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1.
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   

2.
Wheat yield prediction using different agrometeorological indices, spectral index (NDVI, Normalized Difference Vegetation Index) and trend predicted yield (TPY) were developed in Hoshiarpur and Rupnagar districts of Punjab. On the basis of examination of Correlation Coefficients (R), Standard Error of Estimate (SEOE) and Relative Deviation (RD) values resulted from different agromet models, the best agromet subset were selected as Minimum Temperature (Tmin), Maximum Temperature (Tmax) and accumulated Heliothermal Units (HTU) in case of Hoshiarpur district and Minimum Temperature (T--min), accumulated Temperature Difference (TD) and accumulated Pan Evaporation (E) for Rupnagar district at reproductive stage (2nd week of March) of wheat. It was found that Agromet-Spectral-Trend-Yield model could explain 96 % (SEOE = 87 kg/ha) and 91 % (SEOE = 146 kg/ha) of wheat yield variations for Hoshiarpur and Rupnagar districts, respectively.  相似文献   

3.
Crop monitoring using remotely sensed image data provides valuable input for a large variety of applications in environmental and agricultural research. However, method development for discrimination between spectrally highly similar crop species remains a challenge in remote sensing. Calculation of vegetation indices is a frequently applied option to amplify the most distinctive parts of a spectrum. Since no vegetation index exist, that is universally best-performing, a method is presented that finds an index that is optimized for the classification of a specific satellite data set to separate two cereal crop types. The η2 (eta-squared) measure of association – presented as novel spectral separability indicator – was used for the evaluation of the numerous tested indices. The approach is first applied on a RapidEye satellite image for the separation of winter wheat and winter barley in a Central German test site. The determined optimized index allows a more accurate classification (97%) than several well-established vegetation indices like NDVI and EVI (<87%). Furthermore, the approach was applied on a RapidEye multi-spectral image time series covering the years 2010–2014. The optimized index for the spectral separation of winter barley and winter wheat for each acquisition date was calculated and its ability to distinct the two classes was assessed. The results indicate that the calculated optimized indices perform better than the standard indices for most seasonal parts of the time series. The red edge spectral region proved to be of high significance for crop classification. Additionally, a time frame of best spectral separability of wheat and barley could be detected in early to mid-summer.  相似文献   

4.
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   

5.
The existence of mixed pixels in the satellite images has always been an area of concern. Adding to the challenge is an occurrence of non-linearity between the classes, which is generally overlooked. The study makes an attempt to solve the two frequently occurring problems by kernel based fuzzy approach. This research work deals with Possibilistic c-Means (PCM) classifier with local, global, spectral angle and hyper tangent kernels for wheat crop (Triticum aestivum) identification in Haridwar, Uttarakhand, India. The multi-temporal vegetation index data of Formosat-2 have been used which covers the whole phenology of wheat crop. The additional sensor Landsat-8 OLI imagery has been filled the crucial gap of Formosat-2 temporal datasets. Nine types of proposed kernels based PCM classifier have been applied on three temporal datasets (four, five and six date combinations) to classify two classes early sown and late sown wheat crop. These test results have been concluded that at optimized weighted constant KMOD and polynomial kernel was found effective to separate wheat crop. The five and six date combination were sufficient to discriminate early sown and late sown wheat crop.  相似文献   

6.
An experiment was conducted during 1996–97 and 1997–98 to study spectral indices and their relationships with grain yield of wheat. Variations of ratio vegetation index (RVI), normalized differences vegetation index (NDVI). difference vegetation index (DVI), transformed vegetation index (TVI), perpendicular vegetation index (PVI) and greenness vegetation index (GVI) have been studied at anthesis stage under different moisture and nitrogen levels. Spectral indices were correlated with crop parameters and it was found that GVI was the best index for yield estimation (r = 0.91 ).  相似文献   

7.
以地块分类为核心的冬小麦种植面积遥感估算   总被引：5，自引：0，他引：5  
以提高冬小麦种植面积估算精度为目标,选取种植结构复杂的都市农业区,采用QuickBird影像数字化农田地块边界,以多时相TM影像为核心数据源,以地块为基本分类单元,进行不同特征向量组合、不同分类器的冬小麦地块分类方法研究,并对比分析了基于地块分类和基于像元分类的冬小麦种植面积估算精度。研究结果表明,基于地块分类的冬小麦种植面积估算方法的总量精度和位置精度均高于像元分类;植被指数和纹理信息的引入有助于进一步提高地块分类精度;支持向量机与最大似然均能得到高达97%的总量精度和90%的位置精度,支持向量机地块分类所需的训练样本量远低于最大似然,因此支持向量机更加适合于冬小麦地块分类;冬小麦错分与漏分情况大多发生在细碎地块,其面积总量较小,而大地块错分和漏分较少,因此相对于像元分类,地块分类能在整个区域能得到较高的冬小麦位置精度和总量精度。  相似文献   

8.
Agricultural drought has been a recurrent phenomenon in many parts of India. Remote sensing plays a vital role in real time monitoring of the agricultural drought conditions over large area, there by effectively supplementing the ground mechanism. Conventional drought monitoring is based on subjective data. The satellite based monitoring such as National Agricultural Drought Assessment and Monitoring System (NADAMS) is based on the crop condition, which is an integrated effect of soil, effective rainfall, weather, etc. Drought causes changes in the external appearance of vegetation, which can clearly be identified (by their changed spectral response) and judged using satellite sensors through the use of vegetation indices. These indices are functions of rate of growth of the plants and are sensitive to the changes of moisture stress in vegetation. The satellite based drought assessment methodology was developed based on relationship obtained between previous year’s Normalised Difference Vegetation Index (NDVI) profiles with corresponding agricultural performance available at district/block level. Palar basin, one of the major river basins in Tamil Nadu state was selected as the study area. The basin covers 3 districts, which contain 44 blocks. Wide Image Field Sensor (WiFS) of 188m spatial resolution from Indian Remote Sensing Satellite (IRS) data was used for the analysis. Satellite based vegetation index NDVI, was generated for Samba and Navarai seasons in the years 1998 and 1999. An attempt has been made to estimate the area under paddy. It was also observed that, there was reduction in the crop area as well as vigour in the vegetation in both Samba and Navarai seasons in 1999 when compared with 1998. Drought severity maps were prepared in GIS environment giving blockwise agricultural water deficiency status.  相似文献   

9.
Timely and reliable estimation of regional crop yield is a vital component of food security assessment, especially in developing regions. The traditional crop forecasting methods need ample time and labor to collect and process field data to release official yield reports. Satellite remote sensing data is considered a cost-effective and accurate way of predicting crop yield at pixel-level. In this study, maximum Enhanced Vegetation Index (EVI) during the crop-growing season was integrated with Machine Learning Regression (MLR) models to estimate wheat and rice yields in Pakistan's Punjab province. Five MLR models were compared using a fivefold cross-validation method for their predictive accuracy. The study results revealed that the regression model based on the Gaussian process outperformed over other models. The best performing model attained coefficient of determination (R2), Root Mean Square Error (RMSE, t/ ha), and Mean Absolute Error (MAE, t/ha) of 0.75, 0.281, and 0.236 for wheat; 0.68, 0.112, and 0.091 for rice, respectively. The proposed method made it feasible to predict wheat and rice 6– 8 weeks before the harvest. The early prediction of crop yield and its spatial distribution in the region can help formulate efficient agricultural policies for sustainable social, environmental, and economic progress.  相似文献   

10.
The Regione del Veneto (Italy) is cooperating with the University of California, Santa Barbara and other researchers in Italy and the U.S.A. to develop a system of econometric crop production modeling. Five crops are to be included in this project: small grains (wheat and barley), corn, sugar beets, soybeans, orchards and vineyards. A critical part of the crop yield modeling process is the identification of crops using multispectral satellite data. This paper explores two strategies to improve crop classification accuracies: (1) use of ancillary data stored in digital format and (2) use of multitemporal data. Ancillary information stored on digital files were used in this research to remove (mask) non‐agricultural areas from satellite image data. Comparison between the classification of masked and unmasked images showed that improvement ranged from 3% to 26% depending on crop type. The multidate classification was performed by compiling an image of transformed spectral bands and three TM‐5 bands. The transformed bands were TM band 4 over TM band 3. Based on the work conducted in this study it is clear that crop type determination from satellite imagery is possible for small field agricultural areas such as those found in Italy.  相似文献   

11.
遥感卫星的波段设置、信噪比及传感器观测角度等因素都会影响作物提取精度。为充分挖掘与发挥Sentinel-2卫星多光谱成像仪(MSI)与Landsat 8陆地成像仪(OLI)在冬小麦信息提取方面的优势,本文以商河县为研究区,基于两数据源的光谱特征、纹理特征、植被指数特征组合数据,利用随机森林(RF)与支持向量机(SVM)对冬小麦进行提取。结果表明:基于单一影像的最优Kappa系数与最优OA分别为0.89和95.13%,基于组合数据源的最优Kappa系数为0.92,最优OA为95.28%,两数据源组合的精度优于单一数据源提取精度;数据组合效果与分类器的性能有关,RF的Kappa系数相对于SVM分别提升0.04、0.20和0.11,OA分别提升2.41%、11.31%和6%,RF对冬小麦提取精度优于SVM。本文研究结果对于构建中高分辨率影像组合的典型农作物分类提取体系具有重要意义。  相似文献   

12.
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   

13.
Large-scale crop yield prediction is critical for early warning of food insecurity, agricultural supply chain management, and economic market. Satellite-based Solar-Induced Chlorophyll Fluorescence (SIF) products have revealed hot spots of photosynthesis over global croplands, such as in the U.S. Midwest. However, to what extent these satellite-based SIF products can enhance the performance of crop yield prediction when benchmarking against other existing satellite data remains unclear. Here we assessed the benefits of using three satellite-based SIF products in yield prediction for maize and soybean in the U.S. Midwest: gap-filled SIF from Orbiting Carbon Observatory 2 (OCO-2), new SIF retrievals from the TROPOspheric Monitoring Instrument (TROPOMI), and the coarse-resolution SIF retrievals from the Global Ozone Monitoring Experiment-2 (GOME-2). The yield prediction performances of using SIF data were benchmarked with those using satellite-based vegetation indices (VIs), including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation (NIRv), and land surface temperature (LST). Five machine-learning algorithms were used to build yield prediction models with both remote-sensing-only and climate-remote-sensing-combined variables. We found that high-resolution SIF products from OCO-2 and TROPOMI outperformed coarse-resolution GOME-2 SIF product in crop yield prediction. Using high-resolution SIF products gave the best forward predictions for both maize and soybean yields in 2018, indicating the great potential of using satellite-based high-resolution SIF products for crop yield prediction. However, using currently available high-resolution SIF products did not guarantee consistently better yield prediction performances than using other satellite-based remote sensing variables in all the evaluated cases. The relative performances of using different remote sensing variables in yield prediction depended on crop types (maize or soybean), out-of-sample testing methods (five-fold-cross-validation or forward), and record length of training data. We also found that using NIRv could generally lead to better yield prediction performance than using NDVI, EVI, or LST, and using NIRv could achieve similar or even better yield prediction performance than using OCO-2 or TROPOMI SIF products. We concluded that satellite-based SIF products could be beneficial in crop yield prediction with more high-resolution and good-quality SIF products accumulated in the future.  相似文献   

14.
Developing techniques are required to generate agricultural land cover maps to monitor agricultural fields. Landsat 8 Operational Land Imager (OLI) offers reflectance data over the visible to shortwave-infrared range. OLI offers several advantages, such as adequate spatial and spectral resolution, and 16 day repeat coverage, furthermore, spectral indices derived from Landsat 8 OLI possess great potential for evaluating the status of vegetation. Additionally, classification algorithms are essential for generating accurate maps. Recently, multi-Grained Cascade Forest, which is also called deep forest, was proposed, and it was shown to give highly competitive performance for classification. However, the ability of this algorithm to generate crop maps with satellite data had not yet been evaluated. In this study, the reflectance at 7 bands and 57 spectral indices calculated from Landsat 8 OLI data were evaluated for its potential for crop type identification.  相似文献   

15.
Numerous efforts have been made to develop various indices using remote sensing data such as normalized difference vegetation index (NDVI), vegetation condition index (VCI) and temperature condition index (TCI) for mapping and monitoring of drought and assessment of vegetation health and productivity. NDVI, soil moisture, surface temperature and rainfall are valuable sources of information for the estimation and prediction of crop conditions. In the present paper, we have considered NDVI, soil moisture, surface temperature and rainfall data of Iowa state, US, for 19 years for crop yield assessment and prediction using piecewise linear regression method with breakpoint. Crop production environment consists of inherent sources of heterogeneity and their non-linear behavior. A non-linear Quasi-Newton multi-variate optimization method is utilized, which reasonably minimizes inconsistency and errors in yield prediction.  相似文献   

16.
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   

17.
Crop yield is mainly dependent on weather, soil and technological inputs. Yield forecasting models have been developed mainly using multiple regression techniques based on biometrical characters of the plants and/or weather parameters. Matiset al. (1985) proposed another approach of crop yield modelling using Markov Chain theory based on biometrical characters. The integration of remote sensing with other technologies has provided an immense scope to improve upon the existing crop yield models. In the present study, multi date spectral data during crop growth period was used in Markov Chain Model to forecast wheat yield. The results indicate that the use of spectral data near the maximum vegetative growth of wheat crop improves the efficiency and reliability of yield forecast about a month before its actual harvest.  相似文献   

18.
Canopy temperature in differentially irrigated and fertilized wheat plots were collected by hand held infrared thermometer from seedling emergence to maturity for two growing seasons (1981–82 and 1982–83). Canopy temperature indices like stress degree day (SDD) and crop water stress index (CWSI) based four-parameter (crop growth stage partitioned) and two-parameter (Non-partitioned) yield models suitable for remote sensing application were developed and tested with observed yield data. From statistical analysis of the models it was concluded that crop growth stage partitioned CWSI or SDD yield model was better than non-partitioned SDD models for predicting wheat grain as well as biological yields.  相似文献   

19.
Penman–Monteith method adapted to satellite data was used for the estimation of wheat crop evapotranspiration during the entire growth period using satellite data together with ground meteorological measurements. The IRS-1D/IRS-P6 LISS-III sensor data at 23.5 m spatial resolution for path 096 and row 059 covering the study area were used to derive, albedo, normalized difference vegetation index, leaf area index and crop height and then to estimate wheat crop evapotranspiration referred to as actual evapotranspiration (ETact). The ETact varied from 0.86 to 3.41 mm/day during the crop growth period. These values are on an average 16.40 % lower than wheat crop potential evapotranspiration (ETc) estimated as product of reference crop evapotranspiration estimated by Penman–Monteith method and lysimetric crop coefficient (Kc). The deviation of ETact from ETc is significant, when both the values were compared with t test for paired two sample means. Though the observations on ETact were taken from well maintained unstressed experimental plot of 120 × 120 m size, there was significant deviation. This deviation could be attributed to, the satellite images representing the actual crop evapotranspiration as function crop canopy biophysical parameters, condition of the crop stand, climatic and soil conditions and the microclimate variation over area of one hectare. However, Penman–Monteith method represents a flat rate of specific growth stage of the crop.  相似文献   

20.
Considering the requirement of multiple pre-harvest crop forecasts, the concept of Forecasting Agricultural output using Space, Agrometeorology and Land based observations (FASAL) has been formulated. Development of procedure and demonstration of this technique for four in-season forecasts for kharif rice has been carried out as a pilot study in Orissa State since 1998. As the availability of cloud-free optical remote sensing data during kharif season is very poor for Orissa state, multi-date RADARSAT SCANSAR data were used for acreage estimation of kharif rice. Meteorological models have been developed for early assessment of acreage and prediction of yield at mid and late crop growth season. Four in-season forecasts were made during four kharif seasons (1998-2001); the first forecast of zone level rice acreage at the beginning of kharif crop season using meteorological models, second forecast of district level acreage at mid growth season using two-date RADARSAT SCANSAR data and yield using meteorological models, third forecast at late growth season of district level acreage using three-date RADARSAT SCANSAR data and yield using meteorological models and revised forecast incorporating field observations at maturity. The results of multiple forecasts have shown rice acreage estimation and yield prediction with deviation up to 14 and 11 per cent respectively. This study has demonstrated the potential of FASAL concept to provide inseason multiple forecasts using data of remote sensing, meteorology and land based observations.  相似文献   

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