Normalized Difference Spectral Index Research Articles

The relationships between electrical conductivity of soil and reflectance of canopy, grain, and leaf of rice in northeastern Thailand

Soil salinity is a global environmental problem and the most widespread land degradation problem that reduces crop yields and agricultural productivity. The characteristic of soil salinity is conventionally measured by the electric conductivity (EC) of soil while remote-sensing techniques have been extensively applied to detect the presence of salts indirectly through the vegetation using crop spectral reflectance. This study aims primarily to investigate whether salt stress the rice can be detected by field reflectance or not, and second, to search the significant bands of vegetation indices that can indicate the relationships between the EC of soil and field hyperspectral reflectance of canopy, grain, and leaf of rice, using the normalized difference spectral index (NDSI). Field investigations on various paddy fields in northeastern Thailand were carried out in late November 2010 during the ripening season just before harvest in an attempt to realize the applications of the field hyperspectral technique for monitoring the spread of saline soils and estimation of the effects of soil salinity on rice plants. Jasmine rice and glutinous rice were two different rice species selected for this study. Rice plant investigations were conducted by collecting data on crop length, panicle length, canopy openness, leaf area index, and digital photographs of plant conditions from each site. The statistical analysis revealed that the changes in soil EC were significantly sensitive to the ripening stages of both jasmine rice and glutinous rice planted on different levels of soil salinity. Among reflectance measurements, canopy reflectance was highly correlated with soil EC. However, the estimated accuracies of the relationship between soil EC and reflectance of glutinous rice were relatively lower than those of jasmine rice.

Tracking seasonal changes of leaf and canopy light use efficiency in a Phlomis fruticosa Mediterranean ecosystem using field measurements and multi-angular satellite hyperspectral imagery

Numerous normalized difference spectral indices (NDSIs) derived from leaf measurements and CHRIS/PROBA hyperspectral and multi-angular satellite images were examined for their capacity to track seasonal variations of leaf (εleaf) and canopy (εcan) light use efficiency of a Mediterranean phryganic ecosystem. A series of seasonal field ecophysiological measurements, i.e. leaf area index (LAI), leaf photosynthesis and leaf reflectance, were conducted on the Phlomis fruticosa shrubs at the days of CHRIS acquisitions over the study site. Leaf scale analysis confirmed background theory on the relationship of the photochemical reflectance index (PRI) with εleaf and provided a detailed view of the wavelengths that can be used in PRI formulation for the specific species. In canopy scale analysis, PRI and some alternative formulations of this index based on CHRIS bands, presented the most significant relationships with εcan, indicating that this index preserves its efficiency in satellite observations for the specific ecosystem. Additionally, spectral indices related to chlorophyll and water content were found to present good relationships with εcan. Taking into account the functional relationship between εcan and chlorophyll content, a combination of the xanthophyll de-epoxidation band (531nm) with 701nm CHRIS band in a NDSI is suggested as an alternative to the original PRI formulation that could improve seasonal εcan estimations. The satellite observation geometry effects on the determination of εcan were not very intense for the studied ecosystem. However, the most effective viewing direction was proved to be the backward scattering, while zenith observations were the least efficient for the specific ecosystem, most probably due to increased background effects. Even though the sensitivity of the original PRI formulation to εcan was reduced in forward scattering viewing directions, when 531nm xanthophyll de-epoxidation band was replaced with higher wavelength bands (540–550nm), a strong PRI–εcan relationship reappeared. These findings indicate possible shift of xanthophyll de-epoxidation signal according to viewing direction.

Evaluating total inorganic nitrogen in coastal waters through fusion of multi-temporal RADARSAT-2 and optical imagery using random forest algorithm

Satellites routinely provide frequent, large-scale, near-surface views of many oceanographic variables pertinent to plankton ecology. However, the nutrient fertility of water can be challenging to detect accurately using remote sensing technology. This research has explored an approach to estimate the nutrient fertility in coastal waters through the fusion of synthetic aperture radar (SAR) images and optical images using the random forest (RF) algorithm. The estimation of total inorganic nitrogen (TIN) in the Hong Kong Sea, China, was used as a case study. In March of 2009 and May and August of 2010, a sequence of multi-temporal in situ data and CCD images from China's HJ-1 satellite and RADARSAT-2 images were acquired. Four sensitive parameters were selected as input variables to evaluate TIN: single-band reflectance, a normalized difference spectral index (NDSI) and HV and VH polarizations. The RF algorithm was used to merge the different input variables from the SAR and optical imagery to generate a new dataset (i.e., the TIN outputs). The results showed the temporal-spatial distribution of TIN. The TIN values decreased from coastal waters to the open water areas, and TIN values in the northeast area were higher than those found in the southwest region of the study area. The maximum TIN values occurred in May. Additionally, the estimation accuracy for estimating TIN was significantly improved when the SAR and optical data were used in combination rather than a single data type alone. This study suggests that this method of estimating nutrient fertility in coastal waters by effectively fusing data from multiple sensors is very promising.

Exploring novel bands and key index for evaluating leaf equivalent water thickness in wheat using hyperspectra influenced by nitrogen.

Leaf equivalent water thickness (LEWT) is an important indicator of crop water status. Effectively monitoring the water status of wheat under different nitrogen treatments is important for effective water management in precision agriculture. Trends in the variation of LEWT in wheat plants during plant growth were analyzed based on field experiments in which wheat plants under various water and nitrogen treatments in two consecutive growing seasons. Two-band spectral indices [normalized difference spectral indices (NDSI), ratio spectral indices (RSI), different spectral indices (DSI)], and then three-band spectral indices were established based on the best two-band spectral index within the range of 350–2500 nm to reduce the noise caused by nitrogen and saturation. Then, optimal spectral indices were selected to construct models of LEWT monitoring in wheat. The results showed that the two-band spectral index NDSI(R1204, R1318) could be used for LEWT monitoring throughout the wheat growth season, but the model performed differently before and after anthesis. Therefore, further two-band spectral indices NDSIb(R1445, R487), NDSIa(R1714, R1395), and NDSI(R1429, R416), were constructed for the two developmental phases, with NDSI(R1429, R416) considered to be the best index. Finally, a three-band index (R1429−R416−R1865)/(R1429+R416+R1865), which was superior for monitoring LEWT and reducing the noise caused by nitrogen, was formed on the best two-band spectral index NDSI(R1429, R416) by adding the 1,865 nm wavelenght as the third band. This produced more uniformity and stable performance compared with the two-band spectral indices in the LEWT model. The results are of technical significance for monitoring the water status of wheat under different nitrogen treatments in precision agriculture.

Monitoring arsenic contamination in agricultural soils with reflectance spectroscopy of rice plants.

The objective of this study was to explore the feasibility and to investigate the mechanism for rapidly monitoring arsenic (As) contamination in agricultural soils with the reflectance spectra of rice plants. Several data pretreatment methods were applied to improve the prediction accuracy. The prediction of soil As contents was achieved by partial least-squares regression (PLSR) using laboratory and field spectra of rice plants, as well as linear regression employing normalized difference spectral index (NDSI) calculated from fild spectra. For laboratory spectra, the optimal PLSR model for predicting soil As contents was achieved using Savitzky-Golay smoothing (SG), first derivative and mean center (MC) (root-mean-square error of prediction (RMSEP)=14.7 mg kg(-1); r=0.64; residual predictive deviation (RPD)=1.31). For field spectra, the optimal PLSR model was also achieved using SG, first derivative and MC (RMSEP=13.7 mg kg(-1); r=0.71; RPD=1.43). In addition, the NDSI with 812 and 782 nm obtained a prediction accuracy with r=0.68, RMSEP=13.7 mg kg(-1), and RPD=1.36. These results indicated that it was feasible to monitor the As contamination in agricultural soils using the reflectance spectra of rice plants. The prediction mechanism might be the relationship between the As contents in soils and the chlorophyll-a/-b contents and cell structure in leaves or canopies of rice plants.

Comparative Analysis of Normalised Difference Spectral Indices Derived from MODIS for Detecting Surface Water in Flooded Rice Cropping Systems

Identifying managed flooding in paddy fields is commonly used in remote sensing to detect rice. Such flooding, followed by rapid vegetation growth, is a reliable indicator to discriminate rice. Spectral indices (SIs) are often used to perform this task. However, little work has been done on determining which spectral combination in the form of Normalised Difference Spectral Indices (NDSIs) is most appropriate for surface water detection or which thresholds are most robust to separate water from other surfaces in operational contexts. To address this, we conducted analyses on satellite and field spectral data from an agronomic experiment as well as on real farming situations with different soil and plant conditions. Firstly, we review and select NDSIs proposed in the literature, including a new combination of visible and shortwave infrared bands. Secondly, we analyse spectroradiometric field data and satellite data to evaluate mixed pixel effects. Thirdly, we analyse MODIS data and Landsat data at four sites in Europe and Asia to assess NDSI performance in real-world conditions. Finally, we test the performance of the NDSIs on MODIS temporal profiles in the four sites. We also compared the NDSIs against a combined index previously used for agronomic flood detection. Analyses suggest that NDSIs using MODIS bands 4 and 7, 1 and 7, 4 and 6 or 1 and 6 perform best. A common threshold for each NDSI across all sites was more appropriate than locally adaptive thresholds. In general, NDSIs that use band 7 have a negligible increase in Commission Error over those that use band 6 but are more sensitive to water presence in mixed land cover conditions typical of moderate spatial resolution analyses. The best performing NDSI is comparable to the combined index but with less variability in performance across sites, suggesting a more succinct and robust flood detection method.

基于光谱指数的植物叶片叶绿素含量的估算模型

叶绿素是光合作用能力和植被发育阶段的指示器，是监测湿地植被生长健康状况的重要指标之一；高光谱遥感技术可以为植物叶绿素含量的定量化诊断提供简便有效、非破坏性的数据采集和处理方法。为保证被探测叶片面积相同，消除背景反射、叶片表面弯曲造成的光谱波动及叶片内部变异造成的影响，研究采用Field Spec 3光谱仪加载手持叶夹式叶片光谱探测器，测定野鸭湖湿地典型植物的叶片高光谱反射率数据，同时通过分光光度计室内测定相应叶片的叶绿素含量。采用相关性及单变量线性拟合分析技术，建立二者的关系模型，包括叶绿素含量与"三边"参数的相关模型以及比值光谱指数（SR）模型和归一化差值光谱指数（ND）模型，并采用交叉检验中的3K-CV方法对估算模型进行模型精度检验。结果表明：植物叶片叶绿素含量与"三边"参数大多都呈极显著相关，相关系数最大达到0.867；计算光谱反射率组成的比值（SR）和归一化（ND）光谱指数与叶绿素含量的决定系数，总体相关性比较高，较好的波段组合均为550-700nm与700-1400nm以及550-700nm与1600-1900nm，与叶绿素含量相关性最好的指数分别是SR（565nm，740nm）和ND（565nm，735nm）。并通过选取相关性最佳的光谱特征参数，分别基于"三边"参数和ND模型指数构建了植物叶片叶绿素含量的估算模型。其中，基于红边位置（WP_r）光谱特征参数和ND（565nm，735nm）光谱指数建立的叶绿素含量估算模型，取得了较好的测试效果，检验拟合方程的决定系数（<em>R</em>²）都达到0.8以上，估算模型分别为<em>y</em>=0.113<em>x</em>-78.74，<em>y</em>=5.5762<em>x</em>+4.4828。通过3K-CV方法进行测试和检验，植物叶绿素含量估算模型均取得了较为理想的预测精度，预测精度的分别为93.9%及90.7%。高光谱遥感技术对植被进行微弱光谱差异的定量分析，在植被遥感研究与应用中表现出强大优势，为植物叶绿素含量诊断中的实际应用提供了重要的理论依据和技术支持。;Chlorophyll can be an indicator in photosynthesis capacity and vegetation developmental stages,which is also one of important indicators to monitor health status of wetland vegetation growth. Hyperspectral remote sensing technology can provide a simple, effective and non-destructive data acquisition, which can offer processing method for quantifying diagnosis plant chlorophyll content as well. This study used the Fieldspec 3 spectrometer and a plant probe leaf clip spectral detector to guarantee the spectrum are detected in the same area of the leaf, it is also eliminating the background reflectance, spectral fluctuations caused by bending of the blade surface and the impact caused by leaf internal variability. This study determined the typical wetland plants leaf hyperspectral reflectance data at Wild Duck Lake, and at the same time the corresponding leaf chlorophyll content was measured using a spectrophotometer indoor. The relationship between chlorophyll content and the Trilateral parameters, as well as the ratio of spectral index model (SR) and normalized difference spectral index (ND) were established respectively using linear regression model., then 3-Fold Cross Validation(3K-CV) was used to test the accuracy of the estimation model. The results showed that most of the trilateral parameters were significantly correlated with plant leaf chlorophyll content; the maximum correlation coefficient reached 0.867. The correlation coefficient between ratio (SR) and normalized (ND) and chlorophyll content were high in general. Suitable band combinations were 550-700 nm,700-1400nm, 550-700 nm and 1600-1900 nm. The best indices with highest correlation with chlorophyll content were SR (calculated from bands 565 nm and 740 nm) and ND (calculated from bands 565 nm and 735 nm). And then by choosing the best correlation spectrum characteristic parameters based on the Trilateral parameters and ND model index, a plant chlorophyll estimation model was constructed. Among them,a chlorophyll content estimation model established by Red edge position (WP_r) of spectral characteristic parameters and ND (565nm, 735nm) spectral index achieved better test results, and <em>R</em>² both reached above 0.8, the estimation model were <em>y</em>=0.113<em>x</em>-78.74, <em>y</em>=5.5762<em>x</em> +4.4828. Using 3K-CV method for testing and validation, the prediction accuracies of both plant leaf chlorophyll content estimation models were 93.9% and 90.7%, respectively. The quantitative analysis of hyperspectral remote sensing technology shows a strong advantage in detecting vegetation weak spectral differences and provides an important theoretical basis and technical support for the practical application in the diagnosis of plant chlorophyll content.

Improved land cover mapping using high resolution multiangle 8-band WorldView-2 satellite remote sensing data

Here, we discuss the improvements in urban classification that were made using the spatial-spectral-angular information from a WorldView-2 (WV-2) multiangle image sequence. In this study, we evaluate the use of multiangle high resolution WV-2 panchromatic (PAN) and multispectral image (MSI) data for extracting urban geospatial information. Current multiangular WV-2 data were classified into misclassification-prone surfaces, such as vegetation, water bodies, and man-made features, using a cluster of normalized difference spectral index ratios (SIR). A novel multifold methodology protocol was designed to estimate the consequences of multiangularity and germane PAN-sharpening algorithms on the spectral characteristics (distortions) of satellite data and on the resulting land use/land cover (LU/LC) mapping using an array of SIRs. Eight existing PAN-sharpening algorithms were used for data fusion, followed by estimation of multiple SIRs to mitigate spectral distortions arising from the multiangularity of the data. This research highlights the benefits of using traditional PAN-sharpening techniques with a specific set of SIRs on land cover mapping based on five available tiles of satellite data. The research provides a method to overcome the atmospherically triggered spectral distortions of multiangular acquisitions, which will facilitate better mapping and understanding of the earth’s surface.

A spectral index ratio-based Antarctic land-cover mapping using hyperspatial 8-band WorldView-2 imagery

This study evaluates the potential of 8-band high resolution WorldView-2 (WV-2) panchromatic (PAN) and multispectral image (MSI) data for the extraction of polar geospatial information. We introduce a novel method based on a customized set of normalized difference Spectral Index Ratios (SIRs), incorporating multiple bands, to improve the accuracy of land-cover mapping in the Antarctic. Most recently available WV-2 data are classified into land-cover surfaces such as snow/ice, water bodies, and landmass using the customized normalized difference SIRs. A novel multi-fold methodology is used to evaluate the effect of pan-sharpening algorithms on spectral characteristics of satellite data, and on subsequent land-cover mapping using an array of SIRs. A set of existing pan-sharpening algorithms was implemented in order to fuse PAN with MSI data, followed by estimation of multiple SIRs to extract target land-cover classes. These algorithms were compared on the basis of their effectiveness in extracting target classes using a defined set of SIRs. Our results indicate that the use of 8-band WV-2, customized SIRs, and appropriate pan-sharpening can greatly improve the extraction of land-cover information.

Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements

Timely assessment of canopy nitrogen content (CNC) in crops is critical for growth diagnosis and precision management of crops to generate higher yield and better quality while also minimizing adverse environmental impacts. The objective of this study was to determine the most suitable algorithm, using hyperspectral reflectance data, for the regional assessment of CNC at a critical growth stage in paddy rice. Ground-based hyperspectral datasets were obtained during the panicle formation stage under a wide range of plant and environmental conditions in Japan and China using spectroradiometers. A hyperspectral airborne dataset was obtained over a typical rice-growing region in Japan using the Compact Airborne Spectrographic Imager 3 (CASI-3). On the basis of a comprehensive analysis of the hyperspectral data, significant spectral indices (SIs) such as the normalized difference spectral index (NDSI) and ratio spectral index (RSI) were explored to provide an accurate and robust assessment of CNC. The capability of multivariable regression approaches, such as partial least-squares regression (PLSR) using the whole hyperspectral data or interval PLSR (IPLSR) using selected wavebands was also examined. Among various SIs, a simple index, RSI (D740, D522) using the first derivative (D) values at 740nm and 522nm, was found to be most accurate and robust for the assessment of CNC. The predictive ability of the index was comparable to those of PLSR and IPLSR. Independent validation using the airborne dataset supported the robust applicability of the new SI. The CNC was closely related to the conventional diagnostic indicators based on direct plant measurements. The results demonstrated the operational applicability of hyperspectral measurements for diagnostic mapping of CNC on a regional scale. The investigation based on the precise dataset for rice will be a good basis for remote sensing of canopy nitrogen content in a wide range of vegetation.

FUSION OF OPTICAL DATA AND SAR DATA FOR THE ESTIMATION OF NITROGEN CONCENTRATION IN PEARL RIVER ESTUARY HONG KONG SEAS, CHINA

Abstract. The knowledge of nitrogen concentration in the ocean is fundamental for the study of oceanic biogeochemical processes. The objective of this research is to estimate total inorganic nitrogen (TIN) by integrating optical parameters from HJ-1 CCD image and polarization parameters from RADARSAT-2 quad-polarization image. The situ data and HJ-1 CCD, RADARSAT-2 image were acquired from Pearl River Estuary Hong Kong Seas, China in August, 2010. The four sensitive parameters, reflectance of Band 4, NDSI (Normalized Difference Spectral Index), the backscattering coefficient of HV and VH were derived as input variables to assess the TIN. A multiple regression model was established between four input variables and TIN. The result showed that the fusion of optical data and SAR data was proved to be successful in estimating TIN in sea surface, with the correlation coefficient (R2) between measured TIN and predicated TIN of 0.774, and the root mean square error (RMSE) of 0.063. The optical data in combination with SAR data is promising for detecting biochemical component in sea surface.

Monitoring the leaf water content and specific leaf weight of cotton (Gossypium hirsutum L.) in saline soil using leaf spectral reflectance

The objectives of the present study are to determine the relationships of equivalent water thickness (EWT), fuel moisture content (FMC) and specific leaf weight (SLW) of cotton leaves with leaf spectra reflectance, and to find sensitive spectral bands and best spectral indices to establish quantitative models for the quick and accurate estimation of EWT, FMC and SLW in cotton plants under different salinity levels. Plot experiments were conducted at different levels of salinity and cotton cultivars during three consecutive growing seasons. Time-course measurements of the leaf spectral reflectance, leaf fresh weight, leaf dry weight, leaf area, and leaf ion content of cotton were recorded under various treatments. Then, the normalized difference spectral indices (NDSI) and ratio spectral indices (RSI) based on the leaf spectrum were obtained within 350–2500nm, and their correlation with EWT, FMC and SLW were quantified. The results show that the EWT, FMC and SLW of cotton leaves increased with increasing soil salinity levels and that the changes in leaf spectral reflectance under varied salinity levels are highly significant, with consistent patterns across the two cultivars tested. As the soil salinity levels increased, the Na+, Cl−, and SO42− content in the cotton leaves increased, whereas K+ and Ca2+ decreased at the same growth stage. In addition, the relationships among ion content, EWT, FMC and SLW were significant (P<0.01). The sensitive spectral bands for EWT, FMC and SLW occurred mainly within the near infrared (NIR) and short-wave infrared (SWIR) ranges. The best spectral indices for estimating EWT, FMC and SLW in cotton were found to be NDSI (R1347, R2307), RSI (R2307, R1347); NDSI (R1650, R1801), RSI (R1801, R1650); NDSI (R1300, R2308), RSI (R2307, R1347) and 1650/2220nm ratio, and the regression models based on the above spectral indices were identified as the best equations for the effective estimation of EWT and SLW in cotton. After testing these derived equations, the models for EWT and SLW estimation based on NDSI and RSI yielded an R2 of over 0.73, with more satisfactory performance under different ecological conditions, but the estimated accuracies of FMC models were very low and may not be suitable for estimating vegetation water content in saline conditions from leaf-level reflectance. The high fit between the measured and estimated values indicates that the EWT and SLW models based on new spectral indices from leaf-level reflectance could be used for the indirect estimation of plant salinity status by monitoring the changes in EWT and SLW caused by soil salinity in cotton plants.

基于湿地植物光谱的水体总氮估测

利用再生水补充城市湿地是目前湿地恢复与重建的主要方向,然而再水中高浓度的氮、磷含量极易导致水体富营养化。遥感技术已成为富营养化监测的重要手段,但对于植被覆盖水域的富营养化直接探测存在一定的局限性。以北京市典型再生水补水湿地奥林匹克公园南园湿地为研究区,利用湿地植物光谱进行水体富营养化主控因子总氮的遥感探测。测定芦苇(<em>Phragmites australis</em>)和香蒲(<em>Typha angustifolia</em>)的叶片光谱及水体总氮含量,在对数据进行预处理的基础上建立二者的关系模型,包括单变量模型(比值光谱指数(SR)模型和归一化差值光谱指数(ND)模型),与多变量模型(逐步多元线性回归(SMLR)模型和偏最小二乘回归(PLSR)模型),并利用交叉验证决定系数(<em>R</em>_cv²)和均方根误差(RMSE_cv)进行模型精度检验。结果表明,不同回归模型相比,多变量回归模型精度较高;多变量回归模型中,PLSR模型精度较高,<em>R</em>_cv²可达0.72,RMSE_cv仅为0.24,是建立湿地植物光谱与水体总氮含量关系的最优模型。不同湿地植物类型相比,利用芦苇反射光谱建立的各种预测模型的精度都高于香蒲。其他环境因子(总磷)也是影响TN含量与湿地植物反射光谱关系的重要因素。研究成果可以弥补现有水体富营养化遥感探测的不足,并为再生水利用的城市湿地水质监测与管理提供有力的科学依据。;Supplying urban wetlands with reclaimed water is recognized as a superior way for wetland restoration and reconstruction. However, the high concentration of nitrogen and phosphorus in reclaimed water can easily lead to water eutrophication. Although remote sensing technology has become a useful tool to monitor the eutrophication of water body, it is usually employed to detect eutrophication in open water. Limited applications have been found in measuring eutrophication of wetland covered by vegetation. Utilizing plants spectral response to environment can monitor environmental changes. This study explores the possibility to use wetland vegetation reflectance spectra in estimating total nitrogen content which is one of the key indicators of water eutrophication. The South Wetland in the Olympic Park in Beijing, a typical wetland using reused water, was selected as our study area. The leaf reflectance spectra of main wetland plants, reed (<em>Phragmites australis</em>) and cattail (<em>Typha angustifolia</em>), were acquired by means of an ASD FieldSpec 3 spectrometer (350-2500nm). Water quality samples were collected at the same time and analyzed by Center for Environmental Quality Test, Tsinghua University subsequently. The research established several univariate models including simple ratio spectral index (SR) model and normalized difference spectral index (ND) model, as well as multivariate models including stepwise multiple linear regression (SMLR) model and partial least squares regression (PLSR) model. The accuracy of these models was tested through cross-validated coefficient of determination (<em>R</em>_cv²) and cross-validated root mean square error (RMSE_cv). The results have shown that 1) In comparison with univariate techniques, multivariate regressions can improve the estimation of total nitrogen concentration in water. The accuracy of PLSR model was the highest (<em>R</em>_cv²=0.72, RMSE_cv=0.24) among all models. PLSR provides the most useful explorative tool for unraveling the relationship between spectral reflectance of wetland plants and total nitrogen content in water at leaf scale. 2) The accuracy of prediction models built in this study using <em>Phragmites australis</em> reflectance spectra is higher than those using <em>Typha angustifolia</em> reflectance spectra. 3) Other environmental factors should also be discreetly considered in modeling exercise. Total phosphorus is found to have impact on the relationship between TN and reflectance spectra from wetland vegetation. Strong predictive power for multiple regression equations has been achieved when the range of total phosphorus was restricted. The result from this study can not only fill the gaps in the detection of eutrophication using remote sensing, but also provide a strong scientific basis for the water quality monitoring and management of urban wetlands using recycled water.

Correlation between soil apparent electroconductivity and plant hyperspectral reflectance in a managed wetland

The apparent electrical conductivity (σa) of soil is influenced by a complex combination of soil physical and chemical properties. For this reason, σa is proposed as an indicator of plant stress and potential community structure changes in an alkaline wetland setting. However, assessing soil σa is relatively laborious and difficult to accomplish over large wetland areas. This work examines the feasibility of using the hyperspectral reflectance of the vegetation canopy to characterize the σa of the underlying substrate in a study conducted in a Central California managed wetland. σa determined by electromagnetic (EM) inductance was tested for correlation with in-situ hyperspectral reflectance measurements, focusing on a key waterfowl forage species, swamp timothy (Crypsis schoenoides). Three typical hyperspectral indices, individual narrow-band reflectance, first-derivative reflectance and a narrow-band normalized difference spectral index (NDSI), were developed and related to soil σa using univariate regression models. The coefficient of determination (R 2) was used to determine optimal models for predicting σa, with the highest value of R 2 at 2206 nm for the individual narrow bands (R 2 = 0.56), 462 nm for the first-derivative reflectance (R 2 = 0.59), and 1549 and 2205 nm for the narrow-band NDSI (R 2 = 0.57). The root mean squared error (RMSE) and relative root mean squared error (RRMSE) were computed using leave-one-out cross-validation (LOOCV) for accuracy assessment. The results demonstrate that the three indices tested are valid for estimating σa, with the first-derivative reflectance performing better (RMSE = 30.3 mS m−1, RRMSE = 16.1%) than the individual narrow-band reflectance (RMSE = 32.3 mS m−1, RRMSE = 17.1%) and the narrow-band NDSI (RMSE = 31.5 mS m−1, RRMSE = 16.7%). The results presented in this paper demonstrate the feasibility of linking plant–soil σa interactions using hyperspectral indices based on in-situ spectral measurements.

Potential for spectral indices to remotely sense phosphorus and potassium content of legume-based pasture as a means of assessing soil phosphorus and potassium fertility status

Precision nutrient management needs analytical tools that aid collection of site-specific data. Adequate soil phosphorus (P) and potassium (K) fertility is crucial for pasture production in New Zealand. This article explores (a) the relationship between 12 spectral indices from in situ canopy reflectance and pasture growth rate (PGR), and pasture P and K content in pastures, (b) the performance of the model in different seasons and (c) the relationship between sensed pasture P and K content and soil P (Olsen P) and K (exchangeable K) fertility. Hyperspectral data were collected from a small area of each of 30 legume-based pastures that varied in soil P (Olsen P 5–72 mg kg−1) and soil exchangeable K (0.20−1.32 cmol kg−1) in spring 2004 and again in summer 2006. Overall, the photochemical reflectance index (PRI) showed the best coefficients of determination (R 2) for most variables. In an exploratory analysis using all the spectral waveband data, normalized difference spectral indices (NDSIs) using the combination of reflectance at 523 and 583 nm of the pasture canopy gave the best prediction of soil P and exchangeable K status. The prediction of Olsen P from plant P (R 2 > 0.89) and soil K from plant K (R 2 > 0.73) was achieved through fitted logarithmic functions that linked plant P and K to soil P and K status, respectively. This pilot study has been broadened to examine other methodologies for interpreting the spectral data and extended to other pasture types and soil orders.

Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite hyperspectral imagery: An application on a Phlomis fruticosa Mediterranean ecosystem using multiangular CHRIS/PROBA observations

This study focuses on the potential of satellite hyperspectral imagery to monitor vegetation biophysical and biochemical characteristics through narrow-band indices and different viewing angles. Hyperspectral images of the CHRIS/PROBA sensor in imaging mode 1 (5 observation angles, 62 bands, 410–1005 nm) were acquired throughout a two-year period for a Mediterranean ecosystem fully covered by the semi-deciduous shrub Phlomis fruticosa. During each acquisition, coincident ecophysiological field measurements were conducted. Leaf area index (LAI), leaf biochemical content (chlorophyll a, chlorophyll b, carotenoids) and leaf water potential were measured. The hyperspectral images were corrected for coherent noises, cloud and atmosphere, in order to produce ground reflectance images. The reflectance spectrum of each image was used to calculate a variety of vegetation indices (VIs) that are already published in relevant literature. Additionally, all combinations of the 62 bands were used in order to calculate Normalized Difference Spectral Indices (NDSI ( x, y) ) and Simple Subtraction Indices (SSI ( x, y )). The above indices along with raw reflectance and reflectance derivatives were examined for linear relationship with the ground-measured variables and the strongest relationships were determined. It is concluded that higher observation angles are better for the extraction of biochemical indices. The first derivative of the reflectance spectra proved to be very useful in the prediction of all measured variables. In many cases, complex and improved spectral indices that are proposed in the literature do not seem to be more accurate than simple NDSIs such as NDVI. Even traditional broadband NDVI is proved to be adequate in LAI prediction, while green bands seem also very useful. However, in biochemical estimation narrow bands are necessary. Indices that incorporate red, blue and IR bands, such as PSRI, SIPI and mNDVI presented good performance in chlorophyll estimation, while CRI did not show any relevance to carotenoids and WI was poorly correlated to water potential. Moreover, analyses indicated that it is very important to use a near red-edge band (701 nm) for effective chlorophyll index design. SSIs that incorporate 701 nm with 511 or 605 nm showed best performance in chlorophyll determination. For carotenoid estimation, a band on the edge of carotenoid absorption (511 nm) combined with a red band performed best, while a normalized index of two water absorption bands (945, 971 nm) proved to be an effective water index. Finally, the attempt to investigate stress conditions through pigment ratios resulted in the use of the band centred at 701 nm.

Study for estimation of rice grain protein contents using hyperspectral data

Ministry of Economy, Trade and Industry (METI) of Japan is developing the hyperspectral sensor that is planned to be installed in the next generation earth observation satellite. Hyperspectral sensor is able to collect information as a set of images in high spectral resolution. It has begun to spread into fields of agriculture and environmental surveillance in Japan.The aim of this study is to study the estimation formula of rice grain protein contents using hyperspectral data. Especially, it aimed to standardize the estimation formula for the same observation area and the same rice species.We investigated the standardized estimation formula of rice grain protein contents as paddy growing information through two approaches. One is to use the vegetation indices as biophysical indicator in previous research. Another one is to use Normalized Difference Spectral Index (NDSI). For the estimation rice grain protein contents in Osaki city, Miyagi prefecture, the standardized estimation formula in year of 2007, 2008, and 2009 was obtained. Obtained formula uses ratio of R460 and R510 as independent variable and coefficient of determination R2 is 0.72.

Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat

Hyperspectral sensing can provide an effective means for fast and non-destructive estimation of leaf nitrogen (N) status in crop plants. The objectives of this study were to design a new method to extract hyperspectral spectrum information, to explore sensitive spectral bands, suitable bandwidth and best vegetation indices based on precise analysis of ground-based hyperspectral information, and to develop regression models for estimating leaf N accumulation per unit soil area (LNA, gNm−2) in winter wheat (Triticum aestivum L.). Three field experiments were conducted with different N rates and cultivar types in three consecutive growing seasons, and time-course measurements were taken on canopy hyperspectral reflectance and LNA under the various treatments. Then, normalized difference spectral indices (NDSI) and ratio spectral indices (RSI) based on the original spectrum and the first derivative spectrum were constructed within the range of 350–2500nm, and their relationships with LNA were quantified. The results showed that both LNA and canopy hyperspectral reflectance in wheat changed with varied N rates, with consistent patterns across different cultivars and seasons. The sensitive spectral bands for LNA existed mainly within visible and near infrared regions. The best spectral indices for estimating LNA in wheat were found to be NDSI (R860, R720), RSI (R990, R720), NDSI (FD736, FD526) and RSI (FD725, FD516), and the regression models based on the above four spectral indices were formulated as Y=26.34x1.887, Y=5.095x−6.040, Y=0.609e3.008x and Y=0.388x1.260, respectively, with R2 greater than 0.81. Furthermore, expanding the bandwidth of NDSI (R860, R720) and RSI (R990, R720) from 1nm to 100nm at 1nm interval produced the LNA monitoring models with similar performance within about 33nm and 23nm bandwidth, respectively, over which the statistical parameters of the models became less stable. From testing of the derived equations, the model for LNA estimation on NDSI (R860, R720), RSI (R990, R720), NDSI (FD736, FD526) and RSI (FD725, FD516) gave R2 over 0.79 with more satisfactory performance than previously reported models and physical models in wheat. It can be concluded that the present hyperspectral parameters of NDSI (R860, R720), RSI (R990, R720), NDSI (FD736, FD526) and RSI (FD725, FD516) can be reliably used for estimating LNA in winter wheat.

Reflectance characteristics of major land surfaces in slash‐and‐burn ecosystems in Laos

The in situ reflectance spectra in the 400–2500 nm wavelength region were obtained using a portable radiometer over a range of land surfaces including burnt fields, crop canopies, and fallow vegetation at different community ages in slash‐and‐burn ecosystems in Laos. Normalized difference spectral indices (NDSI[i,j] = [Rj −Ri ]/[Rj +Ri ]) were derived using reflectance Ri and Rj at i and j nm wavelengths for a thorough combination (14 706 pairs) of 172 wavebands (10‐nm resolution). The separability of burnt fields from dry/senescent vegetation was highest at NDSI[1090, 2390], whereas it was highly discriminated from fallow and crop vegetation by NDSI[760, 1970]. NDSIs using 730–760 nm with 1970–1990 nm showed the largest differences between dry/senescent vegetation and fallow or crop vegetation. None of the NDSIs was useful in discriminating between fallow and crop vegetations or between slashed/senescent vegetation and crop residue/abandoned field. Community age and biomass of fallow vegetation could not be inferred directly from spectral information, since no NDSIs showed any significant differences among crop and fallow vegetation that had a large variability in the amount of green vegetation. Results would provide useful information for various applications of optical satellite sensor images especially in assessments of land use or post‐fire regeneration of vegetation.

Traceability of slash‐and‐burn land‐use history using optical satellite sensor imagery: a basis for chronosequential assessment of ecosystem carbon stock in Laos

This study examined the use of satellite sensor imagery for chronosequential assessment of land use and ecosystem carbon stock in slash‐and‐burn (S/B) regions of Laos. The segmentation approach was useful because the boundaries of S/B patches are subject to change due to natural or anthropogenic factors. Polygon‐based classification using six optical bands of Landsat Enhanced Thematic Mapper Plus (ETM+) imagery showed that S/B patches could be discriminated with high accuracy (0.98). Normalized difference spectral indices, NDSI[i, j] = [Rj −Ri ]/[Rj +Ri ], using reflectances Rj and Ri at j and i nm wavelengths for S/B polygons during four consecutive years (1999–2002) showed that NDSI[2215, 830], NDSI[1650, 830] and NDSI[660, 830] ( = the normalized difference vegetation index, NDVI) values decreased significantly in S/B years compared to those under fallow conditions (by 0.21±0.04, 0.20±0.04 and 0.17±0.03, respectively). Only slight differences were found before and after the S/B year, regardless of fallow length or biomass estimated by the allometry method. Relating reflectance signatures directly to fallow biomass was unsuitable, but these NDSIs were also useful for distinguishing S/B patches. Land‐use history, including the community age of fallow vegetation, can be traced on a pixel basis using a superimposed set of segmented classified images.