Geostatistical estimation of signal-to-noise ratios for spectral vegetation indices

In the past 40 years, many spectral vegetation indices have been developed to quantify vegetation biophysical parameters. An ideal vegetation index should contain the maximum level of signal related to specific biophysical characteristics and the minimum level of noise such as background soil influences and atmospheric effects. However, accurate quantification of signal and noise in a vegetation index remains a challenge, because it requires a large number of field measurements or laboratory experiments. In this study, we applied a geostatistical method to estimate signal-to-noise ratio (S/N) for spectral vegetation indices. Based on the sample semivariogram of vegetation index images, we used the standardized noise to quantify the noise component of vegetation indices. In a case study in the grasslands and shrublands of the western United States, we demonstrated the geostatistical method for evaluating S/N for a series of soil-adjusted vegetation indices derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The soil-adjusted vegetation indices were found to have higher S/N values than the traditional normalized difference vegetation index (NDVI) and simple ratio (SR) in the sparsely vegetated areas. This study shows that the proposed geostatistical analysis can constitute an efficient technique for estimating signal and noise components in vegetation indices.     

Relationship of wheat grain yield with spectral indices

An experiment was conducted during 1996–97 and 1997–98 to study spectral indices and their relationships with grain yield of wheat. Variations of ratio vegetation index (RVI), normalized differences vegetation index (NDVI). difference vegetation index (DVI), transformed vegetation index (TVI), perpendicular vegetation index (PVI) and greenness vegetation index (GVI) have been studied at anthesis stage under different moisture and nitrogen levels. Spectral indices were correlated with crop parameters and it was found that GVI was the best index for yield estimation (r = 0.91 ).     

Reducing background effects in orchards through spectral vegetation index correction

Satellite remote sensing provides an alternative to time-consuming and labor intensive in situ measurements of biophysical variables in agricultural crops required for precision agriculture applications. In orchards, however, the spatial resolution causes mixtures of canopies and background (i.e. soil, grass and shadow), hampering the estimation of these biophysical variables. Furthermore, variable background mixtures obstruct meaningful comparisons between different orchard blocks, rows or within each row. Current correction methodologies use spectral differences between canopies and background, but struggle with a vegetated orchard floor. This background influence and the lack of a generic solution are addressed in this study.Firstly, the problem was demonstrated in a controlled environment for vegetation indices sensitive to chlorophyll content, water content and leaf area index. Afterwards, traditional background correction methods (i.e. soil-adjusted vegetation indices and signal unmixing) were compared to the proposed vegetation index correction. This correction was based on the mixing degree of each pixel (i.e. tree cover fraction) to rescale the vegetation indices accordingly and was applied to synthetic and WorldView-2 satellite imagery. Through the correction, the effect of background admixture for vegetation indices was reduced, and the estimation of biophysical variables was improved (ΔR² = 0.2–0.31).     

遥感植被指数对多时相AVHRR数据主成分分析的影响

对中国全年36个旬NOAA－AVHRR的1km覆盖数据进行两步处理：分别采用比值植被指数RVI、归一化植被指数NDVI、土壤调整植被指数SAVI和修改型土壤调整植被指数MSAVI最大值合成方法从每3旬数据合成每月数据；对每一种处理后的原始数据计算四种植被指数，并对这16种数据进行了主成分变换，分析不同处理方式对主分量积累方差和各主分量所分映生物学规律的影响。     

成像光谱数据特征选择及小麦品种识别实验研究

针对河北栾城获得的MAIS成像光谱仪数据用于小麦品种识别进行了特征选择和分类研究。利用遗传算法以JM距离为准则并结合实验区小麦的生物物理特性，进行了最佳波段选择；利用Fuzzy—Anmap分类器及选出的最佳波段对成像光谱数据进行了分类，区分出了4种小麦品种，小麦的总体分类精度超过97％。     

Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield

ABSTRACT

Agricultural drought threatens food security. Numerous remote-sensing drought indices have been developed, but their different principles, assumptions and physical quantities make it necessary to compare their suitability for drought monitoring over large areas. Here, we analyzed the performance of three typical remote sensing-based drought indices for monitoring agricultural drought in two major agricultural production regions in Shaanxi and Henan provinces, northern China (predominantly rain-fed and irrigated agriculture, respectively): vegetation health index (VHI), temperature vegetation dryness index (TVDI) and drought severity index (DSI). We compared the agreement between these indices and the standardized precipitation index (SPI), soil moisture, winter wheat yield and National Meteorological Drought Monitoring (NMDM) maps. On average, DSI outperformed the other indices, with stronger correlations with SPI and soil moisture. DSI also corresponded better with soil moisture and NMDM maps. The jointing and grain-filling stages of winter wheat are more sensitive to water stress, indicating that winter wheat required more water during these stages. Moreover, the correlations between the drought indices and SPI, soil moisture, and winter wheat yield were generally stronger in Shaanxi province than in Henan province, suggesting that remote-sensing drought indices provide more accurate predictions of the impacts of drought in predominantly rain-fed agricultural areas.     

Quantification winter wheat LAI with HJ-1CCD image features over multiple growing seasons

Remote sensing images are widely used to map leaf area index (LAI) continuously over landscape. The objective of this study is to explore the ideal image features from Chinese HJ-1 A/B CCD images for estimating winter wheat LAI in Beijing. Image features were extracted from such images over four seasons of winter wheat growth, including five vegetation indices (VIs), principal components (PC), tasseled cap transformations (TCT) and texture parameters. The LAI was significantly correlated with the near-infrared reflectance band, five VIs [normalized difference vegetation index, enhanced vegetation index (EVI), modified nonlinear vegetation index (MNLI), optimization of soil-adjusted vegetation index, and ratio vegetation index], the first principal component (PC1) and the second TCT component (TCT2). However, these image features cannot significantly improve the estimation accuracy of winter wheat LAI in conjunction with eight texture measures. To determine the few ideal features with the best estimation accuracy, partial least squares regression (PLSR) and variable importance in projection (VIP) were applied to predict LAI values. Four remote sensing features (TCT2, PC1, MNLI and EVI) were chosen based on VIP values. The result of leave-one-out cross-validation demonstrated that the PLSR model based on these four features produced better result than the ten features’ model, throughout the whole growing season. The results of this study suggest that selecting a few ideal image features is sufficient for LAI estimation.     

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.     

Establishing and assessing the Integrated Surface Drought Index (ISDI) for agricultural drought monitoring in mid-eastern China

Accurately monitoring the temporal, spatial distribution and severity of agricultural drought is an effective means to reduce the farmers’ losses. Based on the concept of the new drought index called VegDRI, this paper established a new method, named the Integrated Surface Drought Index (ISDI). In this method, the Palmer Drought Severity Index (PDSI) was selected as the dependent variable; for the independent variables, 12 different combinations of 14 factors were examined, including the traditional climate-based drought indicators, satellite-derived vegetation indices, and other biophysical variables. The final model was established by fully describing drought properties with the smaller average error (relative error) and larger correlation coefficients. The ISDI can be used not only to monitor the main drought features, including precipitation anomalies and vegetation growth conditions but also to indicate the earth surface thermal and water content properties by incorporating temperature information. Then, the ISDI was used for drought monitoring from 2000 to 2009 in mid-eastern China. The results for 2006 (a typical dry year) demonstrate the effectiveness and capability of the ISDI for monitoring drought on both the large and the local scales. Additionally, the multiyear ISDI monitoring results were compared with the actual drought intensity using the agro-meteorological disaster data recorded at the agro-meteorological sites. The investigation results indicated that the ISDI confers advantages in the accuracy and spatial resolution for monitoring drought and has significant potential for drought identification in China.     

高光谱反演水稻叶面积指数的主成分分析法

为了通过水稻冠层反射光谱来提取水稻叶面积指数信息，尝试利用辐射传输模型PROSPECT+SAIL来模拟水稻冠层反射光谱， 比较了各植被指数中叶面积指数(LAI)和叶绿素浓度的相关性。在观察光谱曲线后发现，红边位置光谱可以较好地区分LAI和叶绿素 浓度二者引起光谱变化的差异。由此提出对700 nm~750 nm区间内的反射光谱做主成分变换，并利用第2主成分与LAI建立反演模型( 即主成分分析法)，取得了较好效果，表明在植被指数趋近于饱和以至于无法区分二者相关性时，主成分分析法可以作为一种简单 而有效提取水稻叶面积指数信息的补充手段。     

Leaf Area Index Estimation Using Time-Series MODIS Data in Different Types of Vegetation

The aim of this study is to estimate leaf area index (LAI) in different type of plants using vegetation indices (VIs) and neural network algorithms retrieved from MODIS data. Four VI were calculated, and neural networks were built up based on MODIS surface reflectance products. Among the tested VIs, normalized difference vegetation index (NDVI) and chlorophyll index (CI) appeared to be the best candidate indices in estimating LAI across sites with different vegetation types. The models having the highest accuracy were CI for grassland and deciduous broad leaf forest with determination coefficients (R-square above 0.70, and NDVI for crop R-square?=?0.78). Neural network showed better results than VI methods except in grassland sites. The added VI information showed no significant improvement of model accuracy for the neural networks in most sites.     

Analysis of Spectral Vegetation Signal Characteristics as a Function of Soil Moisture Conditions Using Hyperspectral Remote Sensing

The assessment and quantification of spatio-temporal soil characteristics and moisture patterns are important parameters in the monitoring and modeling of soil landscapes. Remote-sensing techniques can be applied to characterize and quantify soil moisture patterns, but only when dealing with bare soil. For soils with vegetation, it is only possible to quantify soil-moisture characteristics through indirect vegetation indicators, i.e. the “vitality” of plants. The “vitality” of vegetation is a sum of many indicators, whereby different stress factors can induce similar changes to the biochemical and physiological characteristics of plants. Analysis of the cause and effect of soil-moisture properties, patterns and stress factors can therefore only be carried out using an experimental approach that specifically separates the causes. The study describes an experimental approach and the results from using an imaging hyperspectral sensor AISA-EAGLE (400–970 nm) and a non-imaging spectral sensor ASD (400–2,500 nm) under controlled and comparable conditions in a laboratory to study the spectral response compared to biochemical and biophysical vegetation parameters (“vitality”) as a function of soil moisture characteristics over the entire blooming period of Ash trees. At the same time that measurements were taken from the hyperspectral sensors, the following vegetation variables were also recorded: leaf area index (LAI), chlorophyll meter value — SPAD-205, vegetation height, C/N content and leaf water content as indicators of the “vitality” and the state of the vegetation. The spectrum of each hyperspectral image was used to calculate a range of vegetation indices (VI’s) with relationships for soil moisture characteristics and stress factors. The relationship between vegetation indices and plant “vitality” indicators was analysed using a Generalized Additive Model (GAM). The results show that leaf water content is the most appropriate vegetation indicator for assessing the “vitality” of vegetation. With the Water Index (WI) it was possible to differentiate between the moisture treatments of the control, moisture drought stress and the moisture flooding treatment over the entire growing season of the plants (R ²?=?0.94). There is a correlation between the “vitality” vegetation parameters (LAI, C/N content and vegetation height) and the indicators NDVI, WI, PRI and Vog2. In our study with Ash trees the vegetation parameter chlorophyll was found not to be a suitable indicator for detecting the “vitality” of plants using the spectral indicators. There is a possibility that the sensitivity of the indicators selected was too low compared to changes in the chlorophyll content of Ash trees. Adding the co-variable ‘time’ strengthens the correlation, whereas incorporating time and moisture treatment only improves the model very slightly. This shows that changes to the biochemical and biophysical characteristics caused by phenology, overlay a differentiation of the moisture treatments.     

Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice

The common spectra wavebands and vegetation indices (VI) were identified for indicating leaf nitrogen accumulation (LNA), and the quantitative relationships of LNA to canopy reflectance spectra were determined in both wheat (Triticum aestivum L.) and rice (Oryza sativa L.). The 810 and 870 nm are two common spectral wavebands indicating LNA in both wheat and rice. Among all ratio vegetation indices (RVI), difference vegetation indices (DVI) and normalized difference vegetation indices (NDVI) of 16 wavebands from the MSR16 radiometer, RVI (870, 660) and RVI (810, 660) were most highly correlated to LNA in both wheat and rice. In addition, the relations between VIs and LNA gave better results than relations between single wavebands and LNA in both wheat and rice. Thus LNA in both wheat and rice could be indicated with common VIs, but separate regression equations are better for LNA monitoring.     

RNDSI: A ratio normalized difference soil index for remote sensing of urban/suburban environments

Understanding land use land cover change (LULCC) is a prerequisite for urban planning and environment management. For LULCC studies in urban/suburban environments, the abundance and spatial distributions of bare soil are essential due to its biophysically different properties when compared to anthropologic materials. Soil, however, is very difficult to be identified using remote sensing technologies majorly due to its complex physical and chemical compositions, as well as the lack of a direct relationship between soil abundance and its spectral signatures. This paper presents an empirical approach to enhance soil information through developing the ratio normalized difference soil index (RNDSI). The first step involves the generation of random samples of three major land cover types, namely soil, impervious surface areas (ISAs), and vegetation. With spectral signatures of these samples, a normalized difference soil index (NDSI) was proposed using the combination of bands 7 and 2 of Landsat Thematic Mapper Image. Finally, a ratio index was developed to further highlight soil covers through dividing the NDSI by the first component of tasseled cap transformation (TC1). Qualitative (e.g., frequency histogram and box charts) and quantitative analyses (e.g., spectral discrimination index and classification accuracy) were adopted to examine the performance of the developed RNDSI. Analyses of results and comparative analyses with two other relevant indices, biophysical composition index (BCI) and enhanced built-up and bareness Index (EBBI), indicate that RNDSI is promising in separating soil from ISAs and vegetation, and can serve as an input to LULCC models.     

利用AMSR-E数据反演华北平原冬小麦单散射反照率

首先,基于冬小麦不同生育期的地面实测参数,构建了组成冬小麦冠层的、包括不同尺寸和含水量的介电散射体模拟数据库,并在此基础上建立冬小麦单散射反照率和光学厚度分别在C(6.925 GHz)和X(10.65 GHz)波段之间的依赖关系。然后,根据一阶参数化模型推导得到的微波植被指数MVIs(Microwave Vegetation Indices)的物理表达式,结合AMSR-E被动微波亮温数据,反演了华北平原地区冬小麦不同生育期的单散射反照率。与MODIS日归一化差异植被指数NDVI的对比结果显示:冬小麦单散射反照率与NDVI随时间的变化趋势大致相同,但在冬小麦的抽穗期到乳熟期,NDVI呈现饱和趋势,而单散射反照率对小麦的生长变化仍旧比较敏感,在指示冬小麦生长方面具有一定优势。     

Exploring relation of land surface temperature with selected variables using geographically weighted regression and ordinary least square methods in Manipur State,India

In the present study, relationship between Land surface temperature and selected indices, vegetation index (VARI), built-up index (BUI) and elevation (DEM) is investigated. Ordinary least square method and geographically weighted regression are used to analyse the spatial correlation between the indices with surface temperature. Subsequently, temporal trends (2001–2015) in surface temperature and vegetation are explored after every two years of interval. LANDSAT image and ASTER DEM are used to extract LST and additional indices. The selected variables (Built-up, vegetation and topography) explain 69% of the variation in surface temperature. The OLS and GWR revealed that topography and vegetation are the significant factor of LST in Manipur State. Topography being a constant parameter, its effect is constant over time. The changing scenario of vegetation is significantly contributing to LST. The surface temperature over a period of 15 years show increasing trend and is negatively and strongly correlated to vegetation cover.     

Wheat crop production estimation using satellite data

This paper reports acreage, yield and production forecasting of wheat crop using remote sensing and agrometeorological data for the 1998–99 rabi season. Wheat crop identification and discrimination using Indian Remote Sensing (IRS) ID LISS III satellite data was carried out by supervised maximum likelihood classification. Three types of wheat crop viz. wheat-1 (high vigour-normal sown), wheat-2 (moderate vigour-late sown) and wheat-3 (low vigour-very late sown) have been identified and discriminated from each other. Before final classification of satellite data spectral separability between classes were evaluated. For yield prediction of wheat crop spectral vegetation indices (RVI and NDVI), agrometeorological parameters (ET_max and TD) and historical crop yield (actual yield) trend analysis based linear and multiple linear regression models were developed. The estimated wheat crop area was 75928.0 ha. for the year 1998–99, which sowed ?2.59% underestimation with land record commissioners estimates. The yield prediction through vegetation index based and vegetation index with agrometeorological indices based models were 1753 kg/ha and 1754 kg/ha, respectively and have shown relative deviation of 0.17% and 0.22%, the production estimates from above models when compared with observed production show relative deviation of ?2.4% and ?2.3% underestimations, respectively.     

An Information fusion approach for PALSAR data to retrieve soil moisture

Estimation of vegetation covered soil moisture with satellite images is still a challenging task. Several models are available for soil moisture retrieval in which water cloud model (WCM) is most common. But, it requires an estimation of accurate vegetation parameterization. Thus, there is a need to develop such an approach for soil moisture retrieval which minimize these limitations. Therefore, this paper deals with the soil moisture retrieval using fully polarimetric SAR data by fusing the information from different bands. Various polarimetric indices and observables were critically analysed, and found that the index; SPAN (total scattered power) gives better information of vegetation cover as compared to other indices/observables. Based on this, WCM model has been modified using SPAN as parameter and soil moisture content were retrieved.     

Combination of multi-sensor remote sensing data for drought monitoring over Southwest China

Drought is one of the most frequent climate-related disasters occurring in Southwest China, where the occurrence of drought is complex because of the varied landforms, climates and vegetation types. To monitor the comprehensive information of drought from meteorological to vegetation aspects, this paper intended to propose the optimized meteorological drought index (OMDI) and the optimized vegetation drought index (OVDI) from multi-source satellite data to monitor drought in three bio-climate regions of Southwest China. The OMDI and OVDI were integrated with parameters such as precipitation, temperature, soil moisture and vegetation information, which were derived from Tropical Rainfall Measuring Mission (TRMM), Moderate Resolution Imaging Spectroradiometer Land Surface Temperature (MODIS LST), AMSR-E Soil Moisture (AMSR-E SM), the soil moisture product of China Land Soil Moisture Assimilation System (CLSMAS), and MODIS Normalized Difference Vegetation Index (MODIS NDVI), respectively. Different sources of satellite data for one parameter were compared with in situ drought indices in order to select the best data source to derive the OMDI and OVDI. The Constrained Optimization method was adopted to determine the optimal weights of each satellite-based index generating combined drought indices. The result showed that the highest positive correlation and lowest root mean square error (RMSE) between the OMDI and 1-month standardized precipitation evapotranspiration index (SPEI-1) was found in three regions of Southwest China, suggesting that the OMDI was a good index in monitoring meteorological drought; in contrast, the OVDI was best correlated to 3-month SPEI (SPEI-3), and had similar trend with soil relative water content (RWC) in temporal scale, suggesting it a potential indicator of agricultural drought. The spatial patterns of OMDI and OVDI along with the comparisons of SPEI-1 and SPEI-3 for different months in one year or one month in different years showed significantly varied drought locations and areas, demonstrating regional and seasonal fluctuations, and suggesting that drought in Southwest China should be monitored in seasonal and regional level, and more fine distinctions of seasons and regions need to be considered in the future studies of this area.     

Relationship of bidirectional reflectance of wheat with biophysical parameters and its radiative transfer modeling using PROSAIL

The algorithms for deriving vegetation biophysical parameters rely on the understanding of bi-directional interaction of radiation and its subsequent linkages with canopy radiative transfer models and their inversion. In this study, an attempt has been made to define the geometry of sensor and source position to best relate plant biophysical parameters with bidirectional reflectance of wheat varieties varying in canopy architecture and to validate the performance of PROSAIL (PROSPECT+SAIL) canopy radiative transfer model. A field experiment was conducted with two wheat cultivars varying in canopy geometry and phenology. The bidirectional measurements between 400nm–1100nm at 5nm interval were recorded every week at six view azimuth and four view zenith positions using spectro-radiometer. Canopy biophysical parameters were recorded synchronous to bi-directional reflectance measurements. The broadband reflectances were used to compute the NDVIs which were subsequently related to leaf area index and biomass. Results showed that the bidirectional reflectance increased with increase in view zenith from 20⁰ to 60⁰ irrespective of the sensor azimuth. For a given view zenith, the reflectance was observed to be maximum at 150⁰ azimuth where the difference between the sun and sensor azimuth was least. For sun azimuth of 160⁰ and zenith of 52⁰, the view geometry defined by 150⁰ azimuth and 50⁰ zenith corresponded to hotspot position. The measured bidirectional NDVI had significant logarithmic relationship with LAI and linear relationship with biomass for both the varieties of wheat and maximum correlation of NDVI with LAI and with biomass was obtained at the hotspot position. The PROSAIL validation results showed that the model simulated well the overall shape of spectra for all combination of view zenith and azimuth position for both wheat varieties with overall RMSE less than 5 per cent. The hotspot and dark spot positions were also well simulated and hence model performance may be suitable for deriving wheat biophysical parameters using satellite derived reflectances.