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.     

基于融合NDVI和EVI时间序列的遥感影像分类研究

通过比较分析发现同一地物的NDVI和EVI时间序列变化规律具有一定差异,据此,提出结合NDVI和EVI植被指数时间序列的分类方法,结果表明比单独使用NDVI或EVI时间序列总体精度要提高2.7%,kappa系数提高0.04。     

Temporal and Spatial Distribution of Ulva.prolifera in the Yellow Sea and Influence of Typhoon “CHAN-HOM” in 2015

A recurrent floating green algae bloom was detected in the Yellow Sea since 2007.The Ulva.prolifera is non\|toxic,but the massive accumulations can result in significant environmental damage and cause economic loss to marine industries.In this study,the spatial and temporal patterns of Ulva.prolifera green tides were investigated in the Yellow Sea during 2015 using HJ\|1A/1B and MODIS satellite images by means of NDVI (normalized difference vegetation index)and artificial interpretation.The results showed：(1)A little Ulva.prolifera was discovered firstly in adjacent sea of Yancheng,Jiangsu province in early May with distribution area 0.831 km².Under the action of the southeast monsoon,Ulva.prolifera was gradually drifted to Shandong peninsula waters from south to north.The influential area and range reached a peak value with 1 752.756 km² in late June,and gradually subsided from July to August.And Ulva.prolifera about 38.791 km² was monitored in the South Bay of North Korea.In conclusion,Ulva.prolifera in the Yellow Sea in 2015 has experienced five major processes including “Occur\|Development\|Outbreak\|Recession\|Disappeared”.(2)Typhoon "CHAN\|HOM" certainly influenced the northward pathway of Ulva.prolifera and shifted towards the southwest,resulting in most of Ulva.prolifera moved to the east coast of Lianyungang,and speculated that minority Ulva.prolifera drifted to the South Bay of North Korea.(3)From the monitoring data,the spatial resolution between MODIS and ENVISAT (HJ\|1A / 1B)is difference significantly,250 m and 30 m respectively.A functional relation of the two data with monitoring area difference about 2.26 times was established to make up for the shortage of the environmental satellite (HJ\|1A/1B)images.      

不同大气校正方法对中小湖泊蓝藻遥感动态监测的影响

环境小卫星可实现中小湖泊蓝藻动态监测,但不同大气校正方法对于相同影像的处理结果有很大差异。研究利用多种大气校正方法对环境小卫星影像进行辐射校正处理,利用多个感兴趣区的全局和局部特征以及多个统计量对处理结果进行分析,比较其在蓝藻动态监测中的作用。基于多光谱植被指数计算蓝藻生物量的思路,分析了影像经不同大气校正算法处理后,其归一化植被指数的差异性来源及其对蓝藻生物量计算结果的影响。结果表明:蓝藻动态监测的定量描述会因大气校正算法不同而不一致,进而对几种大气校正算法在蓝藻生物量监测和定量分析中所产生的不确定性进行了比较分析,并对各算法的有效利用提出了建议。     

Hierarchical Land Cover Classification based on MODIS NDVI Time-series

In this paper,we mainly used MODIS NDVI time-series dataset at 16-days temporal resolution and 250-meters spatial resolution to analyze land cover mapping of northeastern China.We used two different filter methods to fit NDVI time-series dataset,and compared their average classes’ separability based on Jeffries-Matusita distance index.In addition,we made use of hierarchical classification method to complete classification,combined with short-wave infrared spectral reflectance data and DEM.We conformed to the principle that separate area hierarchically into several parts first and then classify each part further,and use a single characteristic band first and then multiple feature bands.In the process of classification,we adopted threshold value method,support vector machine,artificial net neural and C5.0 decision tree classification to distinguish each land-cover type hierarchically.Finally,we evaluated the accuracy of the final classification of study area using known land-cover classification data and high-resolution remote sensing imagery,overall accuracy is 84.61%,Kappa coefficient is 0.8262.     

Air temperature estimation with MSG-SEVIRI data: Calibration and validation of the TVX algorithm for the Iberian Peninsula

Air temperature can be estimated from remote sensing by combining information in thermal infrared and optical wavelengths. The empirical TVX algorithm is based on an estimated linear relationship between observed Land Surface Temperature (LST) and a Spectral Vegetation Index (NDVI). Air temperature is assumed to be equal to the LST corresponding to the effective full vegetation cover, and is found by extrapolating the line to a maximum value of NDVI_max. The algorithm has been tested and reported in the literature previously. However, the effect of vegetation types and climates and the potential variation in NDVI of the effective full cover has not been subject for investigation. The present study proposes a novel methodology to estimate NDVI_max that uses observed air temperature to calibrate the NDVI_max for each vegetation type. To assess the validity of this methodology, we have compared the accuracy of estimates using the new NDVI_max and the previous NDVI_max that have been proposed in literature with MSG-SEVIRI images in Spain during the year 2005. In addition, a spatio-temporal assessment of residuals has been performed to evaluate the accuracy of retrievals in terms of daily and seasonal variation, land cover, landscape heterogeneity and topography. Results showed that the new calibrated NDVI_max perform well, with a Mean Absolute Error ranging between 2.8 °C and 4 °C. In addition, vegetation-specific NDVI_max improve the accuracy compared with a unique NDVI_max.     

The effect of the temporal resolution of NDVI data on season onset dates and trends across Canadian broadleaf forests

Satellite remote sensing has the potential to contribute to plant phenology monitoring at spatial and temporal scales relevant for regional and global scale studies. Historically, temporal composites of satellite data, ranging from 8 days to 16 days, have been used as a starting point for satellite-derived phenology data sets. In this study we assess how the temporal resolution of such composites affects the estimation of the start of season (SOS) by: 1) calibrating a relationship between satellite derived SOS with in situ leaf unfolding (LU) of trembling aspen (Populus tremuloides) across Canada and 2) quantifying the sensitivity of calibrated satellite SOS estimates and trends, over Canadian broadleaf forests, to the temporal resolution of NDVI data. SOS estimates and trends derived from daily NDVI data were compared to SOS estimates and trends derived from multiday NDVI composites that retain the exact date of the maximum NDVI value or that assume the midpoint of the multiday interval as the observation date. In situ observations of LU dates were acquired from the PlantWatch Canada network. A new Canadian database of cloud and snow screened daily 1-km resolution National Oceanic and Atmospheric Administration advanced very high resolution radiometer surface reflectance images was used as input satellite data. The mean absolute errors of SOS dates with respect to in situ LU dates ranged between 13 and 40 days. SOS estimates from NDVI composites that retain the exact date of the maximum NDVI value had smaller errors (~ 13 to 20 days). The sensitivity analysis reinforced these findings: SOS estimates from NDVI composites that use the exact date had smaller absolute deviations from the LU date (0 to − 5 days) than the SOS estimates from NDVI composites that use the midpoint (− 2 to − 27 days). The SOS trends between 1985 and 2007 were not sensitive to the temporal resolution or compositing methods. However, SOS trends at individual ecozones showed significant differences with the SOS trends from daily NDVI data (Taiga plains and the Pacific maritime zones). Overall, our results suggest that satellite based estimates of vegetation green-up dates should preferably use sub-sampled NDVI composites that include the exact observation date of the maximum NDVI to minimize errors in both, SOS estimates and SOS trend analyses. For trend analyses alone, any of the compositing methods could be used, preferably with composite intervals of less than 28 days. This is an important finding, as it suggests that existing long-term 10-day or 15-day NDVI composites could be used for SOS trend analyses over broadleaf forests in Canada or similar areas. Future studies will take advantage of the growing in situ phenology networks to improve the validation of satellite derived green-up dates.     

Analysis of monotonic greening and browning trends from global NDVI time-series

Remotely sensed vegetation indices are widely used to detect greening and browning trends; especially the global coverage of time-series normalized difference vegetation index (NDVI) data which are available from 1981. Seasonality and serial auto-correlation in the data have previously been dealt with by integrating the data to annual values; as an alternative to reducing the temporal resolution, we apply harmonic analyses and non-parametric trend tests to the GIMMS NDVI dataset (1981-2006). Using the complete dataset, greening and browning trends were analyzed using a linear model corrected for seasonality by subtracting the seasonal component, and a seasonal non-parametric model. In a third approach, phenological shift and variation in length of growing season were accounted for by analyzing the time-series using vegetation development stages rather than calendar days. Results differed substantially between the models, even though the input data were the same. Prominent regional greening trends identified by several other studies were confirmed but the models were inconsistent in areas with weak trends. The linear model using data corrected for seasonality showed similar trend slopes to those described in previous work using linear models on yearly mean values. The non-parametric models demonstrated the significant influence of variations in phenology; accounting for these variations should yield more robust trend analyses and better understanding of vegetation trends.     

多时机NOAA—AVHRR数据主成分分析的生物学意义

利用多时上NOAA－AVHRR的中国归一化植被指数NDVI数据进行主成分分析,并与从NDVI派生的4个生物不数作相关分析,结果表明：主成分变换既压缩了信息,将21个月的信息主要压缩到前4个主分量,又提取了关键的变化信息,第一主分量反映基本植被覆信息,第二、第三和第四主分量反映植被季相变化信息,正是由于一年12个月的NDVI曲线反映了植被季相变化特征,使得主成分变换得到的各主分量具有一定的生物学意义,而且17种中国典型植被在这4个主分量图像上存在一定的差异性,使其具有进行较高精度土地覆盖分类的潜力。     

HJ-1/CCD地表反照率估算及其与NDVI关系分析

参照TM地表反照率反演算法,建立了适用于HJ-1/CCD传感器估算地表反照率的算法。通过应用6S辐射传输模型建立查找表,对覆盖内蒙古自治区锡林浩特市的HJ-1/CCD数据进行大气校正,并根据反照率定义,回归分析得到可见光波段地表反照率;通过与地面实测数据进行对比分析,表明该算法估算得到的地表反照率精度较高,其最大相对误差为14.32%。同时,本文将估算结果与同时期NDVI进行拟合分析,得出地表反照率与NDVI存在较高的负相关关系。