以地表温度-植被指数特征空间为基础基于MODIS数据进行土壤水分监测研究

Soil moisture has been considered as one of the main indicators that are widely used in the fields of hydrology, climate, ecology and others. The land surface temperature-vegetation index （LST-VI） space has comprehensive information of the sensor from the visible to thermal infrared band and can well reflect the regional soil moisture conditions. In this study, 9 pairs of moderate-resolution imaging spectroradiometer （MODIS） products （MOD09A1 and MODllA2）, covering 5 provinces in Southwest China, were chosen to construct the LST-VI space, and then the spatial distribution of soil moisture in 5 provinces of Southwest China was monitored by the temperature vegetation dryness index （TVDI）. Three LST-VI spaces were constructed by normalized difference vegetation index （NDVI）, enhanced vegetation index （EVI）, and modified soil-adjusted vegetation index （MSAVI）, respectively. The correlations between the soil moisture data from 98 sites and the 3 TVDIs calculated by LST-NDVI, LST-EVI and LST-MSAVI, respectively, were analyzed. The results showed that TVDI was a useful parameter for soil surface moisture conditions. The TVDI calculated from the LST-EVI space （TVDIE） revealed a better correlation with soil moisture than those calculated from the LST-NDVI and LST-MSAVI spaces. From the different stages of the TVDIE space, it is concluded that TVDIE can effectively show the temporal and spatial differences of soil moisture, and is an effective approach to monitor soil moisture condition.     

普通克里格法在土壤有机碳制图中的应用

The quantification of the pattern and spatial distribution of soil organic carbon (SOC) is fundamental to understand many ecosystem processes.This study aimed to apply ordinary kriging (OK) to model the spatial distribution of SOC in a selected part of Zambia.A total of 100 soil samples were collected from the study area and analyzed for SOC by determining soil oxidizable carbon using the Walkley-Black method.An automated fitting procedure was followed when modeling the spatial structure of the SOC data with the exponential semivariogram.The results indicated that the short range spatial dependence of SOC was strong with a nugget close to zero.The spatial autocorrelation was high to medium with a nugget to sill ratio of 0.25.The root mean square error of the predictions was 0.64,which represented 58.18％ of the mean observed data for SOC.It can be concluded that the generated map could serve as a proxy for SOC in the region where evidence of spatial structure and quantitative estimates of uncertainty are reported.Therefore,the maps produced can be used as guides for various uses including optimization of soil sampling.     

通过光谱辐射方法测定估算土壤化学性质

The contents of nitrogen and organic carbon in an agricultural soil were analyzed using reflectance measurements （n = 52） performed with an ASD FieldSpee-Ⅱ spectroradiometer. For parameter prediction, empirical models based on partial least squares （PLS） regression were defined from the measured reflectance spectra （0.4 to 2.4 μm）. Here, reliable estimates were obtained for nitrogen content, but prediction accuracy was only moderate for organic carbon. For nitrogen, the real spatial pattern of within-field variability was reproduced with high accuracy. The results indicate the potential of this method as a quick screening tool for the spatial assessment of nitrogen and organic carbon, and therefore an appropriate alternative to time- and cost-intensive chemical analysis in the laboratory.     

Model-Based Integrated Methods for Quantitative Estimation of Soil Salinity from Hyperspectral Remote Sensing Data:A Case Study of Seffected South African Soils

Soil salinization is a land degradation process that leads to reduced agricultural yields. This study investigated the method that can best predict electrical conductivity (EC) in dry soils using individual bands, a normalized difference salinity index (NDSI), partial least squares regression (PLSR), and bagging PLSR. Soil spectral reflectance of dried, ground, and sieved soil samples containing varying amounts of EC was measured using an ASD FieldSpec spectrometer in a darkroom. Predictive models were computed using a training dataset. An independent validation dataset was used to validate the models. The results showed that good predictions could be made based on bagging PLSR using first derivative reflectance (validation R2 = 0.85), PLSR using untransformed reflectance (validation R2 = 0.70), NDSI (validation R2 = 0.65), and the untransformed individual band at 2257 nm (validation R2 = 0.60) predictive models. These suggested the potential of mapping soil salinity using airborne and/or satellite hyperspectral data during dry seasons.     

光谱指数用于EO-1 Hyperion 数据估算中国黄河三角洲地区土壤盐分

Soil salinization is one of the most common land degradation processes. In this study, spectral measurements of saline soil samples collected from the Yellow River Delta region of China were conducted in laboratory and hyperspectral data were acquired from an EO-1 Hyperion sensor to quantitatively map soil salinity in the region. A soil salinity spectral index (SSI) was constructed from continuum-removed reflectance (CR-reflectance) at 2052 and 2203 nm, to analyze the spectral absorption features of the salt-affected soils. There existed a strong correlation (r = 0.91) between the SSI and soil salt content (SSC). Then, a model for estimation of SSC with SSI was established using univariate regression and validation of the model yielded a root mean square error (RMSE) of 0.986 and an R² of 0.873. The model was applied to a Hyperion reflectance image on a pixel-by-pixel basis and the resulting quantitative salinity map was validated successfully with RMSE = 1.921 and R² = 0.627. These suggested that the satellite hyperspectral data had the potential for predicting SSC in a large area.     

华北半干旱区土壤养分和植被特征的小尺度空间异质性研究

A field experiment was conducted at Kezuohouqi County, Inner Mongolia Autonomous Region of China, which was located on the southeastern edge of the Horqin Sandy Land, to study the spatial variability of soil nutrients for a small-scale, nutrient-poor, sandy site in a semi-arid region of northern China; to investigate whether or not there were ＂islands of fertility＂ at the experimental site; and to determine the key nutrient elements that sustained ecosystem stability. Results obtained from geostatistical analysis indicated that the spatial distribution pattern of soil total nitrogen （STN） was far different from those of soil organic matter （SOM）, total phosphorus （STP）, and total potassium （STK）. Compared to SOM, STP, and STK, STN had a lower structural heterogeneity ratio and a longer range, while other elements were all similar. In addition, STN had an isotropic spatial structure, whereas the others had an anisotropic spatial structure. The spatial structure patterns of herbage species, cover, and height also differed, indicating that spatial variability was subjected to different ecological factors. Differences in the spatial variability patterns among soil nutrients and vegetation properties showed that soil nutrients for a small-scale were not the primary limiting factors that influenced herbage spatial distribution patterns. Incorporating spatial distribution patterns of tree species, namely, Pinus sylvestris var. mongolica Litv. and shrub Lespedeza bicolor Turcz. in a research plot and using fractal dimension, SOM, STP, and STK were shown to contribute to the ＂islands of fertility＂ phenomenon, however STN was not, possibly meaning that nitrogen was a key limiting element. Therefore, during restoration of similar ecosystems more attention should be given to soil nitrogen.     

一种新的辨别土地利用/覆被的遥感图像融合方法

In China, accelerating industrialization and urbanization following high-speed economic development and population increases have greatly impacted land use/cover changes, making it imperative to obtain accurate and up to date information on changes so as to evaluate their environmental effects. The major purpose of this study was to develop a new method to fuse lower spatial resolution multispectral satellite images with higher spatial resolution panchromatic ones to assist in land use/cover mapping. An algorithm of a new fusion method known as edge enhancement intensity modulation (EEIM) was proposed to merge two optical image data sets of different spectral ranges. The results showed that the EEIM image was quite similar in color to lower resolution multispectral images, and the fused product was better able to preserve spectral information. Thus, compared to conventional approaches, the spectral distortion of the fused images was markedly reduced. Therefore, the EEIM fusion method could be utilized to fuse remote sensing data from the same or different sensors, including TM images and SPOT5 panchromatic images, providing high quality land use/cover images.     

1980-2015年中国农田生态系统的N素平衡

Surface nitrogen （N） balances for China＇s crop production systems was estimated using statistical data collected from 1980 to 2004 at the national and provincial scale and from 1994 to 1999 at the county level. There was a surplus N balance throughout these periods, but the surplus was nearly stable in recent years. Projections using nonseasonal Box-Jenkins model or exponential models show that the N surplus for the total cultivated land in China was likely to increase from 142.8 kg ha^-1 in 2004 to 168.6 kg ha 1 in 2015. The N balance surplus in the more developed southeastern provinces was the largest, and was slightly less in the central region, which caused the nitrate pollution in the ground water. The N surplus was much less in the western and northern provinces because of lower synthetic fertilizer inputs. The region with high N risk includes Beijing Municipality and Jiangsu, Zhejiang, Fujian, Guangdong, Hubei, and Shandong provinces for 2002-2004. The projections suggested that 15 provinces （or municipalities） in the middle and southeastern part of China except Jiangxi and Shanxi provinces would become the high-risk region by 2015. The level of economic development, transportation, and labor force condition had an important effect on the N balance surplus at the county level, but the last two factors showed remarkable impact at the provincial level. To decrease the nonpoint pollution （Npp） risk from crop production, the authors suggested to reduce the target level for national grain self-sufficiency to 90%-95% and change the regional structure of grain production by moving some of the future grain production from the high Npp risk areas of eastern China to parts of the central and western provinces where the Npp risk was much less.     

A New Fusion Technique of Remote Sensing Imagesfor Land Use／Cover

In China, accelerating industrialization and urbanization following high-speed economic development and populationincreases have greatly impacted land use/cover changes, making it imperative to obtain accurate and up to date informationon changes so as to evaluate their environmental effects. The major purpose of this study was to develop a new methodto fuse lower spatial resolution multispectral satellite images with higher spatial resolution panchromatic ones to assistin land use/cover mapping. An algorithm of a new fusion method known as edge enhancement intensity modulation(EEIM) was proposed to merge two optical image data sets of different spectral ranges. The results showed that the EEIM image was quite similar in color to lower resolution multispectral images, and the fused product was better able topreserve spectral information. Thus, compared to conventional approaches, the spectral distortion of the fused images wasmarkedly reduced. Therefore, the EEIM fusion method could be utilized to fuse remote sensing data from the same ordifferent sensors, including TM images and SPOT5 panchromatic images, providing high quality land use/cover images.     

Utilizing landsat TM imagery to map greenhouses in Qingzhou, Shandong Province, China

To build a rapid and accurate method for greenhouse vegetable land information extraction using an index model derived from TM digital data of Qingzhou City, Shandong Province, based on a systematic analysis of the spectral characteristics of different land use types in the study area, a subset of the image was first made to eliminate the mountainous region not associated with vegetable distribution, and then water body pixels were masked. With this the VI index model for greenhouse vegetable land extraction was developed. The index model indicated greenhouse vegetable land for Qingzhou in April 2002 was concentrated in the southeast and around rural residential areas. Field data used for an accuracy evaluation showed that greenhouse hectares determined with remote sensing were 95.9% accurate, and accuracy for the spatial distribution of greenhouse vegetable land cross checked with a random sample was 96.3%. Therefore, this approach provided an effective method for greenhouse vegetable land information extraction and has potential significance for management of greenhouse vegetable production in the study area, as well as North China.     

MODIS数据辅助的GF-1影像晴空光合有效辐射反演

面向农作物产量监测对中高分辨率遥感数据光合有效辐射(photosynthetically available radiation,PAR)反演的实际需求,该文选择山东省禹城市2014年1月至2014年12月共13景GF-1/WFV卫星影像作为数据源,基于中分辨率成像光谱仪(moderate-resolution imaging spectroradiometer,MODIS)地表反射率产品作为辅助数据源,开发了适于业务运行的WFV数据气溶胶光学厚度(aerosol optical depth,AOD)及PAR的反演算法。算法核心是采用6S(second simulation of satellite signal in the solar spectrum)大气辐射传输模型,建立包括AOD在内的大气参数与查找表(look-up table,LUT),结合大气顶层太阳入射辐照度及卫星入瞳处辐射亮度值反演地表反射率数据,通过与WFV蓝光波段地表反射率数据对比获取大气参数。通过反演的大气参数计算400~700 nm连续光谱区间的PAR值,并建立WFV数据离散红、绿、蓝光波段与连续光谱区间PAR的转换系数,实现WFV数据PAR的反演。其中,WFV蓝光波段反射率数据与MODIS地表反射率数据关系、离散到连续谱段PAR的关系可以从美国地质勘探局(United States Geological Survey,USGS)提供的典型地物波谱库数据理论计算获取。利用中国生态系统研究网络(chinese ecosystem research network,CERN)禹城站地面观测值进行验证结果表明,该文提出的算法总体精度达到92.63%,平均绝对误差为14.56 W/m~2,平均相对误差7.37%,具有业务应用的潜力。     

基于历史时序植被指数库的多源数据作物面积自动提取方法

针对目前作物提取工作中难以综合应用多源遥感数据进行自动分类的现状,该文以新疆博乐市为试验区,使用多年MODIS数据建立各类作物历史参考时序植被指数曲线库。对TM和环境星数据共同构成的当年时序数据通过植被指数转换、曲线相似性比较,并结合区分不同作物的关键时相,在长时期种植制度变化不大的区域,自动提取作物种植面积。结果表明:该方法使用多源(环境星+TM)中高分辨率遥感数据构建的时序植被指数提取作物的总体精度可达到90%以上;与传统的监督分类方法相比,省去了人工采集训练区的步骤,实现了作物种植面积的自动提取。     

Validation and Correction of MOD15‐LAI using In Situ Rice LAI in Southern China

Abstract

MOD15 are one of the standard science products available from the moderate resolution imaging spectroradiometer (MODIS) instrument on the Earth‐observing system (EOS) Terra platform, launched in December 1999. An important requirement of MODIS science products is that they be rigorously validated. In this study, a site intensive MOD15‐LAI (leaf area index) product validation was conducted in southern China in 2003. Results showed that the differences between in situ rice LAI and MOD15‐LAI change in different rice growth. The differences are larger in booting stage, heading stage, and milking stage, but smaller in tiller stage and maturing stage. Otherwise, results indicate that the MODIS algorithm will underestimate LAI value by about 5–10% in total from 1‐km resolution data over the in situ rice LAI in southern China the mean and standard deviation deviated from MOD15‐LAI are smaller than those of in situ rice LAI. Last, according to different stages of rice growth, corrected models were established, and the corrected MOD15‐LAI matched well with in situ LAI.     

引入时相信息的耕地土壤有机质遥感反演模型

土壤有机质(soil organic matter,SOM)是土壤质量评价的重要指标。监测SOM含量及其空间分布对土壤利用与保护、土壤有机碳库估算等具有重要意义。该文以松嫩平原典型区为研究区,采集4种主要土壤类型样本共147个,获取裸土期多时相MODIS地表反射率8 d合成产品,以单期、多期影像所构建光谱指数作为输入量,构建包含含水量变化与有机质含量信息的多光谱指数,建立SOM线性回归遥感反演模型,揭示SOM空间分布规律。结果表明:由于土壤含水量空间差异随时间变化,基于单期影像构建的模型主要输入量发生规律性改变,其中年积日137 d裸土条件最好,反演模型最优;比值光谱指数R61与SOM显著相关,而和含水量相关性极小,适于作为反演模型输入量;基于多期影像构建的模型引入时相信息后,精度与稳定性较单期影像模型显著提高,其中基于年积日137、105 d两期影像光谱指数所建立的多元线性模型最优;松嫩平原SOM呈现由东北向西南递减趋势。     

不同叶面积指数遥感产品在呼伦贝尔草甸草原的应用对比

叶面积指数(leaf area index,LAI)是植被生理过程模拟的重要参数之一,对植被气候关系、全球气候变化研究等具有重要的意义。近年来LAI产品越来越多,这些产品的精度、区域适用性都不相同。为了选择适用于呼伦贝尔典型草甸草原的LAI产品,为以后在呼伦贝尔展开工作提供便利,该文以内蒙古呼伦贝尔草甸草原为研究区域,利用2013年6-8月6期地面试验数据,以HJ-1A/B CCD高分辨率影像为中间桥梁建立植被指数SR与LAI的统计模型,反演得到LAI参考图像,对研究区域内与地面试验同期的MODIS LAI和GLASS LAI、GEOV1 LAI产品分别进行了直接验证与交叉验证。结果显示,3个LAI产品均存在高估现象,以GLASS LAI最为显著约高估41%,其次是MODIS LAI约高估了32%。GEOV1 LAI产品准确性最高,RMSE=0.289 MAE=0.216。GLASS LAI与GEOV1 LAI产品的相关性最好(R2=0.6465)。通过对比全年LAI产品发现,3个产品具有良好的时序一致性。GLASS LAI呈现为平滑曲线,高估现象主要存在于LAI值较小时。MODIS LAI最不稳定性,波动性较大。GEOV1 LAI产品在第133天至第201天这段时间内LAI值比其他两个产品的LAI值小;在第202天后GEOV1 LAI值与GLASS LAI值相差无几,高于MODIS LAI。根据对比分析结果,GEOV1 LAI产品最适用于呼伦贝尔典型草甸草原。通过提取质量控制层数据,确定云覆盖不是影响LAI异常的原因。     

陕西省植被水分利用效率及与气候因素的关系

水分利用效率是反映生态系统碳水循环的重要变量。利用MODIS的总初级生产力以及蒸散发数据产品,结合气象数据,对2002—2012年陕西省水分利用效率进行研究。结果发现,陕西省中部平原区年均WUE较高,北部年均WUE较低;在各植被类型中,耕地的年均WUE最高,达1.59 g/（m²·mm）,而灌草地年均WUE则较低。在不同年降水量及年均温区域,WUE与气候因子的关系有所差别:在年降水量低于627 mm的地区,WUE随着降水量增加而增加;在年均温低于11℃的地区,WUE随着年均温的增加而增加。2002—2012年陕西省各植被类型WUE呈增加趋势,其中林地WUE增加趋势不显著。不同的植被类型,其WUE与气候因子关系也不同,其中降水量对WUE产生显著影响的地区主要集中在耕地和草地,而温度对WUE产生显著影响的地区主要集中在林地。     

利用改进的MOD16模型估算区域蒸散发

蒸散发（Evapotranspiration,ET）是地表水循环和能量循环的关键纽带,准确、定量地估算区域ET对于理解陆-气相互作用、全球气候变化等至关重要。MOD16模型基于Penman-Monteith（P-M）方程,是一种获取区域ET的重要遥感模型。然而,MOD16模型没有直接利用土壤水分信息,而是通过相对湿度（Relative Humidity,RH）、饱和水汽压差（Vapor Pressure Deficit,VPD）、叶面积指数（Leaf Area Index,LAI）等间接表达土壤水分信息的作用,这可能会给区域ET的估算带来一些不确定性。该研究将归一化水指数（Normalized Difference Water Index,NDWI）作为土壤水分信息的补充项,对MOD16模型的地表阻抗进行修正,以改进MOD16模型（改进后的模型为MOD16-sm）,并将改进后的模型在中国西北干旱区绿洲进行验证和应用。模型验证包括模拟值与观测值的对比及误差分析。模拟值与观测值的对比分析结果表明,MOD16-sm模型获取的ET精度较高,决定系数（Coefficient of Determination,R2）为0.77,均方根误差（Root Mean Square Error,RMSE）为0.8 mm/d,平均绝对误差（Mean Absolute Deviation,MAE）为0.46 mm/d;误差分析结果显示,MOD16-sm模型估算结果的误差控制优于MOD16模型,结合模拟值与观测值的对比分析可知,MOD16-sm模型改善了MOD16模型的部分高估现象,MOD16-sm模型能更好地反映土壤水分对ET的影响。模型应用包括ET估算值的空间分布分析及ET估算值的频率分布统计。对MOD16-sm模型的估算结果进行空间分析发现,高植被覆盖区的ET值较高,低植被覆盖区的ET值较低,说明MOD16-sm模型的ET估算结果与土地利用类型密切相关;研究区ET估算值的频率分布结果表明,MOD16-sm模型能较好地反映和表达出不同植被覆盖区的ET通量异质性。因此,利用NDWI对MOD16模型进行改进是可行的和合理的,该研究可为提高区域ET的估算精度提供参考和思路。     

基于TVDI和气象数据的陕西省春季旱情时空分析

陕西省居于中国腹地,受干旱影响严重,开展遥感旱情监测工作,认识旱情的时空特征具有重要的现实意义。利用MODIS产品MOD11A2和MOD13A2构建NDVI-Ts特征空间,获取陕西地区温度植被干旱指数(Temperature Vegetation Dryness Index,TVDI),在利用土壤湿度和气象指数验证了TVDI反映旱情有效性的基础上,以TVDI为干旱指标分析了2000—2015年陕西省春季(2—5月)旱情的时空变化特征。结果表明:(1)土壤相对湿度和降水量与TVDI均呈负相关,TVDI对降水量存在一定的滞后性;(2)近16年来,陕西省年年有旱,但春季旱情总体呈逐年减轻的趋势,年内2—5月旱情会逐渐加重;(3)空间上,近16年来陕西省春旱较为严重的地区主要分布在陕北北部以及关中东北部,而陕北南部、关中西南部及陕南地区旱情较轻。     

AMSR2缺失数据重建及其土壤湿度反演精度评价

新型传感器土壤湿度产品在水分循环、农业管理等领域应用之前,需要评价其在不同区域的反演精度与数据质量。该文针对目前全球空间分辨率最高的AMSR2(advanced microwave scanning radiometer 2)3级微波土壤湿度产品,实现了缺失数据重建及其反演精度评价目的。论文研究引入离散余弦函数表达的惩罚最小二乘回归(discrete cosine transformation-partial least square,DCT-PLS)方法,重建因卫星轨道等原因造成的缺失与异常值,以构建时空分布连续的土壤湿度数据;进而以山西省为例,采用1 km分辨率的MODIS光学波段数据对AMSR2微波产品进行降尺度处理,通过实测土壤湿度、温度植被干旱指数(temperature vegetation dryness index,TVDI)验证评价这2种不同尺度的微波土壤湿度数据质量。结果表明:DCT-PLS方法能够充分利用三维时空信息对缺失数据进行重建,重建后土壤湿度具有较高质量;10、1 km 2种尺度的土壤湿度与实测土壤湿度在空间分布上较为吻合,2个示例日期中降尺度土壤湿度与TVDI之间的相关性提高了0.352、0.4264,能够更为准确地反映土壤湿度空间分布细节;通过实测数据、TVDI指数的校验,降尺度前后的AMSR2土壤湿度数据表现出了较高的质量与可靠性。     

吉林省长时间序列陆地生态系统NPP时空变化特征

植被净初级生产力(NPP)是陆地生态系统碳循环过程中的重要组成部分,也是评价碳源/汇的重要环节。基于2001—2019年MODIS地表反射率数据(MOD09A1)、气温和日照时间资料,利用光能利用率模型估算了吉林省陆地生态系统NPP,采用趋势分析、空间变异性分析等方法,分析了吉林省陆地生态系统NPP的时空变化特征及其与气候因子的相关性。结果表明:(1)2001—2019年,吉林省植被NPP年均值与总量均成波动上升趋势,辖区内植被NPP总量表现为森林农田草地。(2)2001—2019年NPP分布表现出明显的空间差异性,大部分区域NPP呈现增加趋势,总体变异性稳定,但西北部区域NPP空间变异程度较大,与该地区施行退耕还林和治理草地退化、沙化、盐碱化有一定关联,植被恢复明显。(3)吉林省植被NPP与年均气温、年降水量的相关性存在一定的地域差异性,研究区大部分区域与气温呈正相关关系,小部分区域与降水量呈现出正相关。