FieldSpec Spectroradiometer Research Articles

Spectral and Numerical Analysis of Hyper spectral Data using Vegetation Indices

Indian economy is majorly influenced by Agriculture and its allied sectors. More than 50% of the population of India is dependent on agriculture and its allied sectors for their survival. According to the Report of Indian Council of Agricultural Research, 30-35% of the crop yield gets wasted due to disease. Using modern day remote sensing techniques plant health can be monitored and it can be specified whether a plant is diseased or healthy. Hyperspectral Remote Sensing is the technique by which fine and minute information of vegetation can be obtained with the help of narrow wavebands. Data of 80 leaf samples of Tomato crop collected in spectra form and text form using ASD FieldSpec4 spectroradiometer and ViewSpec PRO. This information of plant leaves was used to identify vegetation attributes and its status. Vegetation Indices are calculated using mathematical formulae published in the previous study. Random forest classification used to discriminate among Healthy and Diseased plants. Algorithm works with an accuracy of 93.75% with misclassification rate 0.0625. Along with Green wavelength range and Red edge of the spectrum, specific disrupted behavior was observed in Shortwave Infrared Region of the spectra. The research paper focuses on Spectral and Numerical study and analysis of Tomato Leaf disease with the help of ASD FieldSpec4.

Water Column Effects on In Situ Spectral Signatures of Coral Species from Gujarat Coast, India

Scleractinian corals represent the main functional group in a coral reef ecosystem. So far, very few studies have ever reported how an in situ coral spectra changes with increasing water column purely in a natural environment. This paper reports the results of a unique field experiment wherein water column effects were observed over time on in situ hyperspectral signatures (over a continuous spectral range 400–1350 nm) of three live coral species from Gujarat coast of India. In situ hyperspectral measurements were taken with Analytical Spectral Devices (ASD) FieldSpec3 spectroradiometer having full spectral range of 350–2500 nm and spectral resolution of 3 nm (at 700 nm) and 10 nm (at 1400, 2100 nm). Three coral species, Favia favus, Goniopora planulata and Turbinaria peltata, were selected as case representatives for this study. In exposed condition, all three corals exhibit brown mode of coral reflectance with characteristic triple peaks at 575, 600 and 650 nm. It is observed that water column attenuates the original signature of corals by enhancing the spectral magnitude in the visible region and in inverse fashion in the infrared region. The first derivative spectra clearly bring out the sensitivity of 840-nm channel towards water column. The water column height at which the triple peak pattern gets obscured varies for the three coral species. The spectral reflectance of coral as a bottom substrate continues to remain strong between 700–720 and 1050–1080 nm even in marginally submerged condition.

Partial sub-pixel and pixel-based alteration mapping of porphyry system using ASTER data: regional case study in western Yazd, Iran

ABSTRACT This study uses remote sensing image-processing to enhance recognition of areas with mineral abundance halo, which can be associated with porphyry mineralisation of the Urumieh-Dokhtar Magmatic Belt (UDMB) in western Yazd. Granitoid intrusions of Oligocene-Miocene age in UDMB hosts the most important Porphyry Copper Deposits (PCDs) of Iran such as Darezereshk and Aliabad PCDs. Diagnostic spectral absorption features of six shortwave infrared (SWIR) and three visible and near-infrared (VNIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were applied to determine the alteration minerals and their abundance. In this research reflectance data from various representative alteration types throughout the study area were measured by the FieldSpec-3 spectroradiometer and applied for mapping minerals. Different remote sensing analytical techniques, including the Principal Component Analysis (PCA), band ratio, relative absorption band depth, and MTMF, were applied to enhance mapping the alteration minerals. Field works including rock sampling, petrography and field spectroradiometer followed up the alteration mapping. Field observation implies that, the vein-type mineralisation above the cupola of this porphyry system is preserved in the western part, although it was eroded in the eastern area. Consequently, PCDs potential is expected to be higher in the western area.

Identification of mangrove plant using hyperspectral remote sensing data along the Red Sea, Egypt

The mangrove ecosystem is one of the most productive habitats that support many marine species and its adaptation to adverse environmental conditions, increase the demand to map, manage and monitor this ecosystem. The presence of hyperspectral remote sensing techniques can potentially improve the ability to measure the spectral signature of mangrove to differentiate mangrove from the other vegetation and to get detailed information about this ecosystem. This study has been carried out for mapping, monitoring and managing the Red Sea mangrove ecosystems through measuring their spectral properties using advanced hyperspectral remote sensing techniques. The spectral signature data were measured using Analytical Spectral Devices (ASD) Fieldspec spectroradiometer on November 2016 then the data were tested using statistical measures namely One-way Analysis of Variance (ANOVA) along with Tukey’s HSD test. The hyperspectral signatures of A. marina mangrove at the different sites showed that mangroves recorded a high reflectance at the visible and NIR region of the spectrum than the other regions and there are similarities at certain wavelengths and some differences at other wavelengths used for differentiation between mangroves in various environments. ANOVA and Tukey’s HSD test results showed that NIR region is the best region for the differentiation of mangrove from the other vegetation.

Aster Mapping of Limestone Deposits and Associated Lithounits of Parts of Chikkanayakanahalli, Southern Part of Chitradurga Schist Belt, Dharwar Craton, India

Economically viable limestone deposits are mostly formed by calcite minerals, and these minerals are widely used in manufacturing of cement, mortar, fertilizer and flux for smelting of iron ores, and mapping of such deposits is significant and important in scientific research. This study examines the capability of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) with the wavelength range visible–near-infrared and short-wave infrared spectral bands to map such limestone carbonate deposits and associated lithounits occurred in and around of Chikkanayakanahalli, southern part of the Chitradurga schist belt using minimum noise fraction (MNF) and decorrelation stretching methods. The study results that (1) the RGB image of MNF (R: B1; G: B2; B: B3) of ASTER is capable of discriminating the limestones and associated different rock types, namely banded magnetite quartzites (BMQ), graywackes, Mn- and Fe-rich cherts, metabasalts, granitic gneisses, granitoids and migmatites and (2) the decorrelation stretch image of ASTER bands 8, 3, 1 of the ASTER delineated clearly the limestones and associated rocks of the study area. Study of spectral signatures of field samples of such economic limestones in the wavelength of 350–2500 nm using Fieldspec3 Spectroradiometer showed the spectral absorption near 2.32 μm due to the presence of calcite minerals in the rocks. The results of study are cross-verified in the study area and confirmed through petrological and chemical analyses of the samples. This study bespeaks the potential of ASTER sensor and application of image processing methods to map the economic limestone deposits and associated rocks of the study area.

Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging

Soil Organic Carbon (SOC) is a useful representative of soil fertility and an essential parameter in controlling the dynamics of various agrochemicals in soil. Soil texture is also used to calculate soil's ability to retain water for plant growth. SOC and soil texture are thus important parameters of agricultural soils and need to be regularly monitored. Optical satellite remote sensing offers the potential for frequent surveys over large areas. In addition, the recently-operated Sentinel-2 missions provide free imagery. This study compared the capabilities of Sentinel-2 for monitoring and mapping of SOC and soil texture (clay, silt and sand content) with those obtained from airborne hyperspectral (CASI/SASI sensors) and lab ASD FieldSpec spectroradiometer measurements at four agricultural sites in the Czech Republic. Combination of 10 extracted bands of the Sentinel-2 and 18 spectral indices, as independent variables, were used to train prediction models and then produce spatial distribution maps of the selected attributes. Results showed that the prediction accuracy based on lab spectroscopy, airborne and Sentinel-2 in the majority of the sites was adequate for SOC and fair for clay; however, Sentinel-2 imagery could not be used to detect and map variations in silt and sand. The SOC and clay maps derived from the airborne and spaceborne datasets showed similar trend, with both performing better where SOC levels were relatively high, though at the highest levels Sentinel-2 was able to create the SOC map more precisely than the airborne sensors. Taken across all SOC levels measured in the reference data, Sentinel-2 results were marginally lower than lab spectroscopy and airborne imagery, but this reduction in precision may be offset by the extensive geographical coverage and more frequent revisit characteristic of satellite observation. The increased temporal revisit and area are expected to be positive enhancements to the acquisition of high-quality information on variations in SOC and clay content of bare soils.

Prediction of Sugarcane Yield Based on NDVI and Concentration of Leaf-Tissue Nutrients in Fields Managed with Straw Removal

The total or partial removal of sugarcane (Saccharum spp. L.) straw for bioenergy production may deplete soil quality and consequently affect negatively crop yield. Plants with lower yield potential may present lower concentration of leaf-tissue nutrients, which in turn changes light reflectance of canopy in different wavelengths. Therefore, vegetation indexes, such as the normalized difference vegetation index (NDVI) associated with concentration of leaf-tissue nutrients could be a useful tool for monitoring potential sugarcane yield changes under straw management. Two sites in São Paulo state, Brazil were utilized to evaluate the potential of NDVI for monitoring sugarcane yield changes imposed by different straw removal rates. The treatments were established with 0%, 25%, 50%, and 100% straw removal. The data used for the NDVI calculation was obtained using satellite images (CBERS-4) and hyperspectral sensor (FieldSpec Spectroradiometer, Malvern Panalytical, Almelo, Netherlands). Besides sugarcane yield, the concentration of the leaf-tissue nutrients (N, P, K, Ca, and S) were also determined. The NDVI efficiently predicted sugarcane yield under different rates of straw removal, with the highest performance achieved with NDVI derived from satellite images than hyperspectral sensor. In addition, leaf-tissue N and P concentrations were also important parameters to compose the prediction models of sugarcane yield. A prediction model approach based on data of NDVI and leaf-tissue nutrient concentrations may help the Brazilian sugarcane sector to monitor crop yield changes in areas intensively managed for bioenergy production.

Modeling soil organic matter and texture from satellite data in areas affected by wildfires and cropland abandonment in Aragón, Northern Spain

Land use/land cover (LULC) changes create the need for regular monitoring of soil properties. Modern technology and data sources offer possibilities to perform it. In this context, the objective of the study is to explore the possibility of predicting soil organic matter (SOM) content and texture from the spectral information of the three last generation satellites [Landsat-8, Sentinel-2, and the Environmental Mapping and Analysis Program (EnMAP)]. Soil samples (113) were collected in areas affected by wildfires and cropland abandonment in Aragon, Northern Spain. Reflectance spectra of soils were obtained in controlled laboratory conditions using the analytical spectral device Fieldspec4 spectroradiometer (spectral range 350 to 2500 nm). Reflectances simulated for Landsat-8, Sentinel-2, and EnMAP bands were used as predictors in multivariate models developed using partial least squares regression (PLSR) and step-down variable selection algorithm (SD). Modeling of all soil variables was performed simultaneously. The EnMAP models employed few predictors (10 to 58 of 244) and demonstrated good fit (Rcal2>0.8 and Rval2∼0.8), especially for SOM (Rcal2 and Rval2=0.9; RMSEP ∼1.6). Landsat models showed the least reliable estimates (Rcal2 0.54 to 0.77 and Rval2 0.42 to 0.80), whereas Sentinel-2 models showed Rcal2 of 0.70 to 0.81 and Rval2 between 0.67 (clay) and 0.79 (SOM). The results confirm high potential of spectral data from multispectral and hyperspectral satellites for soil monitoring. Application of PLSR combined with SD results in sparser and better-fit models. SOM and soil texture can be estimated with an acceptable accuracy from EnMAP and Sentinel-2 data enabling soil status monitoring in areas of wildfire burns and cropland abandonment.

Application of the empirical line method (ELM) to calibrate the airborne Daedalus-CZCS scanner

ABSTRACTThe present investigation aims at providing a reliable evaluation of the geometric and radiometric accuracy of the airborne Daedalus-Coastal Zone Color Scanner (CZCS), through cal/val techniques. To this aim, on November 2014, we accomplished a remote sensing campaign over the Peligna Valley (Italy). Together with the personnel of the III Nucleo Aereo of the Italian Coast Guard, based at the Pescara Airport, we deployed the Daedalus AA1268EM1 CZCS scanner on board the fixed-wing aircraft type ATR42MP and a ASD FieldSpec spectroradiometer for a simultaneous field survey. We used vicarious calibration and secondary non-parametric geometric correction to achieve absolute atmospheric correction and geometric calibration, respectively. Although the validation and calibration targets used in this study were similar in nature, correlation coefficients of the prediction equations between at-sensor radiance and ground reflectance were > 0.90 for each of the 10 CZCS wavebands and the independent error assessment demonstrate that the empirical line method can be applied to correct CZCS imagery with satisfactory results.

Spatial variation of Aerosol Optical Depth and Solar Irradiance over Delhi -NCR during Summer season

Present study shows the spatial variation of Aerosol Optical Depth (AOD), solar irradiance and their association at the urban and rural sites in Delhi and National Capital Region (NCR) during the summer season of the year 2015. Summer-time AOD data from the NASA’s Terra satellite MODIS sensor has been used to study the spatial distribution of aerosols over Delhi and its surrounding rural area. The ground data for the direct and global solar irradiances was collected over this region at urban and rural locations in Delhi and NCR using a Fieldspec Spectro-radiometer. HYSPLIT model has been used for the air mass trajectory analysis. The AOD values were observed to be higher over Delhi compared to the relatively lower AOD in rural area of NCR. The NCR site observed higher average solar irradiances than Delhi during the summer season. This may be because of the higher aerosol concentration in Delhi as compared to its outskirts. Also, this region is affected by the severe dust storm events during the summer season which further increases the aerosol load in the atmosphere. HYSPLIT results show the influence of western Thar Desert air masses on the Delhi-NCR. Windblown as well anthropogenic aerosols play a major role in scattering and absorption of the incoming solar radiation and hence, in governing the micro-climatology of the region.

Analysis and comparison of vegetation indices of winter wheat crop areas, calculated on the basis of Sentinel-2 and fieldspec spectroradiometer data

Ten vegetation indices (VIs) were analyzed, which were calculated simultaneously based on Sentine-l2 data and on results of ground spectrometric survey by ASD FieldSpec® 3FR for the identically geographical sites of the production crops of winter wheat of two cultivars Bohdana and Skagen. The values of the most studied VIs on Sentinel-2 satellite data are similar by quantity to the same indices, calculated on the narrow spectral channels of ASD FieldSpec® 3FR, except for DRICI (Double ratio index for chlorophyll index) and СІ green (ratio green chlorophyll index), the satellite values of which are much lower than those received by spectroradiometer. It was shown that the differences of VIs received by Sentinel-2 and ASD FieldSpec® 3FR depend on the growth stages of winter wheat: during vegetation season the correlation coefficients between them increase for crop areas of both studied cultivars.

Spectroscopic Determination of Aboveground Biomass in Grass using Partial Least Square Regression Model

Biomass is considered as fresh matter weight or dry matter weight. Biomass is regarded as an important indicator of ecological and management processes in the vegetation. This research aims to predict biomass of grass by using partial least square regression [PLSR] model. To build this PLSR model, we used two variables as an input for PLSR model, i.e. reflectance of aboveground grass and weight of grass [biomass]. For this research we used grass type Cynodondactylon. We used ASD Field Spec4 Spectroradiometer for measuring reflectance of aboveground biomass. For enhancing spectral properties of green vegetation we used five vegetation indices. We estimated aboveground biomass in grass with root mean square error [RMSE] 6.33 and R2 = 0.9 per g/m2 by using PLSR model.

Comportamento espectral de espécies utilizadas em arborização urbana

O objetivo desta pesquisa foi caracterizar e comparar o comportamento espectral de seis espécies arbóreas presentes na Universidade Federal de Santa Maria/RS. As espécies estudadas foram Eucalyptus sp., Ficus benjamina, Inga marginata, Schinus terebinthifolius, Psidium cattleyanum e Handroanthus chrysotrichus. Foram coletadas aleatoriamente sete folhas sadias por espécie, posteriormente analisadas pelo espectrorradiômetro FieldSpec®3, no intervalo de 350 a 2.500 nm. As reflectâncias médias foram utilizadas no cálculo de 61 índices de vegetação. A partir do matriz índice x espécie realizou-se análise de componentes principais. As diferenças entre os comportamentos espectrais das espécies, por bandas do sensor WorldView-2, foram testadas por meio de análise de variância (Tukey). As análises estatísticas foram realizadas no programa R. A análise de agrupamento (método Ward), considerando a distância euclidiana como medida de dissimilaridade, foi realizada com o PC-ORD 6.0. A obtenção e o processamento dos dados espectrais foram realizados nos programas ASD ViewSpecPro 4.05 e SAMS 3.2. O primeiro componente abrangeu 90,94% da variância dos dados. Como resultado, observou-se que a banda com menor relação entre o agrupamento e as diferenças das médias por espécie foi a do Vermelho, enquanto as bandas do Costal, Azul, Verde, Amarelo e Vermelho obtiveram comportamentos semelhantes, com diferenciação das espécies F. benjamina (exceto na banda do Verde), I. marginata (Verde e Amarelo), e P. cattleyanum (Verde). Na banda RedEdge não houve distinção entre espécies. As diferenças de refletâncias foram mais evidentes nas bandas Infravermelho Próximo 1 e 2. Os Índices de Vegetação Otimizada 1 e Razão Simples 8 foram os mais indicados nos estudos das espécies.

Precision Agriculture using Advanced Remote Sensing techniques for peanut crop in Arid Land

Precision agriculture involves studying and managing crop variations within fields that can affect crop yield. In precision agriculture farmers are adapting technology and advanced remote sensing techniques with different software to relieve decision making. Hyperspectral ground measurements can be used for giving timely information about crops in specific areas and thereby providing valuable data for decision makers. In this paper field spectroscopy measurements measured by ASD field Spec4 spectroradiometer were used to monitor the spectral response and differences of peanut crop vegetation cover reflectance due to bio-physical plant variables. The results of Tukey’s HSD showed that blue, Red and NIR spectral zones are more sufficient in the monitoring differences between peanut growth stages than green, SWIR-1 and SWIR-2 spectral zones. The results of physiological spectral indices of growth stages showed significant correlations between varied classes productivity and spectral similarity measures, indicating that similarity between the samples' spectra decreases as the pigments concentration in the plant leaves increases. Furthermore, electromagnetic peanut crop mapping was successfully employed to simulate vegetation healthy effect on canopy structure and final yield.

Temporal dynamics of spectral bioindicators evidence biological and ecological differences among functional types in a cork oak open woodland.

The application of spectral vegetation indices for the purpose of vegetation monitoring and modeling increased largely in recent years. Nonetheless, the interpretation of biophysical properties of vegetation through their spectral signature is still a challenging task. This is particularly true in Mediterranean oak forest characterized by a high spatial and temporal heterogeneity. In this study, the temporal dynamics of vegetation indices expected to be related with green biomass and photosynthetic efficiency were compared for the canopy of trees, the herbaceous layer, and two shrub species: cistus (Cistus salviifolius) and ulex (Ulex airensis). coexisting in a cork oak woodland. All indices were calculated from in situ measurements with a FieldSpec3 spectroradiometer (ASD Inc., Boulder, USA). Large differences emerged in the temporal trends and in the correlation between climate and vegetation indices. The relationship between spectral indices and temperature, radiation, and vapor pressure deficit for cork oak was opposite to that observed for the herbaceous layer and cistus. No correlation was observed between rainfall and vegetation indices in cork oak and ulex, but in the herbaceous layer and in the cistus, significant correlations were found. The analysis of spectral vegetation indices with fraction of absorbed PAR (fPAR) and quantum yield of chlorophyll fluorescence (ΔF/Fm') evidenced strongest relationships with the indices Normalized Difference Water Index (NDWI) and Photochemical Reflectance Index (PRI)512, respectively. Our results, while confirms the ability of spectral vegetation indices to represent temporal dynamics of biophysical properties of vegetation, evidence the importance to consider ecosystem composition for a correct ecological interpretation of results when the spatial resolution of observations includes different plant functional types.

Spectroscopy of olivine basalts using FieldSpec and ASTER data: A case study from Wadi Natash volcanic field, south Eastern Desert, Egypt

This paper aims at revealing the spectral characteristics of the olivine basalts exposed at Wadi Natash area, Egypt, using FieldSpec spectroradiometer. It also evaluates band ratios and fusion techniques for mapping purposes using ASTER data. Several volcanic episodes occurred during Early- to Late-Cretaceous are recorded in the study area. Early-Cretaceous olivine basalts are highly carbonated. Late-Cretaceous eruptions took place throughout several volcanic cones aligned in NW direction. Based on FieldSpec measurements and petrographic data, two groups of olivine basalt namely ‘A’ and ‘B’ are recognized. Fresh olivine basalt (group A) is characterized by low flat spectral profile with overall low reflectance values (∼20%). Spectral profile of altered olivine basalt (group B) shows moderate reflectance values (∼37%) with four little absorption features around the 1.10, 1.40, 2.00 and 2.35 μm wavelength regions. These absorption features are attributed mainly to the presence of chlorite and carbonate alteration products as indicated by petrographic examination. ASTER false colour composite band ratio image (3/2:R, 8/1:G and 8/5:B) discriminates easily the fresh and altered basalts by deep blue and reddish blue colours respectively. Image fusion between previously mentioned FCC ratios image and high spatial resolution ASTER panchromatic image are carried out using brovey and HSV transformation methods. Visual and statistical assessment methods proved that HSV fusion image yields better image interpretability results compared to brovey image. It improves the spatial resolution of original FCC ratios image with acceptable spectral preservation. The present study proved the usefulness of FieldSpec spectral profiles and the processed ASTER data for discriminating different olivine basalt groups exposed at the study area.

A novel reflectance-based model for evaluating chlorophyll concentrations of fresh and water-stressed leaves

Abstract. Water deficits can cause chlorophyll degradation which decreases the total concentration of chlorophyll a and b (Chls). Few studies have investigated the effectiveness of spectral indices under water-stressed conditions. Chlorophyll meters have been extensively used for a wide variety of leaf chlorophyll and nitrogen estimations. Since a chlorophyll meter works by sensing leaves absorptance and transmittance, the reading of chlorophyll concentration will be affected by changes in transmittance as if there were a water deficit in the leaves. The overall objective of this paper was to develop a novel and reliable reflectance-based model for estimating Chls of fresh and water-stressed leaves using the reflectance at the absorption bands of chlorophyll a and b and the red edge spectrum. Three independent experiments were designed to collect data from three leaf sample sets for the construction and validation of Chls estimation models. First, a reflectance experiment was conducted to collect foliar Chls and reflectance of leaves with varying water stress using the ASD FieldSpec spectroradiometer. Second, a chlorophyll meter (SPAD-502) experiment was carried out to collect foliar Chls and meter readings. These two data sets were separately used for developing reflectance-based or absorptance-based Chls estimation models using linear and nonlinear regression analysis. Suitable models were suggested mainly based on the coefficient of determination (R2). Finally, an experiment was conducted to collect the third data set for the validation of Chls models using the root mean squared error (RMSE) and the mean absolute error (MAE). In all of the experiments, the observations (real values) of the foliar Chls were extracted from acetone solution and determined by using a Hitachi U-2000 spectrophotometer. The spectral indices in the form of reflectance ratio/difference/slope derived from the Chl b absorption bands (ρ645 and ρ455) provided Chls estimates with RMSE around 0.40–0.55 mg g−1 for both fresh and water-stressed samples. We improved Chls prediction accuracy by incorporating the reflectance at red edge position (ρREP) in regression models. An effective chlorophyll indicator with the form of (ρ645–ρ455)/ρREP proved to be the most accurate and stable predictor for foliar Chls concentration. This model was derived with an R2 of 0.90 (P < 0.01) from the training samples and evaluated with RMSE 0.35 and 0.38 mg g−1 for the validation samples of fresh and water-stressed leaves, respectively. The average prediction error was within 14% of the mean absolute error.

Characterization of water stress and prediction of yield of wheat using spectral indices under varied water and nitrogen management practices

There is a need to characterize the water stress in wheat using suitable indices, which will help us to find out the water stress sensitive period for efficient use of irrigation water. Recently indices based on canopy spectral reflectance, which are non destructive, fast and reliable, are being used effectively to characterize the water stress. A field experiment was carried out during the year 2010–2012 in split plot design with four levels of irrigation (irrigation at 0.4 IW/CPE, 0.6 IW/CPE, 0.8 IW/CPE and 1.0 IW/CPE, IW=6cm) as main plot factors and three sources of nitrogen (100%N from urea, 50% N from urea and 50% N from farmyard manure (FYM) and 100% N from FYM) as subplot factors. The objective of the study was to find out the water stress indices best correlated with wheat grain and biomass yield, to determine the optimum growth stage for measurement of water stress indices and to predict the grain and biomass yield of wheat based on water stress indices. The canopy reflectance was measured in the spectral range of 350–2500nm with 1nm bandwidth with the help of hand held ASD FieldSpec Spectroradiometer at seven phenostages, viz., crown root initiation (CRI), tillering, booting, flowering, milk, soft dough and harvesting stage. Then different water stress indices were computed as: water index (WI)=R970/R900, normalized water index-1 (NWI-1)=(R970−R900)/(R970+R900), normalized water index-2 (NWI-2)=(R970−R850)/(R970+R850), normalized water index-3 (NWI-3)=(R970−R920)/(R970+R920), normalized water index-4 (NWI-4)=(R970−R880)/(R970+R880), where R and the subscript numbers indicate the light reflectance at the specific wavelength (in nm). It was observed that spectral reflectance based water indices recorded at the milk stage, WI and NWI-1 were significantly negatively correlated with the grain yield and NWI-1 and NWI-3 were significantly negatively correlated with the biomass yield of wheat, having maximum correlation coefficients. Validation of regression model based on NWI-1 could account for the maximum 87.5% variation in the observed grain yield and the regression model based on WI could account for maximum 89.2% variation in the observed biomass yield of wheat with minimum root mean square errors. So the regression models based on NWI-1 and WI recorded at milk stage can be successfully used to predict the grain and biomass yield of wheat in advance.

Models for Estimating the Physical Properties of Paddy Soil Using Visible and Near Infrared Reflectance Spectroscopy

A fast and convenient soil analytical technique is needed for soil quality assessment and precision soil management. The main objective of this study was to evaluate the ability of visible (Vis) and near-infrared (NIR) refl ectance spectroscopy to predict paddy soil properties in a typical Malaysian paddy fi eld. To assess the utility of spectroscopy for soil physical characteristics (bulk density, moisture content, clay, silt and sand) prediction, 118 soil samples were used for laboratory analysis and optical measurement in the Vis-NIR region using an analytical spectral device (ASD) FieldSpec spectroradiometer (350–2500 nm). The Savitzky–Golay algorithm and stepwise multiple linear regression (SMLR) were then applied to preprocess, model, and predict the properties on the basis of their spectral refl ectance within the Vis-NIR range. One-third of the samples (40 samples) were withheld for validation purposes. The study revealed that Vis and NIR spectroscopy calibration models for all the measured soil physical characteristics provided a good fi t (R2 > 0.78); hence Vis and NIR (specifi cally NIR refl ectance) can be considered to be a reliable tool to assess soil physical properties of Malaysian paddy fi elds. The results of this study could contribute signifi cantly to developing site-specifi c management.

Predicting Wheat Grain and Biomass Yield Using Canopy Reflectance of Booting Stage

Field experiment was conducted in a sandy loam soil of Indian Agricultural Research Institute, New Delhi during the year 2011–13 to see the effect of irrigation, mulch and nitrogen on canopy spectral reflectance indices and their use in predicting the grain and biomass yield of wheat. The canopy reflectances were measured using a hand held ASD FieldSpec Spectroradiometer at booting stage of wheat. Four spectral reflectance indices (SRIs) viz. RNDVI (Red Normalized Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), SR (Simple Ratio) and WI (Water Index) were computed using the spectral reflectance data. Out of these four indices, RNDVI, GNDVI and SR were significantly and positively related with the grain and biomass yield of wheat whereas WI was significantly and negatively related with the grain and biomass yield of wheat. Calibration with the second year data showed that among the SRIs, WI could account for respectively, 85 % and 86 % variation in grain and biomass yield of wheat with least RMSE (395 kg ha−1 (15 %) for grain yield and 1609 kg ha−1 (20 %) for biomass yield) and highest d index (0.95 for grain yield and 0.91 for biomass yield). Therefore it can be concluded that WI measured at booting stage can be successfully used for prediction of grain and biomass yield of wheat.