Changes of multiple cropping in Huang-Huai-Hai agricultural region,China

Multiple cropping index (MCI) is the ratio of total sown area and cropland area in a region, which represents the regional time intensity of planting crops. Multiple cropping systems have effectively improved the utilization efficiency and production of cropland by increasing cropping frequency in one year. Meanwhile, it has also significantly altered biogeochemical cycles. Therefore, exploring the spatio-temporal dynamics of multiple cropping intensity is of great significance for ensuring food and ecological security. In this study, MCI of Huang-Huai-Hai agricultural region with intensive cropping practices was extracted based on a cropping intensity mapping algorithm using MODIS Enhanced Vegetation Index (EVI) time series at 500-m spatial resolution and 8-day time intervals. Then the physical characteristics and landscape pattern of MCI trends were analyzed from 2000–2012. Results showed that MCI in Huang-Huai-Hai agricultural region has increased from 152% to 156% in the 12 years. Topography is a primary factor in determining the spatial pattern dynamics of MCI, which is more stable in hilly area than in plain area. An increase from 158% to 164% of MCI occurred in plain area while there was almost no change in hilly area with single cropping. The most active region of MCI change was the intersection zone between the hilly area and plain area. In spatial patterns, landscape of multiple cropping systems tended to be homogenized reflected by a reduction in the degree of fragmentation and an increase in the degree of concentration of cropland with the same cropping system.     

Phenology from Landsat when data is scarce: Using MODIS and Dynamic Time-Warping to combine multi-year Landsat imagery to derive annual phenology curves

Green-leaf phenology describes the development of vegetation throughout a growing season and greatly affects the interaction between climate and the biosphere. Remote sensing is a valuable tool to characterize phenology over large areas but doing at fine- to medium resolution (e.g., with Landsat data) is difficult because of low numbers of cloud-free images in a single year. One way to overcome data availability limitations is to merge multi-year imagery into one time series, but this requires accounting for phenological differences among years. Here we present a new approach that employed a time series of a MODIS vegetation index data to quantify interannual differences in phenology, and Dynamic Time Warping (DTW) to re-align multi-year Landsat images to a common phenology that eliminates year-to-year phenological differences. This allowed us to estimate annual phenology curves from Landsat between 2002 and 2012 from which we extracted key phenological dates in a Monte-Carlo simulation design, including green-up (GU), start-of-season (SoS), maturity (Mat), senescence (Sen), end-of-season (EoS) and dormancy (Dorm). We tested our approach in eight locations across the United States that represented forests of different types and without signs of recent forest disturbance. We compared Landsat-based phenological transition dates to those derived from MODIS and ground-based camera data from the PhenoCam-network. The Landsat and MODIS comparison showed strong agreement. Dates of green-up, start-of-season and maturity were highly correlated (r 0.86-0.95), as were senescence and end-of-season dates (r > 0.85) and dormancy (r > 0.75). Agreement between the Landsat and PhenoCam was generally lower, but correlation coefficients still exceeded 0.8 for all dates. In addition, because of the high data density in the new Landsat time series, the confidence intervals of the estimated keydates were substantially lower than in case of MODIS and PhenoCam. Our study thus suggests that by exploiting multi-year Landsat imagery and calibrating it with MODIS data it is possible to describe green-leaf phenology at much finer spatial resolution than previously possible, highlighting the potential for fine scale phenology maps using the rich Landsat data archive over large areas.     

夏玉米生长过程曲线重建研究——以鹤壁市为例

增强型植被指数(EVI)时间序列数据(即植被生长曲线)是整个生育期内植被各种生物学特征的综合反映。由于太阳位置、大气、地表和传感器位置与性能等的影响,根据遥感数据计算的EVI值往往比实际值偏低(存在大量噪声),并不能反映植被生长的真实情况,应用前需进行去噪重建工作。针对目前生长曲线重建研究大多是针对MODIS等国外遥感数据的情况,在综合分析重建方法的基础上,利用风云3号卫星的MERSI中分辨率遥感卫星数据构建鹤壁市夏玉米的EVI生长过程曲线。首先,用最大值合成法(MVC)对原始EVI时间序列数据进行初步的去云处理。接着,利用基于时间域的Savitzky-Golay滤波(简称SG滤波)对该EVI序列进行进一步的平滑去噪处理,结果发现,在噪声点EVI数值提高了,但同时在其他不是噪声点的地方EVI的值降低了。针对这种不合理的情况,利用基于SG迭代滤波取上包络线的改进方法进行处理,很好地克服了上述缺陷,在非噪声点EVI数值适当提高,且曲线平滑,达到了生长曲线重建的目的。然后,采用基于频率域的小波变换方法进行实验对比,结果发现,小波变换存在着与经典SG滤波类似的缺陷,而且在曲线末端存在突变情况。经过比较分析发现,针对研究区的实际情况,改进SG迭代滤波是较优的去噪方法。     

应用时间序列EVI的MERSI多光谱混合像元分解

针对风云3数据的特点,本文将EVI生长曲线引入多光谱混合像元的分解。首先,利用Landsat8 OLI影像,采用支持向量机的分类方法,提取研究区域的耕地信息,利用该信息对风云MERSI数据进行掩膜处理,获得研究区域的耕地影像。接着,利用MERSI时序影像,计算像元EVI值,通过SG滤波,构建农作物(端元)和混合像元的EVI生长曲线。通过实地调查,获取研究区的农作物端元,尤其对主要的农作物玉米,在空间上均匀选取了14个端元。然后,采用传统的方法,将14种玉米端元生长曲线分别与其它端元组合,进行混合像元分解。发现分解的效果差异很大,提取的玉米种植面积从191.90 km2到574.83 km2不等。为提高分解精度,借用光谱匹配(光谱夹角最小)的方法(用生长曲线代替光谱曲线)自适应选择与混合像元EVI曲线最相似的玉米端元作为组合端元,进行混合像元分解。结果得到玉米的种植面积为589.95 km2,比传统方法的最好(相对)精度提高了2%。     

基于MODIS资料的宁夏LST反演方法新探索

为快速、宏观、全面地获取陆面生态重要参数陆面温度(LST),避免分裂窗算法中诸多参数的估计和参数的适用范围限制,加快计算速度,更好地利用中国气象局"三站四网"的建设成果,利用宁夏2005-2007年13个时次过境晴空地表MODIS资料及对应过境时17个自动气象站观测数据,筛选、优化引入对LST影响较大的水汽通道、NDVI和EVI参数,建立基于MODIS遥感和地面自动气象站观测数据反演陆面温度(LST)的统计模式.研究结果表明:引入相关参数后,宁夏各季及全年模式的相关性和精度有较大提高,且水汽通道和EVI的参数组合最优.与分裂窗算法相比,省去了对大气透过率的估算以及对地表比辐射率估计的繁琐计算,与地面自动站观测真实值误差70.1%能够控制在4.0℃以内,计算速度快,能够满足一般业务的需求,易于推广使用.     

Remotely-sensed phenology of Italian forests: Going beyond the species

Remotely sensed observations of seasonal greenness dynamics represent a valuable tool for studying vegetation phenology at regional and ecosystem-level scales. We investigated the seasonal variability of forests in Italy, examining the different mechanisms of phenological response to biophysical drivers. For each point of the Italian National Forests Inventory, we processed a multitemporal profile of the MODIS Enhanced Vegetation Index. Then we applied a multivariate approach for the purpose of (i) classifying the Italian forests into phenological clusters (i.e. pheno-clusters), (ii) identifying the main phenological characteristics and the forest compositions of each pheno-cluster and (iii) exploring the role of climate and physiographic variables in the phenological timing of each cluster. Results identified four pheno-clusters, following a clear elevation gradient and a distinct separation along the Mediterranean-to-temperate climatic transition of Italy. The “High-elevation coniferous” and the “High elevation deciduous” resulted mainly affected by elevation, with the former characterized by low annual productivity and the latter by high seasonality. To the contrary, the “Low elevation deciduous” showed to be mostly associated to moderate climate conditions and a prolonged growing season. Finally, summer drought was the main driving variable for the “Mediterranean evergreen”, characterized by low seasonality. The discrimination of vegetation phenology types can provide valuable information useful as a baseline framework for further studies on forests ecosystem and for management strategies.     

Effect of spatial resolution of satellite images on estimating the greenness and evapotranspiration of urban green spaces

Urban green spaces (UGS), like most managed land covers, are getting progressively affected by water scarcity and drought. Preserving, restoring and expanding UGS require sustainable management of green and blue water resources to fulfil evapotranspiration (ET) demand for green plant cover. The heterogeneity of UGS with high variation in their microclimates and irrigation practices builds up the complexity of ET estimation. In oversized UGS, areas too large to be measured with in situ ET methods, remote sensing (RS) approaches of ET measurement have the potential to estimate the actual ET. Often in situ approaches are not feasible or too expensive. We studied the effects of spatial resolution using different satellite images, with high-, medium- and coarse-spatial resolutions, on the greenness and ET of UGS using Vegetation Indices (VIs) and VI-based ET, over a 780-ha urban park in Adelaide, Australia. We validated ET with the ground-based ET method of Soil Water Balance. Three sets of imagery from WorldView2, Landsat and MODIS, and three VIs including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Enhanced Vegetation Index 2 (EVI2), were used to assess long-term changes of VIs and ET calculated from the different imagery acquired for this study (2011–2018). We found high correspondence between ET-MODIS and ET-Landsat (R² > 0.99 for all VIs). Landsat-VIs captured the seasonal changes of greenness better than MODIS-VIs. We used artificial neural network (ANN) to relate the RS-ET and ground data, and ET-MODIS (EVI2) showed the highest correlation (R² = 0.95 and MSE =0.01 for validation). We found a strong relationship between RS-ET and in situ measurements, even though it was not explicable by simple regressions; black box models helped us to explore their correlation. The methodology used in this research makes a strong case for the value of remote sensing in estimating and managing ET of green spaces in water-limited cities.     

不同植被指数在基于TVDI方法反演土壤水分中的应用

研究增强型植被指数基于Landsat-8数据反演土壤水分的可行性及适用性,分析研究区土壤水分总体分布,提高该地区应对干旱灾害的能力。基于温度植被干旱指数方法,以淮河流域上游地区作为研究区,基于2017年2月的Landsat-8影像,分别计算了地表温度、归一化植被指数、增强型植被指数,基于TVDI构建了两种土壤水分反演模型。研究比较了:1) EVI在TM数据中的应用特点;2)研究区土壤含水率的空间分布特征;3)两种模型反演结果的差异。结果表明:1)基于TM数据计算的EVI总体明显低于NDVI,但不同时间段的结果并不总是低于NDVI;2)基于EVI的模型结果精度低于基于NDVI模型结果。3)两种模型结果与植被覆盖度、地表温度的关系均为负相关,其中,基于EVI的模型结果与地表温度的负相关程度极高,即基于EVI的模型结果受植被影响较小,受温度影响程度高。     

利用MODIS数据识别水稻关键生长发育期

利用遥感方法提取中国范围内的水稻关键生长发育期。首先, 对时间序列Terra MODIS-EVI(Enhanced Vegetation Index)进行傅里叶和小波低通滤波平滑处理, 然后, 根据水稻在移栽期、分蘖初期、抽穗期和成熟期的EVI变化特征, 实现对各个生长发育期的识别。通过将利用2005年MODIS数据识别的结果与当年气象台站的地面观测资料进行比较, 采用本研究中的识别方法得出的水稻各个生长发育期的绝对误差大部分小于16d, 经过F检验表明提取的结果与地面观测资料在0.05水平下具有显著一致性。研究中的信息提取方法可被用于其他年份的水稻生长发育期识别, 根据其他作物的生长发育特点, 也可能适合于提取其他作物的生长发育期。     

基于MODIS的沿海带状植被NDVI/EVI季节变化研究——以江苏沿海互花米草盐沼为例

植被指数的时空变化是目前全球变化研究的热点问题。该文以江苏沿海互花米草盐沼为例,运用MODIS数据探索沿海带状植被NDVI/EVI的季节变化规律。结果表明,互花米草的返青(出苗)、抽穗、种子成熟等主要物候期在VI曲线上均能得到很好体现。本研究可为监测互花米草盐沼扩展趋势、加强管理、趋利避害提供基础数据,同时也为沿海其它带状植被监测提供借鉴。