Spectral response of a plant canopy with different soil backgrounds

The spectral behavior of a cotton canopy with four soil types alternately inserted underneath was examined at various levels of vegetation density. Measured composite spectra, representing various mixtures of vegetation with different soil backgrounds, were compared with existing measures of greenness, including the NIR-red band ratios, the perpendicular vegetation index (PVI), and the greenness vegetation index (GVI). Observed spectral patterns involving constant vegetation amounts with different soil backgrounds could not be explained nor predicted by either the ratio or the orthogonal greenness measures. All greenness measures were found to be strongly dependent on soil brightness. Furthermore, soil-induced greenness changes became greater with increasing amounts of vegetation up to 60% green cover. The results presented suggests that soil and plant spectra interactively mix in a nonadditive, partly correlated manner to produce composite canopy spectra.     

浅析遥感光谱特征参量的原理及基本方法

概述了导数光谱、红边参数、光谱吸收特征以及光谱反射特征等遥感光谱特征参量的原理及基本方法,总结和分析了这些参量在植被领域中的应用动态,提出了遥感技术存在的问题及其应用展望,遥感光谱特征参量能够为植被理化信息的提取提供强有力的工具。     

Spectral reflectance of coral reef bottom-types worldwide and implications for coral reef remote sensing

Coral reef benthic communities are mosaics of individual bottom-types that are distinguished by their taxonomic composition and functional roles in the ecosystem. Knowledge of community structure is essential to understanding many reef processes. To develop techniques for identification and mapping of reef bottom-types using remote sensing, we measured 13,100 in situ optical reflectance spectra (400-700 nm, 1-nm intervals) of 12 basic reef bottom-types in the Atlantic, Pacific, and Indian Oceans: fleshy (1) brown, (2) green, and (3) red algae; non-fleshy (4) encrusting calcareous and (5) turf algae; (6) bleached, (7) blue, and (8) brown hermatypic coral; (9) soft/gorgonian coral; (10) seagrass; (11) terrigenous mud; and (12) carbonate sand. Each bottom-type exhibits characteristic spectral reflectance features that are conservative across biogeographic regions. Most notable are the brightness of carbonate sand and local extrema near 570 nm in blue (minimum) and brown (maximum) corals. Classification function analyses for the 12 bottom-types achieve mean accuracies of 83%, 76%, and 71% for full-spectrum data (301-wavelength), 52-wavelength, and 14-wavelength subsets, respectively. The distinguishing spectral features for the 12 bottom-types exist in well-defined, narrow (10-20 nm) wavelength ranges and are ubiquitous throughout the world. We reason that spectral reflectance features arise primarily as a result of spectral absorption processes. Radiative transfer modeling shows that in typically clear coral reef waters, dark substrates such as corals have a depth-of-detection limit on the order of 10-20 m. Our results provide the foundation for design of a sensor with the purpose of assessing the global status of coral reefs.     

滏阳河两岸农田土壤Fe、Zn、Se元素光谱响应研究

为了探索遥感技术快速定量化监测土壤元素含量的可行性，本通过对滏阳河两岸农田51个土壤表层样品的室内光谱反射率及其Fe、Zn、Se含量关系的研究，探索了反射光谱快速预测土壤元素含量的技术途径。结果发现预测Fe的最佳光谱间隔为16nm。Zn和Se的为8nm，这说明在使用经验方法预测没有光谱特征的成分时，光谱分辨率不是一个必要条件；土壤中的Fe、Zn、Se元素与土壤的反射光谱存在较好的相关性，各元素含量与土壤平均反射率负复相关系数(R^2)均可达到0．49以上，而与相应TM各波段的平均光谱反射率也都具有较好的负相关关系，与TM7波段的复相关系数最大，Fe、Zn为0．58，Se元素为0．550本研究结果为今后利用高光谱遥感技术定量监测土壤Fe、Zn、Se元素含量提供了一种新的方法和技术途径，对土地质量变化的快速定量监测具有重要的科学意义和应用前景。     

1982～2000年黄淮海地区植被覆盖变化特征分析

利用1982～2000年8km NOAA-AVHRR数据,采用均值法、差值法和一元线性回归模拟法,分析了中国黄淮海地区植被的动态变化及其空间分布特征,模拟了年NDVI均值的变化趋势,并对不同植被类型的NDVI年内和年际变化规律进行了分析。结果表明:黄淮海地区植被覆盖总体上呈增加趋势,20世纪90年代末相对80年代初平均NDVI值增加了近0.03。从空间分布上看,大部分地区NDVI都呈增加的趋势,其中NDVI极显著增加的区域主要分布在山东的西北部和西部、河南的东部、河北的北部及江苏的北部地区,F检验的显著性水平达到了99%。NDVI呈减少趋势的地区很少,主要分布在北京、天津、山西中部。各植被类型的NDVI在年内的变化呈很强的季节性,年际变化规律大致相同,呈波动上升的趋势。     

南方丘陵区植被覆盖度遥感估算与时空变化研究——以福建省永定县为例

植被覆盖度是生态环境监测的重要指标,而复杂地形因素影响对山地植被遥感信息准确提取。基于Landsat-8OLI遥感数据,分别采用像元二分模型和线性混合光谱分解法,在对比分析植被覆盖度的地形敏感性基础上,选择山地植被指数(NDMVI)估算了1992、2002和2014年永定县的植被覆盖度,并分析其变化。结果表明:1基于山地植被指数(NDMVI)的覆盖度估算模型的地形敏感性最弱,更适合于南方丘陵山地的植被覆盖度遥感反演;2永定县总体植被覆盖度较高,平均植被覆盖度达77.99%以上,高覆盖度区占59.73%以上,22年内植被覆盖度经历了先提高再下降的过程;3在空间上,高坎抚、金丰和西部片区的植被覆盖度较低,动态变化较明显。永定县金丰片区植被覆盖度明显提高;而近12年内高坎抚片区因矿业开采活动对生态环境的破坏,植被覆盖度降低幅度大,且变化面积较大。     

Sharpened Mapping of Tropical Forest Biophysical Properties from Coarse Spatial Resolution Satellite Sensor Data

Forest biophysical properties are typically estimated and mapped from remotely sensed data through the application of a vegetation index. This generally does not make full use of the information content of the remotely sensed data, using only the data acquired in a limited number of spectral channels, and may provide a relatively crude spatial representation of the biophysical variable of interest. Using imagery acquired by the NOAA AVHRR, it is shown that a standard neural network may use all the spectral channels available in a remotely sensed data set to derive more accurate estimates of the biophysical properties of tropical forests in Ghana than a series of vegetation indices. Additionally, the spatial representation derived can be refined by fusion with finer spatial resolution imagery, achieved with the application of a further neural network.     

宁夏主要作物霜冻遥感监测研究

利用1991－1990年NOAA卫星资料,宁夏各台站温度资料和霜冻调查资料,在分析宁夏霜冻发生规律和特点的基础上,利用植被指数差值（DVI）法（间接法）,温度指法（TR）法（直接法）和冷谷面积法（ACV）监测宁夏不同类型的霜冻,取得了良好效果。     

基于ASTER数据黑河中游植被含水量反演研究

植被含水量是影响植物生长的主要限制因子之一,也是衡量植被生理状态和形态结构的重要参数。应用遥感技术定量估测植被含水量,对于农业旱情监测、作物产量估计和科学研究具有重要意义。基于2012年黑河生态水文遥感试验期间获得的6景ASTER遥感数据和同步观测的研究区生物量观测数据集,选取NDVI、RVI、SAVI和MSAVI 4种植被指数分别与单位面积内植被含水量的关系进行比较分析,建立了不同植被指数的植被含水量反演模型,并对反演结果进行了验证。研究结果表明:4种植被指数均与实测的植被含水量有较高的相关性(R20.846),利用MSAVI反演的植被含水量精度略优于其他3种指数,其均方根误差(RMSE)在0.794kg/m2内。模型较为可靠,可以为大范围获取植被含水量信息提供有效方法。     

Analysis of teleconnections between AVHRR-based sea surface temperature and vegetation productivity in the semi-arid Sahel

Vegetation productivity across the Sahel is known to be affected by a variety of global sea surface temperature (SST) patterns. Often climate indices are used to relate Sahelian vegetation variability to large-scale ocean-atmosphere phenomena. However, previous research findings reporting on the Sahelian vegetation response to climate indices have been inconsistent and contradictory, which could partly be caused by the variations in spatial extent/definitions of climate indices and size of the region studied. The aim of this study was to analyze the linkage between climate indices, pixel-wise spatio-temporal patterns of global sea surface temperature and the Sahelian vegetation dynamics for 1982-2007. We stratified the Sahel into five subregions to account for the longitudinal variability in rainfall. We found significant correlations between climate indices and the Normalized Difference Vegetation Index (NDVI) in the Sahel, however with different magnitudes in terms of strength for the western, central and eastern Sahel. Also the correlations based on NDVI and global SST anomalies revealed the same East-West gradient, with a stronger association for the western than the eastern Sahel. Warmer than average SSTs throughout the Mediterranean basin seem to be associated with enhanced greenness over the central Sahel whereas colder than average SSTs in the Pacific and warmer than average SSTs in the eastern Atlantic were related to increased greenness in the most western Sahel. Accordingly, we achieved high correlations for SSTs of oceanic basins which are geographically associated to the climate indices yet by far not always these patterns were coherent. The detected SST-NDVI patterns could provide the basis to develop new means for improved forecasts in particular of the western Sahelian vegetation productivity.     

Correction of the Soil Adjusted Factors of Vegetation Indices for Monitoring Rice Leaf Index Zrea

The vegetation indices that take the soil adjustment factor into consideration can reduce the influence of soil background conditions and have been widely used in monitoring all kinds of vegetation.However,the rice has been planted in the soil covered by a certain thickness of layer of water,which is different with other various soil backgrounds.Therefore,in this paper,through two years of rice plot experiments,we obtained the rice canopy spectral data and the corresponding leaf area index (LAI) data,and then calculated a series of vegetation indices (EVI,SAVI,WDVI) by using different soil adjustment factors changing within a certain range.We compared the abilities of these vegetation indices for rice LAI estimation,and then determine the optimum soil adjustment factors of vegetation indices to adjust the background of rice.In the study,we found that the best soil adjustment factor L for EVI,L of SAVI,a of WDVI are 0.25,0.10 and 1.25 respectively,and we further compared the LAI estimation results of the best soil adjustment factor with those of the conventional soil adjustment factor.For the model taking EVI as an independent variable,the RMSE of LAI estimation using the best soil adjustment factor is 6.82 % lower than that using the conventional soil adjustment factor;In SAVI model,the RMSE using the best soil adjustment factor is 10.23% lower than that using the conventional soil adjustment factor .These results indicate that the corrected vegetation indices considering the background of rice can improve the accuracy of rice leaf area index using remotely sensed data.     

三峡库区植被生物量遥感估算方法研究

利用遥感手段估算三峡库区植被的生物量,综合野外观测数据,分析了Landsat TM数据的光谱信息与植被生物量的关系,通过相关性分析表明Landsat TM多光谱数据能较好地反映该地区的植被生物量水平。然后,利用Landsat TM数据分别建立了针对三峡库区的阔叶林、针叶林、针阔混交林、灌木林和草本植被5种主要植被类型的生物量遥感估算模型,并利用所得模型计算了该区域2002年植被的地上生物量总量为1.05×10⁸ Mg。
     

Remotely Sensed Retrieving the Surface Soil Moisture of Yan’an Urban Forest based on Landsat Image and Trapezoid Method

It is crucial for soil moisture assessment to know the prediction accuracy of inversion model. Urban forest surface soil in a gully-loess region (Yan’an), was taken as the research object, and the three scenes of Landsat satellite remotely sensed imagery in different periods and soil moisture sensor in situ measurement data were used as the data source. The parameters of TOTRAM (Thermal-Optical TRApezoid Model) and OPTRAM (OPtical TRApezoid Model) were obtained through the scatter diagram of pixels in two-dimensional spaces (LST-NDVI and STR-NDVI, LSTis land surface temperature,NDVIis normalized vegetation index, and STR is shortwave infrared conversion reflection coefficient) and their fitting dry edge and wet edge, respectively. Then, the w values (soil moisture in percentage) of Yan’an urban forest were retrieved at the pixel level (30 m by 30 m), the accuracy of the two models was verified, the differences between the estimated results of the two models, and the influence of linear and nonlinear edge on the inversion results were compared. The results indicate that: (1) Except that the dry edge and wet edge of OPTRAM models on Landsat 7 and Landsat 8 were non-linear, the other dry and wet edges of pixels in LST-NDVI space and STR-NDVI space are almost linear and enveloped into a trapezoid shape. (2) Compared with the field measurement data, the mean error (ME) of TOTRAM and OPTRAM were 0.009 and 0.045 5, respectively, which indicating that the estimation results of both models were relatively high, but the root mean square error (RMSE) of the OPTRAM model was closer to zero than the TOTRAM model. The value of w estimated by the OPTRAM model is evenly distributed on both sides of the 1∶1 reference line, and the number of points on the reference line is more than that of the TOTRAM model in scatterplots, indicating that the accuracy of OPTRAM is higher than that of the TOTRAM model, moreover, the inversion precision of nonlinear edge is higher than that of linear edge. Thus, in further research, the relationship between the complexity of the dry edge and wet edge and the model’s accuracy improvement should be discussed in the OPTRAM model, and the influences of surrounding environment, rainfall, forest disturbance and NDVI saturation on the estimation accuracy of the two models need to be considered.     

Multi-angle remote sensing of forest light use efficiency by observing PRI variation with canopy shadow fraction

We show that observed co-variations at sub-hourly time scales between the photochemical reflectance index (PRI) and canopy light use efficiency (LUE) over a Douglas-fir forest result directly from sub-hourly leaf reflectance changes in a 531 nm spectral window roughly 50 nm wide. We conclude then, that over a forest stand we are observing the direct effects of photosynthetic down-regulation on leaf-level reflectance at 531 nm. Key to our conclusion is our ability to simultaneously measure the LUE and reflectance of the Douglas-fir stand as a function of shadow fraction from the “hot spot” to the "dark spot"dark spot” and a new finding herein, based on radiative transfer theory, that the magnitude of a normalized reflectance difference index (NDRI) such as PRI can vary with shadow fraction only in case the reflectance of the shaded and sunlit leaves differ in at least one of the NDRI bands.Our spectrometer measurements over a nearly 6 month period show that at a forest stand scale, only two NDRIs (both containing a band near 570 nm) vary with shadow fraction and are correlated with LUE; an NDRI with a band centered at 531 nm roughly 50 nm wide, and another near 705 nm. Therefore, we are able to conclude that only these two bands' reflectance differ between the sunlit and the shaded elements of the canopy. Their reflectance changes on time scales of a few minutes or less. Our observations also show that the reflectance changes at 531 nm are more highly correlated with variations in canopy light use efficiency when only sunlit canopy elements are viewed (the hot spot), than when only shaded elements (the dark spot) are viewed. Taken together then, these results demonstrate that the observed sub-hourly changes in foliage reflectance at 531 nm and 705 nm can only result from corresponding variations in photosynthetic rates.The importance of our results are as follows: (1) We show that variations in PRI with LUE are a direct result of rapid changes in foliage reflectance at 531 nm resulting from photosynthetic down-regulation, and can be observed at forest scales. (2) Our findings also suggest a new sensor and methodology for the direct retrieval from space of changes in forest LUE by measuring PRI as a function of shadow fraction using a multi-angle spectrometer simultaneously retrieving both shadow fraction and PRI.     

Optimal interpolation analysis of leaf area index using MODIS data

A simple data analysis technique for vegetation leaf area index (LAI) using Moderate Resolution Imaging Spectroradiometer (MODIS) data is presented. The objective is to generate LAI data that is appropriate for numerical weather prediction. A series of techniques and procedures which includes data quality control, time-series data smoothing, and simple data analysis is applied. The LAI analysis is an optimal combination of the MODIS observations and derived climatology, depending on their associated errors σ_o and σ_c. The “best estimate” LAI is derived from a simple three-point smoothing technique combined with a selection of maximum LAI (after data quality control) values to ensure a higher quality. The LAI climatology is a time smoothed mean value of the “best estimate” LAI during the years of 2002-2004. The observation error is obtained by comparing the MODIS observed LAI with the “best estimate” of the LAI, and the climatological error is obtained by comparing the “best estimate” of LAI with the climatological LAI value. The LAI analysis is the result of a weighting between these two errors. Demonstration of the method described in this paper is presented for the 15-km grid of Meteorological Service of Canada (MSC)'s regional version of the numerical weather prediction model. The final LAI analyses have a relatively smooth temporal evolution, which makes them more appropriate for environmental prediction than the original MODIS LAI observation data. They are also more realistic than the LAI data currently used operationally at the MSC which is based on land-cover databases.     

地表通量对模型参数的不确定性和敏感性分析

基于2007年12月22日~2009年12月31日黑河流域阿柔冻融观测站的气象驱动数据,利用通用陆面模型(Common Land Model,CoLM)模拟的地表通量结果,研究地表通量对模型参数(叶面积指数、地表反照率和植被覆盖度)的不确定性与敏感性。结果表明,叶面积指数、地表反照率和植被覆盖度对地表感热和潜热通量不同组分的影响存在较大的差异。其中,植被层的感热和潜热通量对叶面积指数的敏感性程度较高,敏感系数均达到0.7以上;与潜热通量相比,感热通量对反照率更加敏感,土壤感热、植被感热和总感热通量对反照率的敏感系数分别达到-0.96、-0.97和-0.66,而土壤潜热和总潜热通量对地表反照率的敏感系数仅为0.1左右;植被潜热通量对植被覆盖度的敏感性程度很高,敏感系数范围为0.92~0.96,而土壤感热通量对植被覆盖度最不敏感,敏感系数只有0.18左右。     

Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data

Accurate estimates of vegetation biophysical variables are valuable as input to models describing the exchange of carbon dioxide and energy between the land surface and the atmosphere and important for a wide range of applications related to vegetation monitoring, weather prediction, and climate change. The present study explores the benefits of combining vegetation index and physically based approaches for the spatial and temporal mapping of green leaf area index (LAI), total chlorophyll content (TC_ab), and total vegetation water content (VWC). A numerical optimization method was employed for the inversion of a canopy reflectance model using Terra and Aqua MODIS multi-spectral, multi-temporal, and multi-angle reflectance observations to aid the determination of vegetation-specific physiological and structural canopy parameters. Land cover and site-specific inversion modeling was applied to a restricted number of pixels to build multiple species- and environmentally dependent formulations relating the three biophysical properties of interest to a number of selected simpler spectral vegetation indices (VI). While inversions generally are computationally slow, the coupling with the simple and computationally efficient VI approach makes the combined retrieval scheme for LAI, TC_ab, and VWC suitable for large-scale mapping operations. In order to facilitate application of the canopy reflectance model to heterogeneous forested areas, a simple correction scheme was elaborated, which was found to improve forest LAI predictions significantly and also provided more realistic values of leaf chlorophyll contents.The inversion scheme was designed to enable biophysical parameter retrievals for land cover classes characterized by contrasting canopy architectures, leaf inclination angles, and leaf biochemical constituents without utilizing calibration measurements. Preliminary LAI validation results for the Island of Zealand, Denmark (57°N, 12°E) provided confidence in the approach with root mean square (RMS) deviations between estimates and in-situ measurements of 0.62, 0.46, and 0.63 for barley, wheat, and deciduous forest sites, respectively. Despite the independence on site-specific in-situ measurements, the RMS deviations of the automated approach are in the same range as those established in other studies employing field-based empirical calibration.Being completely automated and image-based and independent on extensive and impractical surface measurements, the retrieval scheme has potential for operational use and can quite easily be implemented for other regions. More validation studies are needed to evaluate the usefulness and limitations of the approach for other environments and species compositions.     

Predicting satellite-derived patterns of large-scale disturbances in forests of the Pacific Northwest Region in response to recent climatic variation

Across the Pacific Northwest, the climate between 1950 and 1975 was exceptionally cool and wet compared with more recent conditions (1995-2005). We reasoned that the changes in climate could result in expanded outbreaks of insects, diseases, and fire. To test this premise, we first modeled monthly variation in photosynthesis and growth of the most widely distributed species, Douglas-fir (Pseudotsuga menziesii), using a process-based model (3-PG) for the two periods. To compare with remotely sensed variables, we converted modeled growth potential into maximum leaf area index (LAImax), which was predicted to range from 1 to 9 across the region. On most sites, varying soil moisture storage capacity (θcap) from 200 to 300 mm while holding soil fertility constant, made slight but insignificant difference in simulated LAImax patterns. Both values of θcap correlated well with LAI estimates acquired from NASA's MODIS satellites in June, 2005 (r² = 0.7). To evaluate where 15 coniferous tree species might be prone to wide-scale disturbance, we used climatically-driven decision-tree models, calibrated in the 1950-1975 period, to identify vulnerable areas in 1995-2005. We stratified predictions within 34 recognized ecoregions and compared these results with large-scale disturbances recorded on MODIS imagery acquired between 2005 and 2009. The correlation between the percent of species judged as vulnerable within each ecoregion and the percent of forested areas recorded as disturbed with a MODIS-derived Global Disturbance Index was linear and accounted for 65 to 73% of the observed variation, depending on whether or not disturbance by fire was excluded from the analysis. Based on climate projections through the rest of the rest of the 21st century, we expect continued high levels of disturbance in ecoregions located beyond the climatically buffering influence of the Pacific Ocean.     

Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors: Extension to AVHRR NOAA-17, 18 and METOP-A

This work extends the previous study of Trishchenko et al. [Trishchenko, A. P., Cihlar, J., & Li, Z. (2002). Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors. Remote Sensing of Environment 81 (1), 1-18] that analyzed the spectral response function (SRF) effect for the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA satellites NOAA-6 to NOAA-16 as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), the VEGETATION sensor (VGT) and the Global Imager (GLI). The developed approach is now applied to cover three new AVHRR sensors launched in recent years on NOAA-17, 18, and METOP-A platforms. As in the previous study, the results are provided relative to the reference sensor AVHRR NOAA-9. The differences in reflectance among these three radiometers relative to the AVHRR NOAA-9 are similar to each other and range from − 0.015 to 0.015 (− 20% to + 2% relative) for visible (red) channel, and from − 0.03 to 0.02 (− 5% to 5%) for the near infrared (NIR) channel. The absolute change in the Normalized Difference Vegetation Index (NDVI) ranged from − 0.03 to + 0.06. Due to systematic biases of the visible channels toward smaller values and the NIR channels toward slightly larger values, the overall systematic biases for NDVI are positive. The polynomial approximations are provided for the bulk spectral correction with respect to the AVHRR NOAA-9 for consistency with previous study. Analysis was also conducted for the SRF effect only among the AVHRR-3 type of radiometer on NOAA-15, 16, 17, 18 and METOP-A using AVHRR NOAA-18 as a reference. The results show more consistency between sensors with typical correction being under 5% (or 0.01 in absolute values). The AVHRR METOP-A reveals the most different behavior among the AVHRR-3 group with generally positive bias for visible channel (up to + 5%, relative), slightly negative bias for the NIR channel (1%-2% relative), and negative NDVI bias (− 0.02 to + 0.005). Polynomial corrections are also suggested for normalization of AVHRR on NOAA-15, 16, 17 and METOP-A to AVHRR NOAA-18.