Extracting Regional Pattern of Wheat Sowing Dates Using Multispectral and High Temporal Observations from Indian Geostationary Satellite

Monitoring of Agricultural crops using remote sensing data is an emerging tool in recent years. Spatial determination of sowing date is an important input of any crop model. Geostationary satellite has the capability to provide data at high temporal interval to monitor vegetation throughout the entire growth period. A study was conducted to estimate the sowing date of wheat crop in major wheat growing states viz. Punjab, Haryana, Uttar Pradesh (UP), Madhya Pradesh (MP), Rajasthan and Bihar. Data acquired by Charged Couple Detector (CCD) onboard Indian geostationary satellite INSAT 3A have continental (Asia) coverage at 1 km?×?1 km spatial resolution in optical spectral bands with high temporal frequency. Daily operational Normalized Difference Vegetation Index (NDVI) product from INSAT 3A CCD available through Meteorological and Oceanographic Satellite Data Archival Centre (MOSDAC) was used to estimate sowing date of wheat crop in selected six states. Daily NDVI data acquired from September 1, 2010 to December 31, 2010 were used in this study. A composite of 7 days was prepared for further analysis of temporal profile of NDVI. Spatial wheat crop map derived from AWiFS (56 m) were re-sampled at INSAT 3A CCD parent resolution and applied over each 7 day composite. The characteristic temporal profiles of 7 day NDVI composite was used to determine sowing date. NDVI profile showed decreasing trend during maturity of kharif crop, minimum value after harvest and increasing trend after emergence of wheat crop. A mathematical model was made to capture the persistent positive slope of NDVI profile after an inflection point. The change in behavior of NDVI profile was detected on the basis of change in NDVI threshold of 0.3 and sowing date was estimated for wheat crop in six states. Seven days has been deducted after it reached to threshold value with persistent positive slope to get sowing date. The clear distinction between early sowing and late sowing regions was observed in study area. Variation of sowing date was observed ranging from November 1 to December 20. The estimated sowing date was validated with the reported sowing date for the known wheat crop regions. The RMSD of 3.2 (n?=?45) has been observed for wheat sowing date. This methodology can also be applied over different crops with the availability of crop maps.     

Albedo-rainfall Feedback Over Indian Monsoon Region Using Long Term Observations Between 1981 to 2000

Albedo determines radiation balance of land (soil-canopy complex) surface and influence boundary layer structure of the atmosphere. Accurate surface albedo determination is important for weather forecasting, climate projection and ecosystem modelling. Albedo-rainfall feedback relationship has not been studied so far using observations on spatial scale over Indian monsoon region due to lack of consistent, systematic and simultaneous long-term measurements of both. The present study used dekadal (ten-day) composite of satellite (e.g. NOAA) based Pathfinder AVHRR Land (PAL) datasets between 1981 and 2000 over India (68–100°E, 5–40°N) at 8 km spatial resolution. Land surface albedo was computed using linear transformation of red and near infrared (NIR) surface reflectances. The cloud effects were removed using a smoothening filter with harmonic analysis applied to time series data in each year. The monthly, annual and long term means were computed from dekadal reconstructed albedo. The mean per year and coefficient of variation (CV) of surface albedo over seventeen years, averaged over Indian land region, were found to show a significantly decreasing (0.15 to 0.14 and 60 to 40%, respectively) trend between 1981 and 2000. Among all the land use patterns, the inter-annual variation of albedo of Himalayan snow cover showed a significant and the steepest reducing trend (0.42 – 0.35) followed by open shurbland, grassland and cropland. No significant change was noticed over different forest types.. This could be due to increase in snow melting period and snow melt area. A strong inverse exponential relation (correlation coefficient r = 0.95, n = 100) was found between annual rainfall and annual albedo over seven rainfall zones. The decreasing trend in snow-albedo of accumulation period (September to March) follows the declining trend in measured south-west monsoon rainfall between 1988 (980 mm) to 1998 (880 mm) over India. This finding perhaps suggests the possible reversal of reported coupling of increased snowfall followed by lower monsoon rainfall.     

Analysis of Seasonal Variability of Vegetation and Methane Concentration over India using SPOT-VEGETATION and ENVISAT-SCIAMACHY Data

This paper highlights the spatial and temporal variability of atmospheric columnar methane (CH₄) concentration over India and its correlation with the terrestrial vegetation dynamics. SCanning IMaging Absorption spectrometer for Atmospheric CHartographY (SCIAMACHY) on board ENVIronmental SATellite (ENVISAT) data product (0.5° × 0.5°) was used to analyze the atmospheric CH₄ concentration. Satellite Pour l'Observation de la Terre (SPOT)-VEGETATION sensor’s Normalized Difference Vegetation Index (NDVI) product, aggregated at 0.5° × 0.5° grid level for the same period (2004 and 2005), was used to correlate the with CH₄ concentration. Analysis showed mean monthly CH₄ concentration during the Kharif season varied from 1,704 parts per billion volume (ppbv) to 1,780 ppbv with the lowest value in May and the highest value in September. Correspondingly, mean NDVI varied from 0.28 (May) to 0.53 (September). Analysis of correlation between CH₄ concentration and NDVI values over India showed positive correlation (r = 0.76; n = 6) in Kharif season. Further analysis using land cover information showed characteristic low correlation in natural vegetation region and high correlation in agricultural area. Grids, particularly falling in the Indo-Gangetic Plains showed positive correlation. This could be attributed to the rice crop which is grown as a predominant crop during this period. The CH₄ concentration pattern matched well with growth pattern of rice with the highest concentration coinciding with the peak growth period of crop in the September. Characteristically low correlation was observed (r = 0.1; n = 6) in deserts of Rajasthan and forested Himalayan ecosystem. Thus, the paper emphasizes the synergistic use of different satellite based data in understanding the variability of atmospheric CH₄ concentration in relation to vegetation.     

Spatial and Temporal Variability of Net Primary Productivity (NPP) over Terrestrial Biosphere of India Using NOAA-AVHRR Based GloPEM Model

The monitoring of terrestrial carbon dynamics is important in studies related with global climate change. This paper presents results of the inter-annual variability of Net Primary Productivity (NPP) from 1981 to 2000 derived using observations from NOAA-AVHRR data using Global Production Efficiency Model (GloPEM). The GloPEM model is based on physiological principles and uses the production efficiency concept, in which the canopy absorption of photosynthetically active radiation (APAR) is used with a conversion “efficiency” to estimate Gross Primary Production (GPP). NPP derived from GloPEM model over India showed maximum NPP about 3,000 gCm⁻²year⁻¹ in west Bengal and lowest up to 500 gCm⁻²year⁻¹ in Rajasthan. The India averaged NPP varied from 1,084.7 gCm⁻²year⁻¹ to 1,390.8 gCm⁻²year⁻¹ in the corresponding years of 1983 and 1998 respectively. The regression analysis of the 20 year NPP variability showed significant increase in NPP over India (r = 0.7, F = 17.53, p < 0.001). The mean rate of increase was observed as 10.43 gCm⁻²year⁻¹. Carbon fixation ability of terrestrial ecosystem of India is increasing with rate of 34.3 TgC annually (t = 4.18, p < 0.001). The estimated net carbon fixation over Indian landmass ranged from 3.56 PgC (in 1983) to 4.57 PgC (in 1998). Grid level temporal correlation analysis showed that agricultural regions are the source of increase in terrestrial NPP of India. Parts of forest regions (Himalayan in Nepal, north east India) are relatively less influenced over the study period and showed lower or negative correlation (trend). Finding of the study would provide valuable input in understanding the global change associated with vegetation activities as a sink for atmospheric carbon dioxide.     

Spatial-temporal changes of NDVI and their relations with precipitation and temperature in Yangtze River basin from 1981 to 2001

Multitemporal NOAA/AVHRR NDVI images and monthly temperature and precipitation data were obtained across Yangtze River basin covering the period 1981–2001. The spatial and temporal patterns of NDVI are the same, while spatial analysis shows that the NDVI is influenced by the vegetation types growing in the study regions, and NDVI presents an increasing trend during the study period in the whole basin. The climate indicators play an important role in the changes of vegetation cover in the river basin. In the two Indicators, temperature has a significant effect on the NDVI values than precipitation in the whole basin. However, in the 11 subbasins, the different rules are shown in different subbasins.     

Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/albedo product

Seasonal vegetation phenology can significantly alter surface albedo which in turn affects the global energy balance and the albedo warming/cooling feedbacks that impact climate change. To monitor and quantify the surface dynamics of heterogeneous landscapes, high temporal and spatial resolution synthetic time series of albedo and the enhanced vegetation index (EVI) were generated from the 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) operational Collection V006 daily BRDF/NBAR/albedo products and 30 m Landsat 5 albedo and near-nadir reflectance data through the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The traditional Landsat Albedo (Shuai et al., 2011) makes use of the MODIS BRDF/Albedo products (MCD43) by assigning appropriate BRDFs from coincident MODIS products to each Landsat image to generate a 30 m Landsat albedo product for that acquisition date. The available cloud free Landsat 5 albedos (due to clouds, generated every 16 days at best) were used in conjunction with the daily MODIS albedos to determine the appropriate 30 m albedos for the intervening daily time steps in this study. These enhanced daily 30 m spatial resolution synthetic time series were then used to track albedo and vegetation phenology dynamics over three Ameriflux tower sites (Harvard Forest in 2007, Santa Rita in 2011 and Walker Branch in 2005). These Ameriflux sites were chosen as they are all quite nearby new towers coming on line for the National Ecological Observatory Network (NEON), and thus represent locations which will be served by spatially paired albedo measures in the near future. The availability of data from the NEON towers will greatly expand the sources of tower albedometer data available for evaluation of satellite products. At these three Ameriflux tower sites the synthetic time series of broadband shortwave albedos were evaluated using the tower albedo measurements with a Root Mean Square Error (RMSE) less than 0.013 and a bias within the range of ±0.006. These synthetic time series provide much greater spatial detail than the 500 m gridded MODIS data, especially over more heterogeneous surfaces, which improves the efforts to characterize and monitor the spatial variation across species and communities. The mean of the difference between maximum and minimum synthetic time series of albedo within the MODIS pixels over a subset of satellite data of Harvard Forest (16 km by 14 km) was as high as 0.2 during the snow-covered period and reduced to around 0.1 during the snow-free period. Similarly, we have used STARFM to also couple MODIS Nadir BRDF Adjusted Reflectances (NBAR) values with Landsat 5 reflectances to generate daily synthetic times series of NBAR and thus Enhanced Vegetation Index (NBAR-EVI) at a 30 m resolution. While normally STARFM is used with directional reflectances, the use of the view angle corrected daily MODIS NBAR values will provide more consistent time series. These synthetic times series of EVI are shown to capture seasonal vegetation dynamics with finer spatial and temporal details, especially over heterogeneous land surfaces.     

利用NOAA-AVHRR数据分析1982-2001年间中国植被的动态变化(英文)

The authors derived the normalized difference vegetation index (NDVI) from the NOAA/AVHRR Land dataset, at a spatial resolution of 8km and 15-day intervals, to investigate the vegetation variations in China during the period from 1982 to 2001. Then, GIS is used to examine the relationship between precipitation and the Normalized Difference Vegetation Index (NDVI) in China, and the value of NDVI is taken as a tool for drought monitoring. The results showed that in the study period, China’s vegetation cover h...     

Influence of Atmospheric Water Vapour on IRS NIR Measurements for Detecting Vegetation Signal. Part I: A Simulation Study Using MODTRAN

Quantitative remote sensing involving accurate estimation of vegetation properties relies greatly on the measurements of the near infrared (NIR) channel because of unique interaction property between light and leaf. It is generally assumed that the NIR measurements are made in the absence of atmospheric absorption. However, relatively weak water vapour absorption features still persist in the NIR channel, which has bearing on the quantitative estimates of the vegetation properties and long-term data series. This paper reports the results of a study that was carried out to infer the possible influence of the atmospheric water vapour (WV) on the NIR measurements (0.77–0.86 μm) of Indian Remote Sensing (IRS) satellite sensors through radiative transfer simulations using MODTRAN model. The study also suggests and evaluates the alternate band-positions for the NIR channel to improve the IRS NIR measurements. It was observed that the water absorption features present around 0.810 μm reduces the WV transmission of IRS NIR channel from 1 to 0.91 when atmospheric WV content increased from 0 to 6 g/cm² and thus hampered the NIR reflectance by 14% as compared to reference signal. A significant improvement of the order of 6.5 to 12% in the NIR reflectance and 4.2 to 7% in NDVI was observed, when IRS NIR channel was split into NIR1 (0.775–0.805 μm) and NIR2 (0.845–0.875 μm) channels by avoiding the WV absorption features. The companion paper in this issue (Pandya et al. 2011) will support results of this simulation study through the EO1-Hyperion data analysis.     

CBERS02B卫星CCD传感器数据反演陆地气溶胶

研究利用CBERS02B卫星的CCD传感器数据反演陆地气溶胶的方法。采用的方法是暗像元法。具体步骤为: 根据地面采集的植被光谱数据, 结合CCD传感器特点, 建立浓密植被(暗像元)红蓝波段(CCD传感器的第三和第一波段)反射率与地表反射率之间的关系, 确定了暗像元识别的阈值, 讨论气溶胶光学厚度对暗像元识别的影响以及消除这种影响的方法; 利用6S进行辐射传输运算, 构建查找表; 根据CBERS02B卫星的CCD传感器数据, 从查找表插值得到气溶胶光学厚度, 并进行了算法的误差分析。用广西南宁市及北京地区附近的两景数据进行了实际的反演试验, 使用MODIS的气溶胶产品与反演结果进行比对。结果显示, CBERS02B卫星的CCD传感器数据能够较好的反演陆地气溶胶。     

Spatio-temporal variation of vegetation coverage and its response to climate change in North China plain in the last 33 years

Global climate change has led to significant vegetation changes in the past half century. North China Plain, the most important grain production base of china, is undergoing a process of prominent warming and drying. The vegetation coverage, which is used to monitor vegetation change, can respond to climate change (temperature and precipitation). In this study, GIMMS (Global Inventory Modelling and Mapping Studies)-NDVI (Normalized Difference Vegetation Index) data, MODIS (Moderate-resolution Imaging Spectroradiometer) – NDVI data and climate data, during 1981–2013, were used to investigate the spatial distribution and changes of vegetation. The relationship between climate and vegetation on different spatial (agriculture, forest and grassland) and temporal (yearly, decadal and monthly) scales were also analyzed in North China Plain. (1) It was found that temperature exhibiting a slight increase trend (0.20 °C/10a, P < 0.01). This may be due to the disappearance of 0 °C isotherm, the rise of spring temperature. At the same time, precipitation showed a significant reduction trend (−1.75 mm/10a, P > 0.05). The climate mutation period was during 1991–1994. (2) Vegetation coverage slight increase was observed in the 55% of total study area, with a change rate of 0.00039/10a. Human activities may not only accelerate the changes of the vegetation coverage, but also c effect to the rate of these changes. (3) Overall, the correlation between the vegetation coverage and climatic factor is higher in monthly scale than yearly scale. The correlation analysis between vegetation coverage and climate changes showed that annual vegetation coverage was better correlatend with precipitation in grassland biome; but it showed a better correlated with temperature i the agriculture biome and forest biome. In addition, the vegetation coverage had sensitive time-effect respond to precipitation. (4) The vegetation coverage showed the same increasing trend before and after the climatic variations, but the rate of increase slowed down. From the vegetation coverage point of view, the grassland ecological zone had an obvious response to the climatic variations, but the agricultural ecological zones showed a significant response from the vegetation coverage change rate point of view. The effect of human activity in degradation region was higher than that in improvement area. But after the climate abruptly changing, the effect of human activity in improvement area was higher than that in degradation region, and the influence of human activity will continue in the future.     

Isolating type-specific phenologies through spectral unmixing of satellite time series

Vegetation phenology is commonly studied using time series of multi-spectral vegetation indices derived from satellite imagery. Differences in reflectance among land-cover and/or plant functional types are obscured by sub-pixel mixing, and so phenological analyses have typically sought to maximize the compositional purity of input satellite data by increasing spatial resolution. We present an alternative method to mitigate this ‘mixed-pixel problem’ and extract the phenological behavior of individual land-cover types inferentially, by inverting the linear mixture model traditionally used for sub-pixel land-cover mapping. Parameterized using genetic algorithms, the method takes advantage of the discriminating capacity of calibrated surface reflectance measurements in red, near infrared, and short-wave infrared wavelengths, as well as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index. In simulation, the unmixing procedure reproduced the reflectances and phenological signals of grass, crop, and deciduous forests with high fidelity (RMSE?相似文献   

Influence of Atmospheric Water Vapour on IRS NIR Measurements for Detecting Vegetation Signal. Part II: Validation of Simulation Results using Hyperion Data

This is the second paper of the series on the influence of the atmospheric water vapour (WV) on IRS NIR measurements. In the first paper (Pandya et al. 2011) a simulation study was presented where through the radiative transfer calculations it was shown that the variation of 0 to 6 g/cm² in the WV hampered the IRS NIR reflectance up to 14%. In that study splitting of IRS NIR (0.770–0.860 μm) into two bands, such as NIR1 (0.775–0.805 μm) and NIR2 (0.845–0.875 μm) was also proposed, which facilitated a considerable improvement in NIR reflectance as well as in NDVI. Objective of the present paper is to validate the findings of simulation study with the use of EO1-Hyperion data. An improvement of the order of 7% in the top-of-atmosphere reflectance over vegetation target was obtained from the satellite data analysis, which is in good agreement to that of simulation results (3.7 to 7.9%) for the continental WV conditions of 1 to 3 g/cm². This is also true for NDVI values, which illustrated a good agreement between the satellite observations (2.5%) and simulation results (2 to 4.6%) for the magnitude of improvement. Findings of the present study are preliminary in the nature but it provides a basis for enhanced NIR observations for future IRS sensors.     

Measurement and Scaling of Carbon Dioxide (CO<Subscript>2</Subscript>) Exchanges in Wheat Using Flux-Tower and Remote Sensing

The present study investigates the characteristics of CO₂ exchange (photosynthesis and respiration) over agricultural site dominated by wheat crop and their relationship with ecosystem parameters derived from MODIS. Eddy covariance measurement of CO₂ and H₂O exchanges was carried out at 10 Hz interval and fluxes of CO₂ were computed at half-hourly time steps. The net ecosystem exchange (NEE) was partitioned into gross primary productivity (GPP) and ecosystem respiration (R _e) by taking difference between day-time NEE and respiration. Time-series of daily reflectance and surface temperature products at varying resolution (250–1000 m) were used to derive ecosystem variables (EVI, NDVI, LST). Diurnal pattern in Net ecosystem exchange reveals negative NEE during day-time representing CO₂ uptake and positive during night as release of CO₂. The amplitude of the diurnal variation in NEE increased as LAI crop growth advances and reached its peak around the anthesis stage. The mid-day uptake during this stage was around 1.15 mg CO₂ m⁻² s⁻¹ and night-time release was around 0.15 mg CO₂ m⁻² s⁻¹. Linear and non-linear least square regression procedures were employed to develop phenomenological models and empirical fits between flux tower based GPP and NEE with satellite derived variables and environmental parameters. Enhanced vegetation index was found significantly related to both GPP and NEE. However, NDVI showed little less significant relationship with both GPP and NEE. Furthemore, temperature-greenness (TG) model combining scaled EVI and LST was parameterized to estimate daily GPP over dominantly wheat crop site. (R ² = 0.77). Multi-variate analysis shows that inclusion of LST or air temperature with EVI marginally improves variance explained in daily NEE and GPP.     

Utility of Hyperspectral Data for Potato Late Blight Disease Detection

The study was carried out to investigate the utility of hyperspectral reflectance data for potato late blight disease detection. The hyperspectral data was collected for potato crop at different level of disease infestation using hand-held spectroradiometer over the spectral range of 325–1075 nm. The data was averaged into 10-nm wide wavebands, resulting in 75 narrowbands. The reflectance curve was partitioned into five regions, viz. 400–500 nm, 520–590 nm, 620–680 nm, 770–860 nm and 920–1050 nm. The notable differences in healthy and diseased potato plants were noticed in 770–860 nm and 920–1050 nm range. Vegetation indices, namely NDVI, SR, SAVI and red edge were calculated using reflectance values. The differences between the vegetation indices for plants at different levels of disease infestation were found highly significant. The optimal hyperspectral wavebands to discriminate the healthy plants from disease infested plants were 540, 610, 620, 700, 710, 730, 780 and 1040 nm whereas upto 25% infestation could be discriminated using reflectance at 710, 720 and 750 nm.     

Seasonal Variation of MODIS Vegetation Indexes and Their Statistical Relationship With Climate Over the Subtropic Evergreen Forest in Zhejiang, China

Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day 1-km vegetation index products, daily temperature, photosynthetically active radiation (PAR), and precipitation from 2001 to 2004 were utilized to analyze the temporal variations of the MODIS normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), as well as their correlations with climate over the evergreen forested sites in Zhejiang-a humid subtropical region in the southeast of China. The results showed that both NDVI and EVI could discern the seasonal variation of the evergreen forests. Attributed to the sufficient precipitation in the study area, the growth of vegetation is mainly controlled by energy; as a result, NDVI, and especially EVI, is more correlated with temperature and PAR than precipitation. Compared with NDVI, EVI is more sensitive to climate condition and is a better indicator to study vegetation variations in the study region     

青藏高原地表冻融循环与植被返青期的变化趋势及其气候响应特征

青藏高原特殊的地理环境使其对全球气候变化十分敏感,所以研究其地表冻融循环和植被返青期的时空动态对于回顾和预测青藏高原对全球气候变化的响应具有重要意义。本文通过利用双指标地表冻融状态识别算法和被动微波亮温数据(SMMR、SSMI和SSMIS)来获取青藏高原长时间序列(1982年—2013年)逐日地表冻融状态,通过对GIMMS全球植被指数数据产品进行NDVI的滤波重建和返青期提取来获取青藏高原植被长时间序列(年份)的返青期;并且分析了地表冻融循环和植被返青期的变化趋势、相互关系及对青藏高原气候变化的响应特征。总体来看,在空间上,青藏高原的地表冻结集中发生在10月30日至次年4月2日,平均地表融化首日集中在5月12—27日,平均植被返青期集中在5月19—29日。植被返青期平均发生在地表融化首日后的3.94±5.58日,两者具有显著的相关关系(R=0.51,P=0.003)。青藏高原的地表融化首日和植被返青期在1982年—2013年间经历了推迟、提前再推迟的3个过程,融化时间和返青期在1982年—1987年分别以1.93±1.81 d/a和0.28±1.01 d/a的速度推迟;在1987年—2006年分别以0.67±0.20 d/a和0.13±0.16 d/a的速度提前;在2006年—2013年分别以0.97±0.84 d/a和1.04±0.52 d/a的速度推迟。中国气象局布设在青藏高原的CMA气象站的温度数据表明,高原的春季地表0 cm土壤温度呈持续上升的趋势,而植被返青期和地表融化首日并未持续提前,这可能是由几十年来高原不同地区降水等其他环境因素变化的差异造成。同时在气温持续升高期间,植被返青期的返青温度阈值也不断具有上升的趋势(R=0.72,P0.001),这可能与植被适应气候变化的自身调节能力有关。     

Remote Sensing of Eupatorium Adenophorum Spreng Based on HJ-A Satellite Data

In the southwest of China, one of the greatest threats to local ecosystem is the area expansion of an invasive species, i.e., Eupatorium adenophorum Spreng (EAS). In this study, the remote-sensing technology was used to detect and map the spatial distribution of EAS in Guizhou Province, China. A series of vegetation indices, including normalized difference vegetation index (NDVI), simple ratio index (SRI) and atmospherically resistant vegetation index (ARVI), were used to identify EAS from HJ-A Chninese satellite data. According to the analysis results of fieldworks from March 21 to 22, 2009, it was found that the vegetation index of {1.9589 ≤ SRI ≤ 4.1095}∩{0.2359 ≤ ARVI ≤ 0.5193} was the optimal remote-sensing parameter for identifying EAS from HJ-A data. According to the spatial distribution of EAS estimated from HJ-A data, it was found that EAS was rather more in southwest of Guizhou Province than in northeast. EAS became sparse from southwest to northeast gradually, and the central Guizhou Province was the ecological corridor linking EAS in southwest to that in northeast. By comparison with validated data collected by the government of Guizhou Province, it was found that the uncertainty of remote-sensing method was 18.52%, 29.31%, 8.77% and 9.46% in grassland, forest, farmland and others respectively, and the mean uncertainty was 13.29%. Owing to the lower height of EAS than many plants in forest, the uncertainty of EAS was the greatest in forest than that in grassland, farmland and so on.     

Global analysis of time-lag and -accumulation effects of climate on vegetation growth

Climate dominantly controls vegetation over most regions at most times, and vegetation responses to climate change are often asymmetric with temporal effects. However, systematic analysis of the time-lag and time-accumulation effects of climate on vegetation growth, has rarely been conducted, in particular for different vegetation growing phases. Thus, this study aimed to leverage normalized difference vegetation index (NDVI) to determine the spatiotemporal patterns of climatic effects on global vegetation growth considering various scenarios of time-lag and/or accumulation effects. The results showed that (i) climatic factors have time-lag and -accumulation effects as well as their combined effects on global vegetation growth for the whole growing season and its subphases (i.e., the growing and senescent phases). However, these effects vary with climatic factors, vegetation types, and regions. Compared with those of temperature, both precipitation and solar radiation display more significant time-accumulation effects in the whole growing season worldwide, but behave differently in the growing and senescent phases in the middle-high latitudes of the Northern Hemisphere; (ii) compared to the scenario without time effects, considering time-lag and -accumulation effects as well as their combined effects increased by 17 %, 15 %, and 19 % the overall explanatory power of vegetation growth by climate change for the whole growing season, the growing phase, and senescent phase, respectively; (iii) considering the time-lag and -accumulation effects as well as their combined effects, climate change controls 70 % of areas with a significant NDVI variation from 1982 to 2015, and the primary driving factor was temperature, followed by solar radiation and precipitation. This study highlights the significant time-lag and -accumulation effects of climatic factors on global vegetation growth. We suggest that these effects need to be incorporated into dynamic vegetation models to better understand vegetation growth under accelerating climate change.     

Land surface temperature retrieval and its validation using NOAA AVHRR thermal data

The retrieval of land (soil-vegetation complex) surface temperature (LST) was carried out over semi-arid mixed agriculture landscape of Gujarat using thermal bands (channel 4 and 5) and ground emissivity from atmospherically corrected NDVI of NOAA AVHRR LAC images. The atmospheric correction of Visible and NIR band reflectance was done using SMAC model. The LST computed from split-window method and subsequently corrected with fractional vegetation cover were then compared with near synchronous ground observations of soil and air temperatures made during 13–17 January and April, 1997 at five Land Surface Processes Experiment (LASPEX) sites of Anand, Sanand, Derol, Arnej and Khandha covering 100 km x 100 km. The fractional vegetation cover corrected LST at noon hrs. varied from 301.6 – 311.9K in January and from 315.8 – 325.6K in April. The LST_corr were found to lie in the mid way between AT and ST during January. But in April, LST were found to be more close to ST which may be due to relatively poor vegetation growth as indicated by lower NDVI values in April indicating more contribution to LST from exposed soil surface.     

利用MERIS数据植被指数分析福建省植被长势季节变化

监测植被长势动态变化可以提供生态系统状况有价值的信息,可以检测到人类或气候作用引起的变化。本研究利用2004—2005年间10期MERIS影像数据,以福建省为例,探讨MERIS数据在区域植被长势季节变化监测中的应用效果;分析了MERIS数据用于区域植被季节变化监测时的数据处理方法;比较了MERIS数据几种植被指数,提出了利用10和8波段组合改进MERISNDVI的建议;利用多时相合成的NDVI简单分析了2004年夏季—2005年夏季三个季节的植被长势状况。结果表明,MERIS植被指数的时空变化有效反映了气候变化对植被长势的影响。