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1.
The study to establish the optimum time span for distinguishing Avena ludoviciana from wheat crop based on their spectral signatures was carried out at Student’s Research Farm, Department of Agronomy during 2006–07 and 2007–08. The experimental sites during both the seasons were sandy loam in texture, with normal soil reaction and electrical conductivity, low in organic carbon and available nitrogen and medium in available phosphorus and potassium. The experiment was laid out in randomized block design with four replications and consisting of twelve treatments comprising 0, 10, 15, 25, 50, 75, 100, 125, 150, 200, 250 plants m−2 and a pure Avena ludoviciana plot (Tmax). The results revealed that in all the treatments irrespective of wheat and weeds, the red reflectance (%) value decreased from 34 to 95 DAS (days after sowing) in 2006–07 and 45 DAS to 100 DAS during 2007–08, and thereafter a sharp increase was observed in all the treatments. This trend might be due to increased chlorophyll index after 34 DAS as red reflectance was reduced by chlorophyll absorption. Among all the treatments, Tmax (Pure Avena ludoviciana plot) had the highest red reflectance and T0 (Pure wheat plot) had a lowest value of red reflectance during both the years. The highest value of IR reflectance was obtained at 95 DAS (2006–07) and 70 DAS (2007–08) in all the treatments. IR reflectance of wheat crop ranged between 24.61 and 61.21 per cent during 2006–07 and 27.33 and 67.3 per cent during 2007–08. However, IR reflectance values declined after 95 DAS and 70 DAS up to harvesting during 2006–07 and 2007–08. This lower reflectance may have been due to the onset of senescence. The highest RR and NDVI values were recorded under pure wheat treatment and minimum under pure weed plots. This may be due to dark green colour and better vigor of the wheat as compared to Avena ludoviciana. It was observed that by using RR and NDVI, pure wheat can be distinguished from pure populations of Avena ludoviciana after 34 DAS and different levels of weed populations can be discriminated amongst themselves from 68 DAS up to 107 DAS during both the years of investigation.  相似文献   

2.
Spectral indices as an indicator of physiological traits affecting safflower yield in relation to soil variability were evaluated in a two year experiment (1997–1999). Reflectance, biometric and phonological data were collected. Two indices namely normalized differential vegetation index (NDVI) and ratio of spectral reflectance in infrared region to red region (1R/R) were derived from radiometric observation. Yield data indicated significant difference in different soils. Temporal NDVI behaviour as a function of soil type was not prominent especially in early stages of crop growth. However NDVI at 75 days after sowing (DAS) was found to be relatively better indicator of plant status and yield. IR/R was relatively less effective in indicating the differential response of crop to soil types. Effect of soil and crop interaction on spectral indices was significant at 75 and 90 DAS, which was attributed to attainment of maximum leaf area and leaf area at these stages of growth. Regression analysis showed strong positive relationship between NDVI and leaf area, dry matter and yield. IR/R and leaf area had the strongest and consistent relationship (r = 0.96). A single regression equation accounted for yield variability in the dataset. Thus possible transformation of NDVI maps (satellite data) to LAI units and consequently applications like yield forecasting was indicated. Utility of spectra-temporal data as a pointer of plant development status and yield was also demonstrated.  相似文献   

3.
This paper highlights the spatial and temporal variability of atmospheric columnar methane (CH4) concentration over India and its correlation with the terrestrial vegetation dynamics. SCanning IMaging Absorption spectrometer for Atmospheric CHartographY (SCIAMACHY) on board ENVIronmental SATellite (ENVISAT) data product (0.5° × 0.5°) was used to analyze the atmospheric CH4 concentration. Satellite Pour l'Observation de la Terre (SPOT)-VEGETATION sensor’s Normalized Difference Vegetation Index (NDVI) product, aggregated at 0.5° × 0.5° grid level for the same period (2004 and 2005), was used to correlate the with CH4 concentration. Analysis showed mean monthly CH4 concentration during the Kharif season varied from 1,704 parts per billion volume (ppbv) to 1,780 ppbv with the lowest value in May and the highest value in September. Correspondingly, mean NDVI varied from 0.28 (May) to 0.53 (September). Analysis of correlation between CH4 concentration and NDVI values over India showed positive correlation (r = 0.76; n = 6) in Kharif season. Further analysis using land cover information showed characteristic low correlation in natural vegetation region and high correlation in agricultural area. Grids, particularly falling in the Indo-Gangetic Plains showed positive correlation. This could be attributed to the rice crop which is grown as a predominant crop during this period. The CH4 concentration pattern matched well with growth pattern of rice with the highest concentration coinciding with the peak growth period of crop in the September. Characteristically low correlation was observed (r = 0.1; n = 6) in deserts of Rajasthan and forested Himalayan ecosystem. Thus, the paper emphasizes the synergistic use of different satellite based data in understanding the variability of atmospheric CH4 concentration in relation to vegetation.  相似文献   

4.
The present study has been carried out to delineate the existing cropping systems in the Indo-Gangetic Plains (IGP) using 10 day composite SPOT VEGETATION (VGT) NDVI data acquired over a crop year (June–May). Results showed that it is feasible to identify the major crops like rice, wheat, sugarcane, potato, and cotton in the dominant growing areas with good accuracy. Double cropping pattern is the most prevalent. Rice-wheat, sugarcane based, cotton-wheat, rice-potato, rice-rice, maize/millet-wheat are some of the major rotations followed. Rice-wheat is the dominant rotation accounting for around 40% of the net sown area. Triple crop rotations was less than 5% of the area and observed in some parts of Uttar Pradesh, Bihar and West Bengal. Single crop rotation of rice-fallow is significant only in West Bengal.  相似文献   

5.
Present study was designed to determine the effect of various growing environments on sucking pest population dynamics in cotton and to work out their relation with spectral indices. Crop spectral reflectance in four IRS bands was measured with ground truth radiometer during 1000–1200 h in all the treatment combinations. Incidence of sucking pest in cotton was found out to be highly influenced by growing environments. The leafhopper and whitefly population was highest in 15 May sown cotton crop and was lowest in 15 April sown crop. Cultivar HS-6 was highly affected by both the sucking pest than the other cultivar H-1226. The spectral indices (SR, NDVI and TVI) were highest in 15 April sown crop at all the phenophases and were lowest in 15 May sown crop. The cultivar H-1226 showed higher values of spectral indices as compared to HS-6. The relationship of pests’ population with various spectral indices was established. Multiple regression models based on spectral indices can be used for prediction of sucking pest population more than 69 and 74 % accuracy in leafhopper and whitefly, respectively in cotton crop.  相似文献   

6.
Crop yield estimation has an important role on economy development and its accuracy and speed influence yield price and helps in deciding the excess or deficit production conditions. The water productivity evaluates the irrigation command through water use efficiency (WUE). Remote sensing (RS) and geographical information system (GIS) techniques were used for crop yield and water productivity estimation of wheat crop (Triticum aestivum) grown in Tarafeni South Main Canal (TSMC) irrigation command of West Bengal State in India. One IRS P6 image and four wide field sensor (WiFS) images for different months of winter season were used to determine the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) for area under wheat crop. The temporally and spatially distributed spectral growth profile and AREASUM of NDVI (ANDVI) and SAVI (ASAVI) with time after sowing of wheat crop were developed and correlated with actual crop yield of wheat (Yact). The developed relationships between ASAVI and Yact resulted high correlation in comparison to that of ANDVI. Using the developed model the RS based wheat yield (YRS) predicted from ASAVI varied on entire TSMC irrigation command from 22.67 to 33.13 q ha−1 respectively, which gave an average yield of 26.50 q ha−1. The RS generated yield based water use efficiency (WUEYRS) for water supplied from canal of TSMC irrigation command was found to be 6.69 kg ha−1 mm−1.  相似文献   

7.
In the present study, prediction of agricultural drought has been addressed through prediction of agricultural yield using a model based on NDVI-SPI. It has been observed that the meteorological drought index SPI with different timescale is correlated with NDVI at different lag. Also NDVI of current fortnight is correlated with NDVI of previous lags. Based on the correlation coefficients, the Multiple Regression Model was developed to predict NDVI. The NDVI of current fortnight was found highly correlated with SPI of previous fortnight in semi-arid and transitional zones. The correlation between NDVI and crop yield was observed highest in first fortnight of August. The RMSE of predicted yield in drought year was found to be about 17.07 kg/ha which was about 6.02 per cent of average yield. In normal year, it was 24 kg/Ha denoting about 2.1 per cent of average yield.  相似文献   

8.
A functional form of crop spectral profile suggested by Badhwar was applied to district-wise wheat Normalised Difference Vegetation Index (NDVI) values relatively normalised by Pseudo-Invariant Feature (urban and built-up) NDVI values, derived from Wide Field Sensor (WiFS) onboard Indian Remote Sensing Satellites (IRS) for 17 dates during 1999–2000 rabi season. The goodness of overall profile fitting and the three basic parameters i.e., crop emergence date (To), and crop specific parameters (a and P) was found to be statistically significant. While a corresponds to profile progressive growth rate, β corresponds to profile decay rate. A comparison with earlier studies in Punjab using NOAA-AVHRR indicated improvement in relation between peak NDVI and wheat yield. The estimated time of spectral emergence and profile-derived peak NDVI follow the observed behaviour of shortened crop pre-anthesis period with delayed sowing.  相似文献   

9.
The potential usefulness of spectral properties and vegetation indices in varietal discrimination of potato genotypes was studied in the field experiment. Spectral measurements were recorded in different bands in blue (450–520 nm), green (520–590 nm), red (620–680 nm) and infrared (770–860 nm) of the electromagnetic spectrum at different stages during crop growth period. A ground based hand held multiband radiometer (Model/041) was used for the purpose. The mean per cent green reflectance value among different genotypes was lowest in genotype MS/86-89, while it was observed highest in genotype JX-216. Significant difference among these genotypes was found at all growth stages except 6 week after planting. Consequent to variation in spectral reflectance the vegetation indices like, NDVI, RVI, TVI and DVI showed significant difference among genotypes at all growth stages except at 8th week after planting. The vegetation indices are good indicators of crop growth and condition. Similarly, fresh weight, dry weight, and leaf area index were also highest in MS/86-89, followed by KUFRI Bahar and KUFRI Sutlej while in case of leaf area index it was followed by Kufri Sutlej and Kufri Bahar. JX-23 was highest in chlorophyll content and tuber yield followed by MS/86-89 and JW-160, while lowest chlorophyll content was seen in MS/89-1095 and poorest tuber yield in MS/89-60. Most of the genotypes exhibited considerable variation in their spectral response and vegetation indices thereby indicating the possibility of their discrimination through remote sensing technique.  相似文献   

10.
Satellite-derived evapotranspiration anomalies and normalized difference vegetation index (NDVI) products from Moderate Resolution Imaging Spectroradiometer (MODIS) data are currently used for African agricultural drought monitoring and food security status assessment. In this study, a process to evaluate satellite-derived evapotranspiration (ETa) products with a geospatial statistical exploratory technique that uses NDVI, satellite-derived rainfall estimate (RFE), and crop yield data has been developed. The main goal of this study was to evaluate the ETa using the NDVI and RFE, and identify a relationship between the ETa and Ethiopia’s cereal crop (i.e., teff, sorghum, corn/maize, barley, and wheat) yields during the main rainy season. Since crop production is one of the main factors affecting food security, the evaluation of remote sensing-based seasonal ETa was done to identify the appropriateness of this tool as a proxy for monitoring vegetation condition in drought vulnerable and food insecure areas to support decision makers. The results of this study showed that the comparison between seasonal ETa and RFE produced strong correlation (R2 > 0.99) for all 41 crop growing zones in Ethiopia. The results of the spatial regression analyses of seasonal ETa and NDVI using Ordinary Least Squares and Geographically Weighted Regression showed relatively weak yearly spatial relationships (R2 < 0.7) for all cropping zones. However, for each individual crop zones, the correlation between NDVI and ETa ranged between 0.3 and 0.84 for about 44% of the cropping zones. Similarly, for each individual crop zones, the correlation (R2) between the seasonal ETa anomaly and de-trended cereal crop yield was between 0.4 and 0.82 for 76% (31 out of 41) of the crop growing zones. The preliminary results indicated that the ETa products have a good predictive potential for these 31 identified zones in Ethiopia. Decision makers may potentially use ETa products for monitoring cereal crop yields and early warning of food insecurity during drought years for these identified zones.  相似文献   

11.
To study impact of climate change on vegetation time series vegetation index has a vital role to know the behaviour of vegetation dynamics over a time period. INSAT 3A CCD (Charged Couple Device) is the only geostationary sensor to acquire regular coverage of Asia continent at 1 km × 1 km spatial resolution with high temporal frequency (half-an-hour). A formulation of surface reflectances in red, near infrared (NIR), short wave infrared (SWIR) and NDVI from INSAT 3A CCD has been defined and integrated in the operational chain. The atmospheric correction of at-sensor reflectances using SMAC (Simple Model for Atmospheric Correction) model improved the NDVI by 5–40% and also increased its dynamic range. The temporal dynamics of 16-day NDVI composite at 0500 GMT for a growing year (June 2008–March 2009) showed matching profiles with reference to global products (MODIS TERRA) over known land targets. The root mean square deviation (RMSD) between the two was 0.14 with correlation coefficient (r) 0.84 from 200 paired datasets. This inter-sensor cross-correlation would help in NDVI calibration to add continuity in long term NDVI database for climate change studies.  相似文献   

12.
综合多特征的Landsat 8时序遥感图像棉花分类方法   总被引：3，自引：0，他引：3  
传统的多时相遥感图像分类大多拘泥于单一特征,本文基于多时相的Landsat 8遥感数据,开展了综合多特征的特征提取与特征选择方法研究。综合了NDVI时间序列、最佳时相反射率光谱特征以及纹理特征作为初始分类特征,并采用基于属性重要度的粗糙集特征选择算法对其进行特征约简。分类结果表明:(1)利用初始分类特征,分类的总体精度达到92.81%,棉花提取精度达87.4%,与仅利用NDVI时间序列相比,精度分别提高5.53%和5.05%;(2)利用粗糙集选择后的特征分类,分类总体精度可达93.66%,棉花分类精度达92.73%,与初始分类特征提取结果相比,棉花分类精度提高5.33%。基于属性重要度的粗糙集特征选择不仅提高了分类精度,同时有效降低了分类器的计算复杂度。  相似文献   

13.
A field experiment was conducted on wheat crop during rabi seasons of 1995–96, 1996–97 and 1997–98 to study the spectral response of wheat crop (between 490 to 1080 nm) under water and nutrient stress condition. An indigenously developed ground truth radiometer having narrow band in visible and near infrared region (490 – 1080 nm) was used. Vegetation indices derived using different band combinations and related to crop growth parameters. The near infrared spectral region of 710 – 1025 nm was found most important for monitoring stress condition. Relationship has been developed between crop growth parameters and vegetation indices. Leaf Area Index (LAI) and chlorophyll could be predicted by knowing different reflectance ratios at milking stage of crop with R2 value of 0.78 and 0.89, respectively. Dry biomass (DBM), Plant Water Content (PWC) and grain yield are also significantly related with reflectance ratios at flowering stage of crop with R2 value of 0.90, 0.98 and 0.74, respectively.  相似文献   

14.
Utility of Hyperspectral Data for Potato Late Blight Disease Detection   总被引：1，自引：0，他引：1  
The study was carried out to investigate the utility of hyperspectral reflectance data for potato late blight disease detection. The hyperspectral data was collected for potato crop at different level of disease infestation using hand-held spectroradiometer over the spectral range of 325–1075 nm. The data was averaged into 10-nm wide wavebands, resulting in 75 narrowbands. The reflectance curve was partitioned into five regions, viz. 400–500 nm, 520–590 nm, 620–680 nm, 770–860 nm and 920–1050 nm. The notable differences in healthy and diseased potato plants were noticed in 770–860 nm and 920–1050 nm range. Vegetation indices, namely NDVI, SR, SAVI and red edge were calculated using reflectance values. The differences between the vegetation indices for plants at different levels of disease infestation were found highly significant. The optimal hyperspectral wavebands to discriminate the healthy plants from disease infested plants were 540, 610, 620, 700, 710, 730, 780 and 1040 nm whereas upto 25% infestation could be discriminated using reflectance at 710, 720 and 750 nm.  相似文献   

15.
Crop phenological parameters, such as the start and end time of the crop growth, the total length of the growing season, time of peak vegetation and rate of greening and senescence are important for planning crop management and crop diversification/intensification. Multi-temporal remote sensing data provides opportunity to characterize the crop phenology at regional level. This study was conducted during the kharif season of the year 2001–02 for Punjab. The ten-day Normalised Difference Vegetation Index (NDVI) composite products, with 1 km spatial resolution, available from the Vegetation sensor onboard SPOT4 were used for the study. Twenty-one temporal datasets from May 1, 2001 to November 21, 2001 were used. Logical modelling approach was followed to compute the minimum and maximum NDVI, the amplitude of NDVI, the threshold NDVI during sowing and harvest, the crop duration, integrated NDVI and skewness of profile. The analysis showed that before July beginning, in the whole of Punjab, sowing/planting was over. It was found that the crop emergence in the eastern part of the state started earlier than the western region. The maximum NDVI, which represented peak vegetative stage, was above 0.7 and occurred mostly during August. The duration of crops ranged between 90–140 days, with majority between 110–120 days. Total integrated NDVI in Punjab was generally above 60. Using principal component analysis and divergence analysis seven best metrics were selected for crop discrimination.  相似文献   

16.
The results emerged out of the studies on spectral reflectance under normal and nitrogen and phosphorus stress condition in soybean (Glycine max L.) conducted at Marathwada Agricultural University experimental farm, Parbhani duringkharif 2004–05 showed that crop growth and bio-physiological parameters viz., Height, chlorophyll, leaf area index and total biomass influenced by pest and disease and nutrient stress resulted in detectable spectral reflectance variation. Poor crop growth, reduced canopy cover, chlorophyll content and biomass production are the effects observed in nutrient deficient crops. These above changes in soybean crop were related to spectral indices (RVI and NDVI) that are resulted in discrimination of stressed and normal (non-stressed) soybean crop.  相似文献   

17.
Indian Remote Sensing Satellite (IRS) — P4 Ocean Colour Monitor (OCM) data were used to estimate Suspended Sediment Concentration (SSC) in the coastal waters off Chennai and to study the distribution along the coast. Surface water samples were collected during May and October 2000 synchronized with satellite overpass, and quantitative estimates of Suspended Sediment Concentration (SSC) were done. OCM — Data Analysis System (DAS) software developed by the Space Applications Centre, Ahmedabad was used for OCM data processing and analysis. The field data and OCM derived SSC showed a correlation of r = 0.85 and r = 0.95 for the months of May and October respectively.  相似文献   

18.
Ukraine is one of the most developed agriculture countries and one of the biggest crop producers in the world. Timely and accurate crop yield forecasts for Ukraine at regional level become a key element in providing support to policy makers in food security. In this paper, feasibility and relative efficiency of using moderate resolution satellite data to winter wheat forecasting in Ukraine at oblast level is assessed. Oblast is a sub-national administrative unit that corresponds to the NUTS2 level of the Nomenclature of Territorial Units for Statistics (NUTS) of the European Union. NDVI values were derived from the MODIS sensor at the 250 m spatial resolution. For each oblast NDVI values were averaged for a cropland map (Rainfed croplands class) derived from the ESA GlobCover map, and were used as predictors in the regression models. Using a leave-one-out cross-validation procedure, the best time for making reliable yield forecasts in terms of root mean square error was identified. For most oblasts, NDVI values taken in April–May provided the minimum RMSE value when comparing to the official statistics, thus enabling forecasts 2–3 months prior to harvest. The NDVI-based approach was compared to the following approaches: empirical model based on meteorological observations (with forecasts in April–May that provide minimum RMSE value) and WOFOST crop growth simulation model implemented in the CGMS system (with forecasts in June that provide minimum RMSE value). All three approaches were run to produce winter wheat yield forecasts for independent datasets for 2010 and 2011, i.e. on data that were not used within model calibration process. The most accurate predictions for 2010 were achieved using the CGMS system with the RMSE value of 0.3 t ha−1 in June and 0.4 t ha−1 in April, while performance of three approaches for 2011 was almost the same (0.5–0.6 t ha−1 in April). Both NDVI-based approach and CGMS system overestimated winter wheat yield comparing to official statistics in 2010, and underestimated it in 2011. Therefore, we can conclude that performance of empirical NDVI-based regression model was similar to meteorological and CGMS models when producing winter wheat yield forecasts at oblast level in Ukraine 2–3 months prior to harvest, while providing minimum requirements to input datasets.  相似文献   

19.
Water Utilisation Index (WUI) defined as area irrigated per unit volume is a measure of water delivery performance and constitutes one of the important spatial performance indicators of an irrigation system. WUI also forms basis for evaluating the adequacy of seasonal irrigation supplies in an irrigation system (inverse of WUI is delta, i.e. depth of water supplied to a given irrigation unit). In the present study WUI and adequacy indicators were used in benchmarking the performance of Nagarjunasagar Left Canal Command (NSLC) in Andhra Pradesh. Optimised temporal satellite data of rabi season during the years 1990–91 and 1998–99 was used in deriving irrigated crop areas adopting hierarchical classification approach. Paddy is the predominant crop grown and cotton, chillies, sugarcane etc. are the other crops grown in the study area. Equivalent wet area (paddy crop area) was estimated using the operationally used project specific conversion factors. WUI was estimated at disaggregated level viz., distributary, irrigation block, irrigation zone level using the canal discharge data. At project level, WUI estimated to be 65 ha/MCM and 92 ha/MCM during rabi season of 1990–91 and 1998–99 years respectively. A comparison of total irrigated area and discharges corresponding to both the years indicate that irrigation service is extensive and sub optimal during 1998–99 and it is intensive and optimal in 1990–91. It was also observed that WUI is lesser in blocks of with higher Culturable Command Area (CCA) compared to the blocks of lower CCA. All the disaggregated units were ranked into various groups of different levels of water distribution performance. The study demonstrates the utility of WUI as spatial performance indicator and thus useful for benchmarking studies of irrigation command areas. The WUI together with satellite data derived spatial irrigation intensity, crop productivity constitutes important benchmarking indices in irrigation command areas.  相似文献   

20.
Field experiments were conducted during 1998–99 and 1999–2000 at research farm of the Department of Agricultural Meteorology, CCS Haryana Agricultural University, Hisar. Five wheat cultivars: WH 542, PBW 343, UP 2338, Raj 3765 and Sonak were sown on 25th November, 10th and 25th December with four nitrogen levels viz., no nitrogen. 50, 100 and 150% of recommended dose. Leaf area index, dry matter at anthesis, final dry biomass and grain yield were recorded in all the treatments. Chlorophyll and wax contents of wheat leaves were estimated at different growth stages. Multiband spectral reflectance was measured using hand-held radiometer. Spectral indices such as simple ratio, normalized difference, transformed vegetation index, perpendicular vegetation index and greenness index were computed using the multiband spectral data. Values of all the spectral indices were maximum in 25 November sown crop with maximum dose of nitrogen (180 kg N ha-1). PBW 343 showed higher values of all the spectral indices in comparison with other cultivars. The spectral indices recorded during maximum leaf area index stage were correlated with crop parameters. Using stepwise regression, empirical models for chlorophyll, leaf area index, dry biomass and yield prediction were developed. The ’R2’ values of these models ranged between 0.87 and 0.95.  相似文献   

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