合Landsat 8与Sentinel-2数据的红树林物候信息提取与分类

科学准确地监测红树林是保护海陆过渡性生态系统的基础和前提,但红树林分布于潮间带,难以进行大规模人工监测。遥感技术能够对红树林进行长时间、大面积监测,但已有研究尚存不足。一方面,红树林分布于热带、亚热带区域,受到天气条件限制难以获得长时间覆盖的有效光学遥感数据;另一方面,红树林极易与其他陆生植被混淆,仅利用多波段数据的光谱信息难以精确识别。本文以恒河三角洲孙德尔本斯地区为例,基于谷歌地球引擎GEE（Google Earth Engine）获取2016年全年的Landsat 8 OLI和Sentinel-2 MSI数据,利用物候信息进行红树林提取研究。首先,基于最小二乘回归构建两个传感器在相同指数之间的关系,重建时间序列数据,之后根据可分性判据选取增强型植被指数EVI（Enhanced Vegetation Index）和陆地表面水分指数LSWI（Land Surface Water Index）。其次,对两个指数的时间序列数据进行Savitzky-Golay滤波处理,并分别提取生长期始期等13种物候信息。最后,将两个指数的物候信息进行特征级联,采用随机森林RF（Random Forest）方法进行分类,提取研究区红树林范围。实验结果表明：Landsat 8 OLI和Sentinel-2 MSI数据融合可有效提升时间序列质量,与基于单一传感器数据的分类结果相比,总体精度提高1.58%;物候信息可以显著增强红树林与其他植被的可分性,与直接使用时间序列数据的分类结果相比,总体精度提高1.92%;同时考虑EVI和LSWI指数可极大地提升分类效果,与采用单一指数相比,总体精度分别提高14.11%和9.69%。因此,本文通过数据融合、物候信息提取和指数特征级联可以更好地提取红树林,总体精度达到91.02%,Kappa系数为0.892。研究验证了物候信息在红树林遥感监测中的应用潜力,提出的方法对科学准确地监测全球或区域红树林具有一定参考价值。     

冬小麦叶面积指数的高光谱估算模型研究

本文以山东禹城为研究区,利用地面实测光谱数据,探讨不同植被指数和红边参数建立高光谱模型反演冬小麦叶面积指数的精度。通过逐波段分析计算了4种植被指数(NDVI、RVI、SAVI、EVI),结合同步观测LAI数据,确定反演叶面积指数的最优波段;计算了5种常用的高光谱植被指数MCARI、MCARI2、OSAVI、MTVI2、MSAVI2,同时利用4种常用方法计算红边位置和红谷,与实测LAI进行回归分析,比较植被指数和红边参数模型对冬小麦LAI的估测精度。结果表明各因子与LAI均具有较高的相关性,整个研究区归一化植被指数具有最高的反演精度,确定了估算冬小麦LAI的最优模型,并使用独立的LAI观测数据对模型进行了验证。     

受蚜虫危害与干旱胁迫的冬小麦高光谱判别

从高光谱遥感角度判别冬小麦旱害和蚜虫危害,可进一步提高遥感监测灾害的准确性.在麦长管蚜的自然危害下,通过控制其生育期水分条件形成的不同程度的干旱胁迫,监测了灌浆末期冬小麦冠层反射率对蚜虫危害和干旱胁迫的反应;并经一阶微分数据变换,筛选出识别蚜虫虫害和干旱胁迫响应最敏感的光谱波段.实验结果表明:受蚜虫危害和干旱胁迫后,灌浆末期冬小麦在近红外波段的光谱特征变化比在可见光波段的显著,可见光和近红外波段是识别蚜虫危害和干旱胁迫最敏感的谱段.经一阶微分数据变换发现,自然降水处理(灌水量相当于需水量的＜40％)下的冬小麦光谱曲线的“红边”斜率最小;受蚜虫危害以及灌水量分别相当于需水量的＞70％,60％～ 70％,50％～ 60％和40％～ 50％水分处理下的“红边”斜率依次变大;受蚜虫危害冬小麦光谱曲线的“红边”位置波长最短(698 nm),其他不同水分处理结果随着干旱胁迫的加重向波长短的方向发生“蓝移”.因此,“红边”参数也可以作为判别冬小麦蚜虫危害和干旱胁迫的重要参数.     

利用面向对象分类方法提取冬小麦种植面积的研究

应用陆地卫星TM数据和遥感图像处理软件eCognition5.0和ENVI4.3软件,以面向对象的方法和监督分类波谱角法分别提取泰安市2005年冬小麦种植面积及其分布信息。逐像素分类的结果存在"椒盐"效应,而且很难克服同物异谱、同谱异物现象,面向对象的分类方法可以有效的集成专家知识和各种辅助数据,克服逐像素分类的弊端。分类结果表明,利用面向对象的分类方法可以获得比传统的像素级分类方法更高的分类精度,为冬小麦种植面积的自动提取提供了广阔的前景。     

Winter wheat mapping using a random forest classifier combined with multi-temporal and multi-sensor data

Wheat is a major staple food crop in China. Accurate and cost-effective wheat mapping is exceedingly critical for food production management, food security warnings, and food trade policy-making in China. To reduce confusion between wheat and non-wheat crops for accurate growth stage wheat mapping, we present a novel approach that combines a random forest (RF) classifier with multi-sensor and multi-temporal image data. This study aims to (1) determine whether an RF combined with multi-sensor and multi-temporal imagery can achieve accurate winter wheat mapping, (2) to find out whether the proposed approach can provide improved performance over the traditional classifiers, and (3) examine the feasibility of deriving reliable estimates of winter wheat-growing areas from medium-resolution remotely sensed data. Winter wheat mapping experiments were conducted in Boxing County. The experimental results suggest that the proposed method can achieve good performance, with an overall accuracy of 92.9% and a kappa coefficient (κ) of 0.858. The winter wheat acreage was estimated at 33,895.71?ha with a relative error of only 9.3%. The effectiveness and feasibility of the proposed approach has been evaluated through comparison with other image classification methods. We conclude that the proposed approach can provide accurate delineation of winter wheat areas.     

非监督分类的冬小麦种植信息提取模型

为了解决在区域冬小麦种植信息遥感提取过程中监督学习算法存在的需要地面样本数据支持、流程复杂、人为干扰因素多及自动化程度低等问题,本文以非监督分类为核心,结合多尺度技术,提出了一种新的非监督分类冬小麦种植信息提取模型。选取河北省辛集市为典型试验区,以2014年高分一号数据为数据源,对本文提出的模型进行实例验证。试验结果表明：该模型的Kappa系数为0.88,整体精度为94.00%;对于研究区内的冬小麦,在无需训练样本、人为干扰因素少等条件下,该模型具有与监督分类相似的提取精度,能够满足冬小麦种植信息地面遥感监测的需求。     

Quantifying winter wheat residue biomass with a spectral angle index derived from China Environmental Satellite data

Quantification of crop residue biomass on cultivated lands is essential for studies of carbon cycling of agroecosystems, soil-atmospheric carbon exchange and Earth systems modeling. Previous studies focus on estimating crop residue cover (CRC) while limited research exists on quantifying crop residue biomass. This study takes advantage of the high temporal resolution of the China Environmental Satellite (HJ-1) data and utilizes the band configuration features of HJ-1B data to establish spectral angle indices to estimate crop residue biomass. Angles formed at the NIR_IRS vertex by the three vertices at R, NIR_IRS, and SWIR (ANIR_IRS) of HJ-1B can effectively indicate winter wheat residue biomass. A coefficient of determination (R²) of 0.811 was obtained between measured winter wheat residue biomass and ANIR_IRS derived from simulated HJ-1B reflectance data. The ability of ANIR_IRS for quantifying winter wheat residue biomass using HJ-1B satellite data was also validated and evaluated. Results indicate that ANIR_IRS performed well in estimating winter wheat residue biomass with different residue treatments; the root mean square error (RMSE) between measured and estimated residue biomass was 0.038 kg/m². ANIR_IRS is a potential method for quantifying winter wheat residue biomass at a large scale due to wide swath width (350 km) and four-day revisit rate of the HJ-1 satellite. While ANIR_IRS can adequately estimate winter wheat residue biomass at different residue moisture conditions, the feasibility of ANIR_IRS for winter wheat residue biomass estimation at different fractional coverage of green vegetation and different environmental conditions (soil type, soil moisture content, and crop residue type) needs to be further explored.