Satellite-observed changes in terrestrial vegetation growth trends across the Asia-Pacific region associated with land cover and climate from 1982 to 2011

The Asia-Pacific (AP) region has experienced faster warming than the global average in recent decades and has experienced more climate extremes, however little is known about the response of vegetation growth to these changes. The updated Global Inventory Modeling and Mapping Studies third-generation global satellite Advanced Very High Resolution Radiometer Normalized Difference Vegetation Index dataset and gridded reanalysis climate data were used to investigate the spatiotemporal changes in both trends of vegetation dynamic indicators and climatic variables. We then further analyzed their relations associated with land cover across the AP region. The main findings are threefold: (1) at continental scales the AP region overall experienced a gradual and significant increasing trend in vegetation growth during the last three decades, and this NDVI trend corresponded with an insignificant increasing trend in temperature; (2) vegetation growth was negatively and significantly correlated with the Pacific Decadal Oscillation index and the El Niño/Southern Oscillation (ENSO) in AP; and (3) at pixel scales, except for Australia, both vegetation growth and air temperature significantly increased in the majority of study regions and vegetation growth spatially correlated with temperature; In Australia and other water-limited regions vegetation growth positively correlated with precipitation.     

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.     

Study of normalized difference vegetation index variation and its correlation with climate factors in the three-river-source region

Using NOAA/AVHRR 10-day composite NDVI data and 10-day meteorological data, including air temperature, precipitation, vapor pressure, wind velocity and sunshine duration, at 19 weather stations in the three-river-source region in the Qinghai–Tibetan Plateau in China from 1982 to 2000, the variations of NDVI and climate factors were analyzed for the purpose of studying the correlation between climate change and vegetation growth as represented by NDVI in this region. Results showed that the NDVI values in this region gradually grew from the west to the east, and the distribution was consistent with that of moisture status. The growing season came earlier due to climate warming, yet because of the reduction of precipitation, maximal NDVI during 1982–2000 did not show a significant change. NDVI related positively to air temperature, vapor pressure and precipitation, but negatively related to sunshine duration and wind velocity. Furthermore, the response of NDVI to climate change showed time lags for different climate factors. Water condition and temperature were found to be the most important factors effecting the variation of NDVI during the growing season in both the semi-arid and the semi-humid areas. In addition, NDVI had a better correlation with vapor pressure than with precipitation. The ratio of precipitation to evapotranspiration, representing water gain and loss, can be regarded as a comprehensive index to analyze NDVI and climate change, especially in areas where the water condition plays a dominant role.     

Spatiotemporal variation in the terrestrial vegetation phenology of Iraq and its relation with elevation

Iraq contains the Great Mesopotamian alluvial plain of the Euphrates and Tigris rivers. Its regional vegetation phenological patterns are worthy of investigation because relatively little is known about the phenology of semi-arid environments, and because their inter-annual variation is expected to be driven by uncertain rainfall and varied topography. The aim of this research was to assess and map the spatial variation in key land surface phenology (LSP) parameters over the last decade and their relation with elevation. It is the first study mapping land surface phenology during last decade over the whole of Iraq, and one of only a few studies on vegetation phenology in a semi-arid environment. Time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) normalised difference vegetation index (NDVI) data at 250 m spatial resolution and 8 day temporal resolution, were employed to map the spatial variation in three LSP parameters for the major vegetation types in Iraq during 2001–2012. LSP parameters were defined by inflection points after smoothing the vegetation phenological signals using the Fourier technique. The estimated key LSP parameters indicated that the relatively shorter length of season (LOS) in the north of Iraq resulted from a delayed start of season (SOS). Greater spatial variation occurred in the SOS than end of season (EOS), which may be due to the spatial distribution of rainfall and temperature as a function of elevation. A positive correlation was observed for SOS and EOS with elevation for all major land cover types with EOS producing the largest positive correlation (R² = 0.685, R² = 0.638 and R² = 0.588, p < 0.05 in shrubland, cropland and grassland, respectively). The magnitude of delay in SOS and EOS increased in all land cover types along a rising elevation gradient where for each 500 m increase, SOS was delayed by around 25 or more days and EOS delayed by around 22 or more days, except for grassland. The SOS and EOS also varied temporally during the last decade, particularly the SOS in the lowland, north of the country where the standard deviation was around 80 to 120 days, due mainly to the practice of crop rotation and the traditional biennial cropping system. Thus, the results of this research emphasize the effect of elevation on key LSP parameters over Iraq, for all major vegetation types.     

Biome-level relationships between vegetation indices and climate variables using time-series analysis of remotely-sensed data

ABSTRACT

Climatic factors such as rainfall and temperature play a vital role in the growth characteristics of vegetation. While the relationship between climate and vegetation growth can be accurately predicted in instances where vegetation is homogenous, this becomes complex to determine in heterogeneous vegetation environments. The aim of this paper was to study the relationship between remotely-sensed monthly vegetation indices (i.e. Normalized Difference Vegetation Index and Enhanced Vegetation Index) and climatic variables (temperature and precipitation) using time-series analysis at the biome-level. Specifically, the autoregressive distributed lag model (ARDL1 and ARDL2, corresponding respectively to one month and two month lags) and the Koyck-transformed distributed lag model were used to build regression models. All three models estimated NDVI and EVI fairly accurately in all biomes (Relative Root-Mean-Squared-Error (RMSE): 12.0–26.4%). Biomes characterized by relative homogeneity (Grassland, Savanna, Indian Ocean Coastal Belt and Forest Biomes) achieved the most accurate estimates due to the dominance of a few species. Comparisons of lag size (one month compared to two months) generally showed similarities (Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and log-likelihood) with quite high comparability in certain biomes – this indicates the utility of the ARDL1 and ARDL2 model, depending on the availability of appropriate data. These findings demonstrate the variation in estimation linked to the biome, and thus the validity of biome-level correlation of climatic data and vegetation indices.     

Spatiotemporal analysis of vegetation variability and its relationship with climate change in China

This paper investigated spatiotemporal dynamic pattern of vegetation, climate factor, and their complex relationships from seasonal to inter-annual scale in China during the period 1982–1998 through wavelet transform method based on GIMMS data-sets. First, most vegetation canopies demonstrated obvious seasonality, increasing with latitudinal gradient. Second, obvious dynamic trends were observed in both vegetation and climate change, especially the positive trends. Over 70% areas were observed with obvious vegetation greening up, with vegetation degradation principally in the Pearl River Delta, Yangtze River Delta, and desert. Overall warming trend was observed across the whole country (>98% area), stronger in Northern China. Although over half of area (58.2%) obtained increasing rainfall trend, around a quarter of area (24.5%), especially the Central China and most northern portion of China, exhibited significantly negative rainfall trend. Third, significantly positive normalized difference vegetation index (NDVI)–climate relationship was generally observed on the de-noised time series in most vegetated regions, corresponding to their synchronous stronger seasonal pattern. Finally, at inter-annual level, the NDVI–climate relationship differed with climatic regions and their long-term trends: in humid regions, positive coefficients were observed except in regions with vegetation degradation; in arid, semiarid, and semihumid regions, positive relationships would be examined on the condition that increasing rainfall could compensate the increasing water requirement along with increasing temperature. This study provided valuable insights into the long-term vegetation–climate relationship in China with consideration of their spatiotemporal variability and overall trend in the global change process.     

PhenoCrop: An integrated satellite-based framework to estimate physiological growth stages of corn and soybeans

The accurate and timely estimates of crop physiological growth stages are essential for efficient crop management and precise modeling of agricultural systems. Satellite remote sensing has been widely used to retrieve vegetation phenology metrics at local to global scales. However, most of these phenology metrics (e.g., green-up) are different from crop growth stages (e.g., emergence) used in crop management and modeling. As such, an integrated framework referred to as PhenoCrop was developed to: 1) establish a connection between remote sensing-derived phenology metrics and key crop growth stages based on Wang and Engle plant phenology model and 2) use fused MODIS-Landsat 30 m 8-day reflectance data generated using Kalman Filter-based data fusion technique to produce onset dates of key growth stages of corn (Zea mays L.) and soybeans (Glycine max L.) at 30 m spatial resolution. In this paper, we described the PhenoCrop framework, and tested its performance for the State of Nebraska for 2012–2016 by comparison to observations of estimated key growth stages at four experimental sites, and state-level statistical data from Crop Progress Reports (CPRs) published by the United States Department of Agriculture’s (USDA) National Agricultural Statistical Services (NASS). In addition, to evaluate the suitability of using coarse or high spatial resolution satellite imagery, fused MODIS-Landsat-based estimates were compared with those produced using EOS MODIS 250 m (MOD9Q1) reflectance data.The PhenoCrop estimates captured the typical spatial trends of gradual delay in the progression of the growing season from southeast to northwest Nebraska. Also inter-annual differences due to factors such as weather fluctuations and change in management strategies (e.g., early season in 2012) were evident in the estimates. Validation results revealed that average root mean square error (RMSE) of the state-level estimates of corn and soybean growth stages ranged from 1.10 to 4.20 days and from 3.81 to 7.89 days, respectively, while pixel level estimates had a RMSE ranging from 3.72 to 8.51 days for corn and 4.76–9.51 days for soybean growth stages. Although MODIS 250 m based estimates showed similar general spatial patterns observed in the fused MODIS-Landsat based estimates, the accuracy and ability to capture field scale variations was improved with fused MODIS-Landsat data. Overall, results showed the ability of PhenoCrop framework to provide reliable estimates of crop growth stages that can be highly useful in crop modeling and crop management during the growing season.     

View-illumination effects on hyperspectral vegetation indices in the Amazonian tropical forest

Because of the pointing capability of the Hyperion/Earth Observing-One (EO-1) to improve the revisit time of the scene, temporal series of narrowband vegetation indices (VIs) can be generated to study the phenology of the Amazonian tropical forests. In this study, 10 selected narrowband VIs calculated from Hyperion nadir and off-nadir data and from different view directions (forward scattering and backscattering) were analyzed for their sensitivity to view-illumination effects along the dry season on the Seasonal Semi-deciduous Forest. Data analysis was also supported by PROSAIL modeling to simulate the spectral response of this forest type in both directions. Hyperion and PROSAIL results showed that the Enhanced Vegetation Index (EVI) and Photochemical Reflectance Index (PRI) were the two more anisotropic VIs, whereas the Normalized Difference Vegetation Index (NDVI), Structure Insensitive Pigment Index (SIPI) and the Vogelmann Red Edge Index (VOG) were comparatively less sensitive to view-illumination effects. When compared to the other VIs and because of the greater dependence on the near-infrared (NIR) reflectance, EVI showed a different spectral behavior. EVI increased from forward scattering to backscattering and with decreasing solar zenith angle (SZA) towards the end of the local dry season, due to reduction in shading and enhancement of the illumination effects. On the other hand, PRI was higher with increasing shading in the forward scattering direction, as deduced from the PROSAIL simulation. Results emphasized the importance of taking into account bidirectional effects when analyzing temporal series of VIs collected over tropical forests by imaging spectrometers with pointing capability or even by multispectral sensors with large field-of-view (FOV).     

A comparative analysis of the NDVIg and NDVI3g in monitoring vegetation phenology changes in the Northern Hemisphere

Phenology is a sensitive and critical feature of vegetation and is a good indicator for climate change studies. The global inventory modelling and mapping studies (GIMMS) normalized difference vegetation index (NDVI) has been the most widely used data source for monitoring of the vegetation dynamics over large geographical areas in the past two decades. With the release of the third version of the NDVI (GIMMS NDVI3g) recently, it is important to compare the NDVI3g data with those of the previous version (NDVIg) to link existing studies with future applications of the NDVI3g in monitoring vegetation phenology. In this study, the three most popular satellite start of vegetation growing season (SOS) extraction methods were used, and the differences between SOSg and SOS3g arising from the methods were explored. The amplitude and the peak values of the NDVI3g are higher than those of the NDVIg curve, which indicated that the SOS derived from the NDVIg (SOSg) was significantly later than that derived from the NDVI3g (SOS3g) based on all the methods, for the whole northern hemisphere. In addition, SOSg and SOS3g both showed an advancing trend during 1982–2006, but that trend was more significant with SOSg than with SOS3g in the results from all three methods. In summary, the difference between SOSg and SOS3g (in the multi-year mean SOS, SOS change slope and the turning point in the time series) varied among the methods and was partly related to latitude. For the multi-year mean SOS, the difference increased with latitude intervals in the low latitudes (0–30°N) and decreased in the mid- and high-latitude intervals. The GIMMS NDVI3g data-sets seemed more sensitive than the GIMMS NDVIg in detecting information about the ground, and the SOS3g data were better correlated both with the in situ observations and the SOS derived from the Moderate Resolution Imaging Spectroradiometer NDVI. For the northern hemisphere, previous satellite measures (SOS derived from GIMMS NDVIg) may have overestimated the advancing trend of the SOS by an average of 0.032 d yr^–1.     

兰州市南北两山植被覆盖度动态变化遥感监测

本文基于植被指数(NDVI)和植被覆盖度像元分解模型,建立了兰州市南北两山植被覆盖度遥感定量模型,在此基础上研究了兰州市南北两山1991年和2006年两个时期的植被覆盖度动态变化,结果表明:1991-2006年兰州市南北两山绿化工程区植被覆盖度总体呈上升趋势,低植被覆盖度面积减小,中高和高植被覆盖度面积增加,其中七里河工程区植被覆盖度变化最为明显,绿化效果较好,安宁工程区绿化效果相对较差,结果可为兰州市南北两山绿化工程提供科学依据。     

像元分解模型的植被覆盖度遥感估算

为了提高植被覆盖度遥感估算方法的精度,该文针对置信度方法和空间克里金插值方法各自存在的问题,基于线性像元二分模型,分别采用置信度方法和空间克里金插值方法计算推导,确定像元二分模型中两个重要参数NDVIveg和NDVIsoil,实现估算植被覆盖度,并对两种方法进行对比分析,同时提出方法中存在的问题以及模型的优化改进方向。     

太湖水生植被NDVI的时空变化特征分析

为了明确太湖不同生态区水生植被长势的变化规律及其影响因子,利用MODIS传感器提供的NDVI数据,分析了太湖2000年—2015年NDVI的时间及空间变化特征。结果表明:太湖水生植被NDVI存在明显的季节变化和年际变化,NDVI每年最小值出现在冬季,最大值出现在植被生长旺盛的8月或9月,其值可达0.35;太湖全湖NDVI多年平均值为0.1,最大值为0.14,出现在2007年。太湖NDVI的空间差异可将太湖划分为不同的植被类型区,太湖西北部(竺山湾和梅梁湾)NDVI最大值可达0.2,植被类型主要以浮游藻类为主,东太湖区域最大值超过0.6,主要以沉水植被为主;太湖不同区域植被动态特征对气象因子的响应也不尽相同,沉水植物生长与平均气温有显著的正相关关系,而浮游植物区的生长状况受平均风速影响较大。     

宁夏不同植被类型归一化指数与气象因子分析

针对植被动态对气候变化响应的问题,提出了从小尺度范围研究植被指数与气象因子的相关性,采用2000—2010年MODIS归一化植被指数数据集和宁夏10个气象站2000—2010年逐月气象资料,分析了气象站点周围10km缓冲区内不同植被类型NDVI与气象因子的相关性。结果表明:2000—2010年宁夏不同植被类型NDVI均呈上升趋势;极端最低气温、最高气温、平均气温、平均相对湿度以及日照时数对宁夏地区植被的生长有明显的滞后效应;植被NDVI与极端最低气温的相关性系数最大,其次是平均气温;不同植被类型的NDVI与极端最高气温、极端最低气温以及平均气温的相关性由南向北呈现波动性增长,与降水量的相关性由南向北呈现明显的减小趋势;且耕地NDVI与各气象因子的相关性最大。     

基于RS与GIS技术的泸定县植被空间分布分析

以四川省泸定县为分析研究区域,综合运用遥感图像处理技术与GIS空间分析技术,用ETM+遥感影像获取归一化植被指数(NDVI)信息并反演植被覆盖度,用地形图等高线生成数字高程模型(DEM)并提取地形因子。借助叠合分析法,讨论植被覆盖度与海拔高度、坡度、坡度变率、坡向、坡向变率5种地形因子的空间关系,得到泸定县关于地形因子的各等级植被空间分布特征。分析对地植物学中高山峡谷地区植被的地形格局分布规律研究与生态环境的评价与改良都具有重要的参考价值。     

High positive correlation between soil temperature and NDVI from 1982 to 2006 in alpine meadow of the Three-River Source Region on the Qinghai-Tibetan Plateau

Using satellite-observed Normalized Difference Vegetation Index (NDVI) data and Rotated Empirical Orthogonal Function (REOF) method, we analyzed the spatio-temporal variation of vegetation during growing seasons from May to September in the Three-River Source Region, alpine meadow in the Qinghai-Tibetan Plateau from 1982 to 2006. We found that NDVI in the centre and east of the region, where the vegetation cover is low, showed a consistent but slight increase before 2003 and remarkable increase in 2004 and 2005. Impact factors analysis indicted that among air temperature, precipitation, humid index, soil surface temperature, and soil temperature at 10 cm and 20 cm depth, annual variation of NDVI was highly positive correlated with the soil surface temperature of the period from March to July. Further analysis revealed that the correlation between the vegetation and temperature was insignificant before 1995, but statistically significant from 1995. The study indicates that temperature is the major controlling factor of vegetation change in the Three-River Source Region, and the currently increase of temperature may increase vegetation coverage and/or density in the area. In addition, ecological restoration project started from 2005 in Three-River Source Region has a certain role in promoting the recovery of vegetation.     

A shadow- eliminated vegetation index (SEVI) for removal of self and cast shadow effects on vegetation in rugged terrains

ABSTRACT

The effect of terrain shadow, including the self and cast shadows, is one of the main obstacles for accurate retrieval of vegetation parameters by remote sensing in rugged terrains. A shadow- eliminated vegetation index (SEVI) was developed, which was computed from only red and near-infrared top-of-atmosphere reflectance without other heterogeneous data and topographic correction. After introduction of the conceptual model and feature analysis of conventional wavebands, the SEVI was constructed by ratio vegetation index (RVI), shadow vegetation index (SVI) and adjustment factor (f (Δ)). Then three methods were used to validate the SEVI accuracy in elimination of terrain shadow effects, including relative error analysis, correlation analysis between the cosine of solar incidence angle (cosi) and vegetation indices, and comparison analysis between SEVI and conventional vegetation indices with topographic correction. The validation results based on 532 samples showed that the SEVI relative errors for self and cast shadows were 4.32% and 1.51% respectively. The coefficient of determination between cosi and SEVI was only 0.032 and the coefficient of variation (std/mean) for SEVI was 12.59%. The results indicate that the proposed SEVI effectively eliminated the effect of terrain shadows and achieved similar or better results than conventional vegetation indices with topographic correction.     

沙漠蝗群对印巴边境植被的影响及其未来可能发展趋势

2020年初非洲东北和印巴边境沙漠蝗群席卷多个国家,大面积农田及自然植被被啃食,是什么气候条件促成了此次沙漠蝗灾?距离中国最近的印巴边境蝗群成为研究以及社会关注的热点,蝗灾对当地植被的影响如何?其发展趋势如何?从气候学上分析,蝗灾历史上是否曾经或者未来是否向印度东边迁飞而进入中国呢?本研究利用长时间序列的卫星遥感数据和气象气候观测数据,对沙漠蝗群可能扩展趋势进行了分析。研究结果表明:(1)由于沙漠蝗群的啃食,2020年1月和2月,在蝗群分布区大面积植被区的归一化植被指数较常年大幅度下降,2月(2月3日数据)的啃食面积较1月明显扩大;(2)发生在2018年5月和10月两次印度洋飓风和2019年12月强热带风暴等几个罕见气旋给非洲和阿拉伯半岛带来的强降水,是本次非洲-西亚蝗灾的形成重要原因;(3)从影响沙漠蝗群起飞的气温和沙漠蝗虫适合的降水条件来看,历史上或未来沙漠蝗群迁徙到印度东边的机会很少,进入中国的可能性非常小。     

南方丘陵区植被覆盖度遥感估算的地形效应评估

植被覆盖变化是生态环境领域的核心研究内容之一,但其估算精度常受到地形效应、土壤背景、大气效应等各种因素影响。以Landsat 8 OLI为遥感数据源,基于像元二分模型,分别利用归一化差值植被指数(NDVI)、经Cosine-C校正的归一化差值植被指数(NDVI)和归一化差值山地植被指数(NDMVI)建立植被覆盖度估算模型,以评估南方丘陵区植被覆盖度的地形效应。结果表明,3种植被覆盖度估算模型均能削弱地形效应,但消除或抑制地形效应影响的能力不同。比较而言,基于NDMVI指数构建的植被覆盖度估算模型的地形效应最小,更适合地形复杂区域的植被覆盖度遥感估算;基于Cosine-C校正的NDVI植被指数构建的植被覆盖度估算模型的地形效应次之,但存在一定的过度校正现象;基于NDVI植被指数构建的植被覆盖度估算模型的地形效应最大,尤其当坡度≥10°时,阴坡植被覆盖度比阳坡明显偏低。     

Estimation of vegetation LAI from hyperspectral reflectance data: Effects of soil type and plant architecture

The retrieval of canopy biophysical variables is known to be affected by confounding factors such as plant type and background reflectance. The effects of soil type and plant architecture on the retrieval of vegetation leaf area index (LAI) from hyperspectral data were assessed in this study. In situ measurements of LAI were related to reflectances in the red and near-infrared and also to five widely used spectral vegetation indices (VIs). The study confirmed that the spectral contrast between leaves and soil background determines the strength of the LAI–reflectance relationship. It was shown that within a given vegetation species, the optimum spectral regions for LAI estimation were similar across the investigated VIs, indicating that the various VIs are basically summarizing the same spectral information for a given vegetation species. Cross-validated results revealed that, narrow-band PVI was less influenced by soil background effects (0.15 ≤ RMSE_cv ≤ 0.56). The results suggest that, when using remote sensing VIs for LAI estimation, not only is the choice of VI of importance but also prior knowledge of plant architecture and soil background. Hence, some kind of landscape stratification is required before using hyperspectral imagery for large-scale mapping of vegetation biophysical variables.     

Angle effects of vegetation indices and the influence on prediction of SPAD values in soybean and maize

To study the anisotropy of vegetation indices (VIs) and explore its influence on the retrieval accuracy of canopy soil-plant analyzer development (SPAD) value, the bidirectional reflectance distribution function (BRDF) models of soybean and maize are calculated from the multi-angle hyperspectral images acquired by UAV, respectively. According to the reflectance extracted from the BRDF model, the dependences of 16 commonly-used VIs on observation angles are analyzed, and the SPAD values of maize and soybean canopy are predicted by using the 16 VI values at different observation angles and their combinations as input parameters. The results show that the 16 VIs have different sensitivity to angle in the principal plane: green ratio vegetation index (GRVI), ratio vegetation index (RVI), red edge chlorophyll index (CIRE), and modified chlorophyll absorption in reflectance index/optimized soil-adjusted vegetation index (MCARI/OSAVI) are very sensitive to angles, among which MCARI/OSAVI of maize fluctuated the most (138.83 %); in contrast, the green optimal soil adjusted vegetation index (GOSAVI), normalized difference vegetation index (NDVI), and green normalized difference vegetation index (GNDVI) hardly change with the observation angles. In terms of SPAD prediction, the accuracy of different VI is different, the mean absolute error (MAE) showed that MCARI1 provided the highest accuracy of retrieval for soybean (MAE=1.617), while for maize it was MCARI/OSAVI (MAE=2.422). However, when using the same VI, there was no significant difference in the accuracy of the predicted results, whether the VI from different angles was used or the combination of multi-angles was used. The present results provide guiding significance and practical value for the retrieval of SPAD value in vegetation canopies and in-depth applications of multi-angular remote sensing.