Simulation and Analysis on the Influence of Soil Background of Wheat Farmland on NDVI under Different Vegetation Coverage

It is quite confusing to effectively monitor and precisely evaluate growing conditions of wheat by using normalized differential vegetation index (NDVI)which is based on pixel scale as they are significantly different when acquired by the same growth status wheat with different background of soil types.This paper selects 9 typical soil types in our country as background with the wheat canopy spectrum is fixed which means the NDVIc is a constant value to study the influence of different soil background types on NDVI of wheat and analyze the sensitivity of NDVI of wheat to the vegetation coverage simulated by diverse liner mixed ratio of wheat canopy and soil background.The results show that：(1)wheat NDVI of farmland increases along with the increase of vegetation coverage under the same of soil background type,and vice versa;(2)wheat NDVI of farmland vary greatly with different soil background types,and the difference decrease while the vegetation coverage exceed 25%;(3)NDVI sensitivity also shows a quite difference to vegetation coverage under the diverse soil background types.With the increase of vegetation coverage,NDVI sensitivity decreases with the lower\|reflectance soil background while it increases monotonously with the higher reflectance soil background.It provides the foundation for the times of calculating the remote sensing’s NDVI information of all wheat growing periods under different types of soil background.     

New spectral vegetation indices based on the near-infrared shoulder wavelengths for remote detection of grassland phytomass

This article examines the possibility of exploiting ground reflectance in the near-infrared (NIR) for monitoring grassland phytomass on a temporal basis. Three new spectral vegetation indices (infrared slope index, ISI; normalized infrared difference index, NIDI; and normalized difference structural index, NDSI), which are based on the reflectance values in the H25 (863–881 nm) and the H18 (745–751 nm) Chris Proba (mode 5) bands, are proposed. Ground measurements of hyperspectral reflectance and phytomass were made at six grassland sites in the Italian and Austrian mountains using a hand-held spectroradiometer. At full canopy cover, strong saturation was observed for many traditional vegetation indices (normalized difference vegetation index (NDVI), modified simple ratio (MSR), enhanced vegetation index (EVI), enhanced vegetation index 2 (EVI 2), renormalized difference vegetation index (RDVI), wide dynamic range vegetation index (WDRVI)). Conversely, ISI and NDSI were linearly related to grassland phytomass with negligible inter-annual variability. The relationships between both ISI and NDSI and phytomass were however site specific. The WinSail model indicated that this was mostly due to grassland species composition and background reflectance. Further studies are needed to confirm the usefulness of these indices (e.g. using multispectral specific sensors) for monitoring vegetation structural biophysical variables in other ecosystem types and to test these relationships with aircraft and satellite sensors data. For grassland ecosystems, we conclude that ISI and NDSI hold great promise for non-destructively monitoring the temporal variability of grassland phytomass.     

Analysis of the dynamics of African vegetation using the normalized difference vegetation index

Images at a resolution of 8?km are currently being generated for the whole of Africa, displaying the normalized difference vegetation index (NDVI). These images have undergone a process of temporal compositing to reduce the effects of cloud cover and atmospheric variation. When the NDVI is plotted against time, different cover types are shown to have characteristic profiles corresponding closely with their phenology. The resultant pattern of NDVI values displayed on the images is analysed in terms of the cover types present and local variations in rainfall. Comparison between images for 1983 and 1984 overall showed considerable similarities, but significant differences were observed in the northward extent of the greening wave in the Sahel, the greening up of the Kalahari Desert and East African communities. It is concluded that vegetation monitoring using NDVI images needs to be associated with scene stratification according to cover type     

Integration of two spectral indices to monitor loss of moist grasslands within the Jaldapara Wildlife Sanctuary,India

We conducted a study within flood-managed grasslands to evaluate the utility of remotely sensed imagery to evaluate the influence of an altered flooding regime on grassland distribution. Grasslands found along the Torsa River, which flows north to south through the Jaldapara Wildlife Sanctuary in West Bengal, India, provided an excellent test case due to the protected nature of this landscape from intensive management and cultivation. Further, during the 1968 flood season, the Torsa River experienced a major shift in its course from the west side of the sanctuary to east. We used remote-sensing data to identify an efficient method to spectrally monitor changes in grassland distribution. Spectrally normalized multi-temporal (1978, 1990, 2001, 2005) Landsat (MSS, TM, ETM) and ASTER data were used to compare changes in grassland distribution between the current and historic floodplain. A combination of the normalized difference vegetation index (NDVI) and a normalized difference dry index (NDDI) proved very useful in identifying and monitoring grasslands. Given the absence of historic ground data, spectral indices derived from historical satellite imagery also proved valuable as a means to understand temporal dynamics of the distribution of grasslands.     

Climatic and demographic determinants of vegetation cover in northern Cameroon

The objective of the study was to evaluate the spatio-temporal impacts of seasonal rainfall and urban population growth on the variations in normalized difference vegetation index (NDVI) in north Cameroon, which includes climates from south to north, the Sudanese and Sahelian climates. To this end, 48 points of measured rainfall were interpolated based on the kriging method at a spatial resolution of 8 km in accordance with the NOAA-AVHRR NDVI data set. Relationships between rainfall and NDVI, on the one hand, and urban population growth and NDVI, on the other, were analysed considering the 79 administrative units (AUs) in Cameroon. Seasonal (rainy season) variations of the vegetation cover were studied for the period 1987–2002 using the NDVI product at 8 km (NOAA-AVHRR) and 1 km (SPOT-VEGETATION) of spatial resolution. This article emphasizes the importance of the urban signal for the NDVI studies at finer scales, specifically in tropical areas.     

Assessing viewing and illumination geometry effects on the MODIS vegetation index (MOD13Q1) time series: implications for monitoring phenology and disturbances in forest communities in Queensland,Australia

Time series analysis of satellite data can be used to monitor temporal dynamics of forested environments, thus providing spatial data for a range of forest science, monitoring and management issues. The moderate resolution imaging spectroradiometer (MODIS) vegetation index (MOD13Q1) product has potential for monitoring forest dynamics and disturbances. However, the suitability of the product to accurately measure temporal changes due to phenology and disturbances is questionable as the effects of variable solar and viewing geometry have not been removed from these data. This study aimed to examine the impact that viewing and illumination geometry differences had on MOD13Q1 vegetation index values, and their subsequent ability to map changes arising from phenology and disturbances in a number of forest communities in Queensland, Australia. MOD13Q1 normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were compared to normalized NDVI and EVI (NDVI_normalized and EVI_normalized), which were derived from the reflectance modelled from a bidirectional reflectance distribution function (BRDF)/albedo parameters product (MCD43A1) using fixed viewing and illumination geometry. Time series plots of the vegetation index values from a number of pixels representing different forest types and known disturbances showed that the NDVI_normalized time series was more effective at capturing the changes in vegetation than the NDVI. MOD13Q1 NDVI showed higher seasonal amplitude, but was less accurate at capturing phenology and disturbances compared to the NDVI_normalized. The EVI was less affected by variable viewing and illumination geometry in terms of amplitude, but was affected in terms of time shift in periodicities providing erroneous information on phenology. More studies in different environments with appropriate vegetation phenology reference data will be needed to confirm these observations.     

在植物生长周期内县级区域植被指数的时空演变特征分析

采用１９９１至１９９２年晴空时的ＮＯＡＡ卫星ＡＶＨＲＲ资料，计算甘肃省河东地区６０个县（市）作物和牧草生长周期内标准化差植被指数（ＮＤＶＩ）的平均值和标准差，并逐县绘制其时间演变曲线和直方图。选取以农作物、草地和森林草地混合为主的三类县作对比分析，研究县级区域植被指数时空变化与作物和牧草生育期的关系。分析１９９１至１９９２年度冬小麦生长周期内遇到严重干旱的情况，为干旱监测、估产和区分土地使用类型选择最佳时相提供依据     

Biotic controls over spectral reflectance of arctic tundra vegetation

In this study, seasonal field measurements of the normalized difference vegetation index (NDVI), using a field spectroradiometer, and leaf area index (LAI), using a LI‐COR LAI‐2000 Plant Canopy Analyzer, were compared with above‐ground phytomass data to investigate relationships between vegetation properties and spectral indices for four distinct tundra vegetation types at Ivotuk, Alaska (68.49°?N, 155.74°?W). NDVI, LAI and above‐ground phytomass data were collected biweekly from four 100?m×100?m grids, each representative of a different vegetation type, during the 1999 growing season. Shrub phytomass, especially the live foliar deciduous shrub phytomass, was the major factor controlling NDVI across all vegetation types. LAI showed the strongest relationship with the overstorey component (total above‐ground excluding moss and lichen) of phytomass and also showed a significant relationship with NDVI. The results from this study illustrated that time of the growing season in which sampling is conducted, non‐linearity of relationships, and plant composition are important factors to consider when using relationships between NDVI, LAI and phytomass to parameterize or validate ecological models. The relationships established in this study also suggest that NDVI is useful for estimating levels of total live above‐ground phytomass and LAI in tundra vegetation.     

Response of the normalized difference vegetation index to varying cloud conditions in Arctic tundra environments

Frequent cloud cover is a major environmental constraint to optical remote sensing studies in Arctic locations, including studies based on ground or aircraft observations. The objective of this study was to determine how cloud induced variations in solar illumination affect the normalized difference vegetation index (NDVI) of representative vegetation types on the North Slope of Alaska. Illumination conditions were quantified using a cloud index (incident shortwave radiation at the surface divided by radiation at the top of the atmosphere). The results indicated that the NDVI was stable across a wide range of cloud index values, particularly when the value exceeded 0.5. It is concluded that a cloud index threshold may be used to select unbiased NDVI values from a data set collected under varying illumination conditions.     

Feasibility of using Geostationary Ocean Colour Imager (GOCI) data for land applications after atmospheric correction and bidirectional reflectance distribution function modelling

Korea's Geostationary Ocean Colour Imager (GOCI) has very high temporal resolution as well as wide spatial coverage. There is thus great interest in testing its applicability for monitoring land areas in addition to ocean areas. GOCI has eight spectral bands, from blue to near-infrared. These bands can be sensitive to vegetation change, but their wavelength ranges are slightly different from those of the extensively studied Moderate Resolution Imaging Spectroradiometer (MODIS). This study examines whether GOCI data can be applied for land monitoring and how GOCI data should be processed so as to reflect the spectral characteristics of land surfaces as detected by polar-orbit satellite sensors. Several image processing steps were performed for the GOCI data, including atmospheric correction and semi-empirical bidirectional reflectance distribution function modelling, before the results were compared with the MODIS land-surface product. Among the four GOCI normalized difference vegetation index (NDVI) products tested in this study, the GOCI NDVI with viewing-angle-adjusted reflectance showed the best agreement with MODIS NDVI calculated from normalized reflectance, with the lowest root mean square error of 0.126. Additionally, its temporal trends over forest and mixed vegetation areas were similar to those of MODIS NDVI during the study period from September to December.     

Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction

The normalized difference vegetation index (NDVI) is the most widely used vegetation index for retrieval of vegetation canopy biophysical properties. Several studies have investigated the spatial scale dependencies of NDVI and the relationship between NDVI and fractional vegetation cover, but without any consensus on the two issues. The objectives of this paper are to analyze the spatial scale dependencies of NDVI and to analyze the relationship between NDVI and fractional vegetation cover at different resolutions based on linear spectral mixing models. Our results show strong spatial scale dependencies of NDVI over heterogeneous surfaces, indicating that NDVI values at different resolutions may not be comparable. The nonlinearity of NDVI over partially vegetated surfaces becomes prominent with darker soil backgrounds and with presence of shadow. Thus, the NDVI may not be suitable to infer vegetation fraction because of its nonlinearity and scale effects. We found that the scaled difference vegetation index (SDVI), a scale-invariant index based on linear spectral mixing of red and near-infrared reflectances, is a more suitable and robust approach for retrieval of vegetation fraction with remote sensing data, particularly over heterogeneous surfaces. The proposed method was validated with experimental field data, but further validation at the satellite level would be needed.     

The long-term monitoring of vegetation cover in the Amazonian region of northern Brazil using NOAA-AVHRR data

We have analysed monthly composites of normalized difference vegetation index (NDVI) calculated from NOAA's Advanced Very High Resolution Radiometer (AVHRR) for the Amazonian region of northern Brazil across a decade (August 1981 to June 1991) to ascertain if the dominant vegetation types could be differentiated,and to seek inter-annual climatic variation due to changing environmental conditions. The vegetation types observed included dense forest ( submontana and terras baixas ), open forest ( submontana and terras baixas ), transitional forest, seasonal forest ( caatinga ), and two types of savanna ( cerrado ). We found that monthly NDVI composites revealed seasonality in cerrado and especially in caatinga cover types, which can be used in their identification, whilst the phenology of other forest cover types varies little throughout the year. Additionally, yearly composite NDVI values showed a clear and significant reduction ( p 0.95) in dry years, such as those with El Nino Southern Oscillation events. These results indicate the potential use of multi-temporal NDVI data for the environmental characterization and identification of forest ecosystems. Our research found NDVI images from NOAA AVHRR offer a long-term data set that is unequalled for monitoring terrestrial land cover. However, these data have to be used with a degree of caution, especially in regards to atmospheric interference, such as cloud contamination and volcanic eruptions, and post-launch changes in calibration.     

TVDI在冬小麦春季干旱监测中的应用

应用冬小麦春季生长期的NOAA/AVHRR资料,反演归一化植被指数（NDVI）、土壤调整植被指数（SAVI）和下垫面温度（T_s）,分析了植被指数和下垫面温度空间特征,采用温度植被旱情指数（TVDI）,研究了河北省2005年3~5月的冬小麦旱情状况。结果表明：基于SAVI的温度植被旱情指数与土壤表层相对湿度的相关性好于基于NDVI的温度植被旱情指数。通过与气象站土壤水分观测资料进行相关性分析,表明温度植被旱情指数与10 cm土壤相对湿度关系最好,20 cm次之,50 cm较差。因此,基于SAVI的温度植被旱情指数更适于监测冬小麦春季的旱情。     

A comparative evaluation of arid inflow-dependent vegetation maps derived from LANDSAT top-of-atmosphere and surface reflectances

ABSTRACT

In remote sensing, it is commonly accepted that land remote-sensing satellite (LANDSAT) top-of-atmosphere (TOA) reflectance is less accurate than atmospheric correction (AC) reflectance, as the former is not calibrated for possible modifications in the electromagnetic radiation signals due to atmospheric scattering and absorption. This article investigates whether LANDSAT data calibrated for TOA reflectance are an appropriate information source for delineating inflow-dependent vegetation (IDV) in regions with an arid and desert climate, such as the Pilbara region in Western Australia. Knowledge of where IDVs are in the landscape underpins planning their protection and define the baseline for their monitoring when water resource management options are considered. The appropriateness of TOA calibration for the delineation of IDV in the Pilbara was assessed through its comparison with IDV maps derived from AC reflectance. Both radiometric calibration methods (TOA and AC) were applied to a multi-date LANDSAT 5 TM (Thematic Mapper) dataset of 10 images acquired in 2009 and 2010. Two methods based on the application of remote-sensing techniques to identify the extent of temporally invariant vegetation were applied for IDV delineation in the study area. The first method, groundwater-dependent ecosystems mapping (GEM), employs a two-date normalized difference vegetation index (NDVI) dataset for identifying ‘no-change’ clusters of land cover and detecting those related to IDV. The second method applies principal component analysis (PCA) to a multi-date NDVI dataset. The first principal component (PC₁) typically contains features that remain unchanged over time. This includes vegetation with continuous or frequent access to surface and/or groundwater, such as IDV. To delineate the extent of IDV, a thresholding technique was further employed. Spatial similarity between IDV maps produced from TOA and AC reflectance was quantitatively evaluated by the Kappa coefficient. The results showed that TOA and AC IDV maps are in ‘almost perfect’ agreement with the Kappa values above 0.83. This suggests that TOA reflectance is equally appropriate to AC reflectance for mapping in arid and desert climate such as in Pilbara. When the GEM- and PCA-based methods are applied in other study areas with arid or desert climate, the accuracy of the delineated IDV extent may vary. Therefore, the results need to be validated using ground-truth information about known IDV occurrences in the area of interest.     

Comparison of vegetation phenological metrics extracted from GIMMS NDVIg and MERIS MTCI data sets over China

Evaluating vegetation phenology is crucial for a better understanding of the effects of climate change on the terrestrial ecosystem. The scientific community has used various vegetation index data sets from different sensors to quantify vegetation phenology from regional to global scales. The normalized difference vegetation index (NDVI) related to photosynthetic activities is the most widely used index. Recently, a number of published articles have used the Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI) to measure vegetation phenology. MTCI can closely represent the red-edge position (REP). Unlike NDVI, MTCI is more sensitive to high values of chlorophyll content. However, the consistency of vegetation phenological metrics derived from MTCI and NDVI needs to be further explored. This study compared two phenological metrics, i.e. onset of greenness (OG) and end of senescence (ES), extracted from MERIS MTCI data and Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) first generation NDVI (NDVIg) data, which has the longest time records, at nine regions in China from 2003 to 2006. The results showed that the differences of OG and ES vary between different vegetation types, regions, and years, although both NDVI and MTCI time series capture the growth patterns well for most vegetation types. Compared to ES, the OG estimates are more consistent. NDVI yields in general later ES estimates than MTCI.     

Phenology of vegetation in Southern England from Envisat MERIS terrestrial chlorophyll index (MTCI) data

Given the close association between climate change and vegetation response, there is a pressing requirement to monitor the phenology of vegetation and understand further how its metrics vary over space and time. This article explores the use of the Envisat MERIS terrestrial chlorophyll index (MTCI) data set for monitoring vegetation phenology, via its estimates of chlorophyll content. The MTCI was used to construct the phenological profile of and extract key phenological event dates from woodland and grass/heath land in Southern England as these represented a range of chlorophyll contents and different phenological cycles. The period 2003–2008 was selected as this was known to be a period with temperature and phenological anomalies. Comparisons of the MTCI-derived phenology data were made with ground indicators and climatic proxy of phenology and with other vegetation indices: MERIS global vegetation index (MGVI), MODIS normalized difference vegetation index (NDVI) and MODIS enhanced vegetation index (EVI). Close correspondence between MTCI and canopy phenology as indicated by ground observations and climatic proxy was evident. Also observed was a difference between MTCI-derived phenological profile curves and key event dates (e.g. green-up, season length) and those derived from MERIS MGVI, MODIS NDVI and MODIS EVI. The research presented in this article supports the use of the Envisat MTCI for monitoring vegetation phenology, principally due to its sensitivity to canopy chlorophyll content, a vegetation property that is a useful proxy for the canopy physical and chemical alterations associated with phenological change.     

基于NDVI序列影像的植被覆盖变化研究

归一化植被指数NDVI是地表植被覆盖特征的重要指标之一。以新疆石河子地区2003~2006年MODIS遥感数据反演的NDVI时间序列影像为例,分析研究了植被长势的年内和年际变化,将植被长势的年内变化和年际变化分为比前一年(月)好、比前一年(月)稍好、与前一年(月)持平、比前一年(月)稍差和比前一年(月)差5个等级,得到年内和年际间植被长势的动态分布图,从植被长势分布图中NDVI的变化可以看出年际和年内植被长势的变化。并应用变化矢量分析法对2003~2006年石河子地区NDVI的变化强度进行了分析,获得了植被覆盖变化强度分布情况,研究结果表明4 a内石河子地区植被覆盖未发生大的变化,植被系统基本稳定。     

SMAP卫星后向散射系数与海表面风场关系

海面风是海气互相作用的重要参数之一,如何通过雷达后向散射数据有效提取海表面风场信息,对于海洋动力环境遥感监测具有重要的研究意义。使用SMAP卫星L波段真实孔径雷达数据和国家环境预测中心再分析风场数据进行匹配,利用地球物理模型函数分析了SMAP卫星数据的后向散射系数与海表面风场之间的关系, 讨论了不同风速和不同相对风向角时SMAP卫星数据反演海表面风场的潜力。研究显示,水平极化和垂直极化的后向散射系数与风速的关系紧密,适于海表面风场的反演;SMAP卫星数据存在正-侧风不对称现象和逆正-侧风不对称现象;在相对风向角为90°和270°时后向散射系数与风场的关系较为模糊;随着风速的增加,后向散射系数与相对风向角的规律关系也越来越明显,振幅也随风速增大而增大。GMF函数计算的风速偏差为1.19 m/s（水平极化）和1.51 m/s（垂直极化）,均方根误差为1.58 m/s（水平极化）和1.67 m/s（垂直极化）。     

Lowest order correction for solar zenith angle to Global Vegetation Index (GVI) data

Abstract

NOAA produces vegetation indices as part of a project to develop the uses of meteorological satellite data for global agricultural monitoring (Henderson-Sellers et al. 1986). However, no consideration is given to the variability of vegetation indices with the solar zenith angle. This paper focuses on this particular issue. A brief summary of an inversion technique is presented in which raw values of the normalized difference vegetation indices (NDVIs) for a variety of surface-cover types are simulated as a function of solar zenith angle. A relationship between a change in NDVI and solar zenith angle is presented. This relation is used to correct global vegetation index (GVI) data. The results show that for NOAA-7 and NOAA-9 data there is little correction in the neighbourhood of the equator (± 10") but the amount of correction increases with increasing latitude. Such corrections are also shown to be important in data comparison and integration. For example, in comparing the NOAA-6- and NOAA-8-derived NDVI with that derived from NOAA-7 and NOAA-9 for a given date and location the solar zenith angle correction is important.     

Remote sensing for detection and monitoring of vegetation affected by oil spills

This study is aimed at demonstrating the application of vegetation spectral techniques for detection and monitoring of the impact of oil spills on vegetation. Vegetation spectral reflectance from Landsat 8 data were used in the calculation of five vegetation indices (normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), adjusted resistant vegetation index 2 (ARVI2), green-infrared index (G-NIR) and green-shortwave infrared (G-SWIR) from the spill sites (SS) and non-spill sites (NSS) in 2013 (pre-oil spill), 2014 (oil spill date) and 2015 (post-oil spill) for statistical comparison. The result shows that NDVI, SAVI, ARVI2, G-NIR and G-SWIR indicated a certain level of significant difference between vegetation condition at the SS and the NSS in December 2013. In December 2014 vegetation conditions indicated higher level of significant difference between the vegetation at the SS and NSS as follows where NDVI, SAVI and ARVI2 with p-value 0.005, G-NIR – p-value 0.01 and G-SWIR p-value 0.05. Similarly, in January 2015 a very significant difference with p-value <0.005. Three indices NDVI, ARVI2 and G-NIR indicated highly significant difference in vegetation conditions with p-value <0.005 between December 2013 and December 2014 at the same sites. Post-spill analysis shows that NDVI and ARVI2 indicated low level of significance difference p-value <0.05 suggesting subtle change in vegetation conditions between December 2014 and January 2015. This technique may help with the real time detection, response and monitoring of oil spills from pipelines for mitigation of pollution at the affected sites in mangrove forests.