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1.
Abstract A methodology has been developed to normalize the multi‐temporal NDVIs derived from NOAA AVHRR data for the atmospheric effects to the least affected NDVI for development of spectral and spectrometeorological (or spectromet, for short) crop yield models. This is found to reduce the noise in NDVI due to varying atmospheric conditions from season to season and improve the predictability of statistical multiple linear regression yield models. The spectromet yield models for mustard crop in the nine districts of Rajasthan state haven been developed based on normalized NDVIs and have been validated by comparing the predicted yields with the estimated from crop cutting experiments by the state Development of Agriculture. 相似文献
2.
农作物长势综合遥感监测方法 总被引：54，自引：5，他引：54
作物收获之前进行大范围作物生长状况评价 ,可以尽早的获得有关作物产量信息。介绍了中国农情遥感监测系统的综合作物长势监测方法。以遥感数据标准化处理、云标识、云污染去除和非耕地去除为基础 ,生成质量一致的遥感数据产品集 ,提取区域作物生长过程。作物长势监测分为实时作物长势监测和作物生长趋势分析。实时的作物长势监测可以定性和定量地在空间上分析作物生长状况 ,分级显示作物生长状况 ,分区域统计水田和旱地中不同长势占的比重。作物生长趋势分析可以进行年际间的生长过程对比 ,从时间轴上反映作物持续生长的差异性 ,统计全国、主产区、省和区划单元 4个尺度的耕地、水田、旱地作物生长过程曲线年际间差异 ,从而为早期的产量预测提供信息。通过处理流程的系统化 ,建设了运行化的作物长势遥感监测分析系统 ,为用户构建了综合的作物实时生长状况 ,苗情的生长趋势分析环境。同时可以依据野外地面实测信息对遥感监测结果进行标定和检验。 1998年以来 ,系统在满足日常运行的前提下 ,技术方法逐渐改进和完善 ,监测结果的精度和可靠性不断得到提高。 相似文献
3.
N. K. Patel M. Chakraborty S. Dutta C. Patnaik J. S. Parihar S. C. Moharana A. Das B. K. Sarangi G. Behera 《Journal of the Indian Society of Remote Sensing》2004,32(2):125-143
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
4.
Sujay Dutta N K Patel S K. Srivastava 《Journal of the Indian Society of Remote Sensing》2001,29(3):175-182
Pre-harvest crop production forecast has been successfully provided by remote sensing technique. However, the probability to get cloud-free optical remote sensing data during kharif season is poor. Microwave data having the capability to penetrate cloud is used in the absence of cloud free optical remote sensing data. Yield models in broad band frequency range are in development stage. Meteorological yield models are developed and predicted yield is combined with area estimated by remote sensing data to provide rice production forecast. This paper describes the methodology adopted for improving the predictability of rice yield before harvest of the crop in Bihar province by taking into consideration meteorological parameters during its growth cycle upto October. Models developed using fortnightly meteorological data have been found to give reasonably fair indications of expected yield of rice in advance of harvest. The yield predictions have been made based on meteorological data and effective rainfall based on water requirement calculations representing a group of districts under similar agro-climatic zones, which could be further improved by incorporating meteorological data of individual districts within each group. 相似文献
5.
Abstract Recent investigations demonstrated that inter‐year NOAA‐AVHRR NDVI variations at the middle of the rainy season can provide information on annual crop yields in Sahelian countries. This line of research is presently extended to the consideration of multitemporal NDVI data for several years (1986-1991) pre‐processed by a proven methodology. The investigation was conducted using NDVI and crop yield data from the sahelian sub‐districts of Niger. The results confirm that geographically standardized NDVI data are efficient for crop yield forecasting, but notable differences exist in this prediction capability depending on the beginning of the season. Late beginnings of the growing (rainy) season (after the end of June) allow optimum forecasting only after mid‐August, while early beginnings lead to anticipate the forecasting capability but also to decrease its accuracy. The importance of these findings in the context of an early warning system is finally discussed. 相似文献
6.
The most important advantage of the low resolution National Oceanic and Atmospheric Administration’s Advanced Very High Resolution
Radiometer (NOAA AVHRR) data is its high temporal frequency and high radiometric sensitivity which helps in vegetation detection
in the visible and near-infrared spectral regions. In areas where most of the crop cultivation is in large contiguous areas,
and if the AVHRR data are selected for time period such that the crop of interest is well discriminated from other crops,
these data can be used for monitoring vegetative growth and condition very effectively. The present study deals with the application
of AVHRR data for the monitoring of the wheat crop in its seventeen main growing districts of the Rajasthan state. The fourteen
date AVHRR data covering the entire growth period have been used to generate the normalized difference vegetation index (NDV1)
growth profile for the crop by masking the non-crop pixels following the two-date NDVI change method. The growth profile parameters
and other derived parameters, such as post-anthesis senescence rate and areas under the entire growth profile or under selected
growth periods have been related to the district average wheat yield through statistical regression models. Various methods
adopted for wheat pixels masking have been critically evaluated. It is found that the wheat yield can be predicted well by
the area under the profile in different growth periods. 相似文献
7.
R. P. Dubey N. D. Ajwani R. R. Navalgund 《Journal of the Indian Society of Remote Sensing》1991,19(1):27-44
An attempt has been made to generate crop growth profiles using multi-date NOAA AVHRR data of wheat-growing season of 1987–88 for the districts of Punjab and Haryana states of India. A profile model proposed by Badhwar was fitted to the multi-date Normalised Difference Vegetation Index (NDVI) values obtained from geographically referenced samples in each district. A novel approach of deriving a set of physiologically meaningful profile parameters has been outlined and the relation of these parameters with district wheat yields has been studied in order to examine the potential of growth profiles for crop-yield modelling. The parameter ‘area under the profile’ is found to be the best estimator of yield. However, with such a parameter time available for prediction gets reduced. Combination of different profile parameters shows improvement in correlation but lacks the consistency for individual state data. 相似文献
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Spatial differences in drought proneness and intensity of drought caused by differences in cropping patterns and crop growing environments within a district indicate the need for agricultural drought assessment at disaggregated level. The objective of this study is to use moderate resolution satellite images for detailed assessment of the agricultural drought situation at different administrative units (blocks) within a district. Monthly time composite NDVI images derived from moderate resolution AWiFS (60 m) and WiFS (180 m) images from Indian Remote Sensing satellites were analysed along with ground data on rainfall and crop sown areas for the kharif seasons (June – November) of 2002 (drought year), 2004 (early season drought) and 2005 (good monsoon year). The impact of the 2002 meteorological drought on crop area in different blocks of the district was assessed. The amplitude of crop condition variability in a severe drought year (2002) and a good year (2005) was used to map the degree of vulnerability of different blocks in the district to agricultural drought. The impact of early season deficit rainfall in 2004 on the agricultural situation and subsequent recovery of the agricultural situation was clearly shown. Agricultural drought assessment at disaggregated level using moderate resolution images is useful for prioritizing the problem areas within a district to undertake, in season drought management plans, such as alternate cropping strategies, as well as for end of the season drought relief management actions. The availability of ground data on rainfall, cropping pattern, crop calendar, irrigation, soil type etc., is very crucial in order to interpret the seasonal NDVI patterns at disaggregated level for drought assessment. The SWIR band of AWiFS sensor is a potential data source for assessing surface drought at the beginning of the season. 相似文献
10.
Anup K. Prasad Lim Chai Ramesh P. Singh Menas Kafatos 《International Journal of Applied Earth Observation and Geoinformation》2006
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
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Susanna Azzali 《国际地球制图》2013,28(3):15-26
A method to correlate crop production in Zambia to the yearly evolution of the Normalized Difference Vegetation Index (NDVI) is proposed. The method consists of the analysis of remote sensing data together with meteorological data and simulated crop production to obtain indicators of crop production. The accuracy of these indicators is assessed with statistical data. The main objective was to assess whether the NDVI‐time series extracted from NOAA‐AVHRR‐images , having a pixel resolution of 73 km may give reliable information on crop production in Zambia where agricultural areas cover just 1% of the land area. The mean NDVI‐value of several pixels, e.g. for one province or other administrative units, relates to the dominant type of vegetation in the area under consideration. It is shown that the 7.3 km NDVI‐data give reliable indications on crop production in Zambia, when small areas (200–450 km2 large ) are considered where agricultural land use is intensive. This implies that preliminary analysis is required to localize the agricultural areas. This has been done by means of high resolution satellite images i.e. LANDSAT‐MultiSpectral Scanner. Consequently, the NDVI‐time series of the ‘agricultural ‘ pixels are used to calculate crop growth indicators which can be applied to assess the crop production. 相似文献
14.
This paper reports acreage, yield and production forecasting of wheat crop using remote sensing and agrometeorological data for the 1998–99 rabi season. Wheat crop identification and discrimination using Indian Remote Sensing (IRS) ID LISS III satellite data was carried out by supervised maximum likelihood classification. Three types of wheat crop viz. wheat-1 (high vigour-normal sown), wheat-2 (moderate vigour-late sown) and wheat-3 (low vigour-very late sown) have been identified and discriminated from each other. Before final classification of satellite data spectral separability between classes were evaluated. For yield prediction of wheat crop spectral vegetation indices (RVI and NDVI), agrometeorological parameters (ETmax and TD) and historical crop yield (actual yield) trend analysis based linear and multiple linear regression models were developed. The estimated wheat crop area was 75928.0 ha. for the year 1998–99, which sowed ?2.59% underestimation with land record commissioners estimates. The yield prediction through vegetation index based and vegetation index with agrometeorological indices based models were 1753 kg/ha and 1754 kg/ha, respectively and have shown relative deviation of 0.17% and 0.22%, the production estimates from above models when compared with observed production show relative deviation of ?2.4% and ?2.3% underestimations, respectively. 相似文献
15.
针对中国开展的国外农作物产量遥感估测大多依靠中低分辨率耕地信息、省级(州级)或国家级作物产量统计数据的现状,本文以美国玉米为例,探讨利用多年中高分辨率作物分布信息、时序遥感植被指数和县级作物产量统计数据开展国外重点地区作物单产遥感估测技术研究,以期进一步提高中国对国外农作物产量监测精度和精细化水平。首先,利用美国农业部国家农业统计局(NASS/USDA)生产的作物分布数据(CDL)获得多个年份玉米空间分布图,并对相应年份250 m分辨率16天合成的MODIS-NDVI时序数据进行掩膜处理,统计获得每年各县域内玉米主要生育期NDVI均值;其次,以各州为估产区,以多年县级玉米统计单产和县域内玉米主要生育期NDVI均值为基础,建立各州玉米主要生育期NDVI与玉米单产间关系模型;然后,通过主要生育期玉米单产和玉米植被指数间拟合程度,筛选确定各州玉米最佳估产期和最佳估产模型。最终,利用最佳估产模型实现美国各州玉米单产估测和全国玉米单产推算。其中,建模数据覆盖时间为2007年—2010年,验证数据为2011年。结果表明,应用最佳估产模型的2011年美国各州玉米单产估测相对误差在-4.16%—4.92%,均方根误差在148.75—820.93 kg/ha,各州估测结果计算获得全国玉米单产的相对误差仅为2.12%,均方根误差为285.57 kg/ha。可见,本研究的作物单产遥感估测技术方法具有一定可行性,可准确估测全球重点地区作物单产信息。 相似文献
16.
With the availability of high frequent satellite data, crop phenology could be accurately mapped using time-series remote sensing data. Vegetation index time-series data derived from AVHRR, MODIS, and SPOT-VEGETATION images usually have coarse spatial resolution. Mapping crop phenology parameters using higher spatial resolution images (e.g., Landsat TM-like) is unprecedented. Recently launched HJ-1 A/B CCD sensors boarded on China Environment Satellite provided a feasible and ideal data source for the construction of high spatio-temporal resolution vegetation index time-series. This paper presented a comprehensive method to construct NDVI time-series dataset derived from HJ-1 A/B CCD and demonstrated its application in cropland areas. The procedures of time-series data construction included image preprocessing, signal filtering, and interpolation for daily NDVI images then the NDVI time-series could present a smooth and complete phenological cycle. To demonstrate its application, TIMESAT program was employed to extract phenology parameters of crop lands located in Guanzhong Plain, China. The small-scale test showed that the crop season start/end derived from HJ-1 A/B NDVI time-series was comparable with local agro-metrological observation. The methodology for reconstructing time-series remote sensing data had been proved feasible, though forgoing researches will improve this a lot in mapping crop phenology. Last but not least, further studies should be focused on field-data collection, smoothing method and phenology definitions using time-series remote sensing data. 相似文献
17.
This study explores the possible linkages of El Nino/Southern Oscillation (ENSO) with vegetation and rainfall patterns, vegetation activity and food grain yields, in arid and semi-arid regions of western India. A sequence of 20-year (1981–2000) monthly maximum Normalized Difference Vegetation Index (NDVI) data from the Advanced Very High Resolution Radiometer (AVHRR) and monthly rainfall from 160 stations were examined to study the seasonal patterns and their relation to ENSO activity. In addition, a direct (ENSO-crop yield) linkage and an intermediate (ENSO-NDVI) linkage of agricultural responses to ENSO were also investigated. The results indicate below-normal seasonal NDVI and rainfall associated with El Nino (warm) events, except during 1997, while positive anomalies occur during La Nina (cold) events. Sea surface temperature (SST) anomalies from NINO 3 region (5°N–5°S; 150°W–90°W), as an indicator of ENSO were significantly correlated with NDVI anomalies, rainfall anomalies and yield anomalies but the Southern Oscillation Index (SOI) was significantly related to NDVI anomalies only. NDVI anomaly patterns correspond to rainfall variability including that associated with ENSO activity. The observed strong intermediate linkage between yield anomalies and NDVI anomaly signal (r = 0.609) indicates that NDVI is an ideal index for understanding and analysing agricultural response to ENSO climate teleconnections. 相似文献
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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
20.
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