最新刊期
卷 ， 期 6 ， 2004
- 摘要：The paper introduces China Crop Watch System with Remote Sensing （CropWatch）,including its contents, technical innovation and operational development, validation and future activities. Crop Watch contents 5 components, consisting of crop condition monitoring, crop acreage estimation and yield prediction, grain production estimation, cropping index and crop structure monitoring, and drought monitoring. Each component is described briefly in this paper and detailed in following papers. Since 1998, the CropWatch has been put in operation. Each year, there are 7 monthly bulletins released, covering crop condition, crop acreage and yield, drought, agro-meteorology, cropping index and crop structure, and also grain production. And there are 21 10-days announcements released, covering drought and crop condition. During the operational period since 1998, the technology used by the CropWatch are consistently improved. All AVHRR image from 1991 are processed for calibration and correction precisely which began in 2000, including sensor calibration, zenith correction, cloudy detection and masking, geometric correction, atmospheric correction, BRDF correction,and NDVI and surface temperature calculation. The CropWatch uses both different image and growing profile of NDVI to monitor the crop condition, estimates crop acreage using remote sensing and sampling technique based on stratification. The cluster-sample framework is used to estimate plant proportion with TM and Radarsat image as samples, and transect-sampling framework is used to survey crop type proportion in planted-area. At the same time, The Crop Watch continuously expands its monitoring area. From 1998 to 2003, its monitoring area has been expanded from east China to national wide, to north and south America, to Australia and Thailand. Meanwhile, the Crop Watch continuously expands its monitoring contents, from crop condition monitoring, crop acreage estimation and crop yield prediction, to grain production, crop structure, and cropping index. The Crop Watch has its own validation programme. Each year, a few validation sites have been selected to do the field measurement for verifying specific results. For example, in 1999, Jiangning in South China were Dehui in North China were two sites. The crop LAI every 10 days and crop type proportion have been measured to verify the crop condition and crop acreage. Validation shows that the accuracy are up to 97% for plant proportion and 95 crop type proportion.关键词：CropWatch;monitoring;China
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发布时间：2021-06-10 - 摘要：It is possible to evaluate the crop condition and yield status before the harvest for a large scale of area. This paper introduces an integrated method for crop condition together with water shortage monitoring with remote sensing during the crop season. Both NOAA AVHRR and SPOT VGT data are used. VGT data can be used directly since it has been calibrated and corrected before being delivered to the users. AVHRR data are not lucky enough, it need to process on radiance calibration, atmospheric correction,and cloud masking, which generates a consistency AVHRR dataset for the whole China territory since 1991. The non-arable land is masked too in order to highlight the crop information on the arable land. The land use map at a scale of 1:100 000 is used. There are two ways to monitor the crop condition. The differential image of 10 day composite NDVI images in current decade and the same decade in previous year is used to monitor the current crop conditions. The differential image is divided into five categories and assigned with red, yellow, green, cyan and blue to indicate the conditions. Crop condition figures at provincial level are calculated for paddy field and upland area. The result allows users to quickly assess where the crop conditions have been deteriorated, remained or improved. Except for the differential image, the time series of NDVI during the crop season at different scale, such as nation-wide, main production region, province, county, and at different types of paddy field and upland, are used to evaluate the trend of crop condition and compared with the same profile at previous year. The profile also shows the information of crop yield if you look at the peak value, increase or decrease rate, and pattern of curve. A GIS is developed to integrate crop condition information and provide an environment of analysis and evaluation. The GIS can automatically generate the differential image and draw time series of NDVI by retrieving the data from existing database. The ground survey data are integrated into the GIS to review the real situation at field and validate the monitoring results. The meteorological data, like temperature, rainfall, and sunshine duration are integrated too to provide the background information for the analysis. The methods are used to monitor the crop condition over the China since 1998. During the crop season, the bulletin of crop condition are generated every 10 days from later March to Later October. These information are invaluable to decision makers and analysts within government bodies for better management of agricultural production and grain administration. In 1998, China suffered serious flood problem, but the damage to the crop was only limited to narrow areas along the rivers, and the damage were compensated by remote area where had good water supply.关键词：crop condition;remote sensing;time series
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发布时间：2021-06-10 - 摘要：Time series of NDVI data have extensive applications in crop condition monitoring and yield prediction.The NDVI data are polluted by cloud even with precise correction and 10 days composition. And due to its 1 km resolution, the data of one pixel contains information more than crops themselves. In this paper,we present a method to extract crop growth profile using time series of SPOT-VGT NDVI and land cover data. In order to remove cloud pollution and extract the characteristics of the vegetation dynamics,the Harmonic Analysis of Time Series （HANTS） algorithm is performed on the time series of NDVI data.HANTS can analyze the time signal for each individual pixel and use the series of harmonic sine and cosine waves to reconstructe waves fitting into the total period.Results show that the cloud polluted data are recognized and replaced successfully by reconstruction of seasonal NDVI profiles. In order to monitor the crop condition and indicate yield level, the time series of NDVI should contain as much crop information as possible,from which the extraced indicators should be related to crop yield performances. Land cover data is used to mask non-arable land,and the NDVI are calculated only for arable land pixel.For a regional, like a county or a province, the pixels within a region are used only for those that NDVI is higher than 0.15. The weight average method is used to calculate the regional NDVI which can indicate the crop information, better than other methods. The weight is the proportion of arable land within a pixel. By comparing the profiles of whole country,a region,a province and a county, we can conclude that the profile is affected by the crop calendar and the cultivation structure. If a region has same crop calendar and pure crop structure, the crop profile is a good indicator for crop condition and yield. The yield stratification zone and county level are proposed to calculate regional NDVI profile. The profiles are validated by using field measuring LAI.It shows that HANS can increase the relative coefficient of NDVI and LAI by 5%—11% than raw data. The weight average method can increase the relative coefficient of NDVI and LAI by 14%—17% than other methods.关键词：NDVI;time series;harmonic analysis;regional crop profile
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发布时间：2021-06-10 - 摘要：Scientific investigations indicate that global change information can be derived from the 1 km advanced very high resolution radiometer （AVHRR） data from NOAA satellite series. This paper describes the processing chain of China territory 1-km AVHRR dataset and its applications. Twelve years of AVHRR raw data from 1991 to 2003 have been collected. A dataset of over 9000 AVHRR raw images has been archived. These images cover China territory and the sensor includes AVHRR/2 and AVHRR/3 from NOAA-12 to NOAA-16. The processing algorithms are solicited and applied to the dataset to produce prototype and operational high level products for regional geo-science research. The processing chain uses an improved calibration method, which accounts for sensor degradation, and uses the SMAC （Simplified Method for Atmospheric Correction） method for atmospheric correction of the reflectance measured in AVHRR channels 1 and 2. The ozone data comes from TOMS （Total Ozone Mapping Spectrometer） daily measurements, the daily water vapor and surface pressure are obtained from the CMA （China Meteorological Administration） and the daily aerosol optical depth at a wavelength of 550 nm is retrieved from the ground visibility using a parameterization method. The BRDF correction in AVHRR imagery is performed and the parameters required is derived from the 1∶1,000,000 scale land cover types of China. For cloud detection, the CLAVR algorithm is applied but some parameters are modified for China territory, it uses all five AVHRR channels to estimate cloud cover through a series of tests and the tests are done sequentially using threshold values. LST （Land Surface Temperature） is determined using semi-empirical method （the split window method） and separatively accounting for atmospheric attenuation and soil emissivity. Daily data are composed with MVC （Maximum NDVI Composite of 10 days NDVI data）. The processing chain outputs are surface reflectance in channel 1 and 2, brightness temperature in channels 3-5, cloud （contaminated pixel） mask associated with each pixel, land surface temperature, and NDVI. The dataset products have been used for various studies like crop condition monitoring.关键词：time series dataset;China territory;AVHRR
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发布时间：2021-06-10 - 摘要：Crop plots are very small in China due to special farm-use rules. Image classification techniques are limited in crop acreage survey using remote sensing. In this paper, we analyze this problem and provide a substitute methodology to estimate crop acreage. In this methodology, crop stratification is fundamentd. Proportional of main crop types as well as physical factors of tempera- ture, precipitation, soil type and sun radiation are considered. There are about 11 strata in China at the first level based on physical factors, 44 strata at the second level based on crop proportion and 102 strata at the third level based on arable land intensity. Two individual sampling frameworks are used. The cluster sampling is used to estimate the proportion of planted area on arable land with remote sensing data, mostly Landsat and Radarsat data, currently also ENVISAT ASAR data are used. The clusters are defined as a map sheet at a scale of 1:1:100 000 （about 1/16 Landsat TM scene）. And images are selected based on cluster sample randomly for each crop season. After atmospheric correction, geometric correction non-arable land masking, remotely sensed images are classified by ISODATA unsupervised classification, and then the planted areas are labeled by considering NDVI value. The planted area proportions are calculated for each stratum. The transect sampling framework is used to estimate the proportions of different crop type within planted area. To identify the crop proportion of a small parcel, field works should be used since it is impossible to make crop classification with remote sensing data cost-effectively. The transect sampling actually is a two-stage sampling. In the first stage, PSUs are selected randomly on a 4km×4km area frame. In the second stage, the selected PSUs are sampled only along the road within PSUs, called transect line. The sampling works in the field are to take pictures along the road within PSUs with 100 m buffer, and a GVG system is designed for this purpose. Proportions of every crop type are calculated for each stratum. Crop acreages are calculated under the support of current arable database. For every crop type, the planted acreage is the arable area multiply by planted proportion and crop type proportion of stratum. The estimation of early rice acreage in 2003 in China is presented as a case study. Results show this methodology is feasible. This methodology has adopted in China since 1998. And the experience shows that the stratification schema is efficiency, and the two individual sample frameworks can generate accurate estimation of crop acreage.关键词：crop proportion;crop type proportion;cluster sampling;transect sampling;remote sensing;crop acreage
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发布时间：2021-06-10 - 摘要：GVG instrument is an integration of video capture card,GPS receiver and GIS for collecting the crop type proportions in the field. VIDEO and GPS are integrated into the GIS installed in a notebook. GVG mounted on car or motor takes records with video camera during the movement. The car should not be fast than 40 km/h in order to acquire high quality of pictures. When GVG is working, it records pictures and the position from GPS as well as geographical attributes from GIS data layer, for example county code, into a temporarily database. One person is enough to operate GVG in addition the driver. When fieldwork is done, the GVG provides the function to review every pictures recorded during the sampling. If the picture does not contain the arable land, it shall be discarded, otherwise, the proportion of every crop types will be recorded manually, and the picture will be recorded too. Once all pictures are reviewed for a transect line, it is easy to calculate the proportion of crop types. The crop proportions for a county can be calculated from all transect lines within the county, and the same for stratum. GVG has been used to obtain the crop type proportions of entire country since 1999. The extensive validation shows the GVG can have the accuracy of 95% for main crops, including wheat, rice, maize and soybean. According to the principle of GVG, it has multi-purpose applications, not only on the crop type proportions, but also on environment monitoring for example.关键词：GVG;area sampling;crop type proportion
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发布时间：2021-06-10 - 摘要：In this Paper, the accuracy of planted area proportion using Landsat TM is assessed with two pilot sites at Kaifeng of Henan province and Taigu of Shanxi province. In Kaifeng, one Landsat TM imagery on April 1 2001 was acquired for summer crop. In Taigu, Landsat TM image on Oct. 14 2003 was acquired for autumn crop. Landsat TM images were processed for extracting the planted area proportion with 6 steps: atmospheric correction, geometric correction, SAVI calculation, non-arable land masking, unsupervised classification,and labeling. Planted pixels were counted and the planted area proportion can be calculated by the divided the number of arable land pixels extracted from the land use map at a scale of 1∶100 000. At the same time, in order to assess the accuracy of planted area proportion from Landsat TM, an IKONOS imagery （11km×11km Coverage） on Mar. 21, 2001.was acquired for Kaifeng. After an unsupervised classification based on ground survey, the planted area proportion are obtained. A QuickBird imagery was applied to Taigu site, and the planted area proportion are obtained by field works, which fills every parcel with crop or landuse type. These two planted area proportions for two sites can be compared to assess the accuracy of extracting the planted area proportion with TM data. It shows that the accuracy is 99% in Kaifeng and 97% in Taigu. And this is acceptable for the operational purpose of China Crop Watch System with remote sensing.
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发布时间：2021-06-10 - 摘要：In this paper,we selected a test area about 5km by 5km in Taigu of Shanxi Province to assess the accuracy of crop type proportion obtained with GVG instrument on transect line. Roads on the QuickBird imagery were digitized with length of 255km, and 16.4km of the roads were selected as transect line for field sampling.The sampling ratio was about 6.4%. Crop type proportion for each crops was obtained using GVG instrument on the transect line.323 pictures were taken with clear crop identification. The density of taking picture was about 1 picture for every 50 meter. At the same time, the intensive field works were taken to map the crop pattern in detail. QuickBird imagery was used to make parcel map with a unique identity code. Field survey recorded the crop type for each parcel. If more than one crop exist in a parcel,the proportions are assigned to different crops. The crop type proportion can be calcuated with detailed crop pattern map. By comparing the crop type proportion derived from sampling and mapping,we could draw conclusions; Crop type proportion from GVG instrument on transect line has a very high accuracy for main crops, 98.7% for maize, 98.98% for soybean and 95 12% for vegetables. The accuracy is acceptable for the operational use of China Crop Watch System. For other crop types with low proportion, the accuracy is low and cannot be used in China Crop Watch System. This means GVG instrument can only be used to obtain the main crop proportion. Crop type proportion from GVG instrument on transect line is somewhat larger than the true values, this means that the missing samples for some crop types with low proportion. In Taigu test area, sesame, cottage and some medicinal vegetables are not sampled on transect line with GVG instrument关键词：crop type proportion;accuracy assessment;transect sampling framework;GVG
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发布时间：2021-06-10 - 摘要：In this paper, the need of implementing sampling network for crop type proportion is analyzed and the strategy to implement the sampling network on regional division, sampling contents, team, frequency and date for different division as well as the quality control was also discussed. The entire country is divided into 7 regions for field sampling by 7 institutes of CAS in 1999, and then 9 regions was optimized in 2002 for better scheduling and meeting the crop calendar. The sampling was to get the crop type proportion for every growing season. According to the crop calendar,different regions have different sampling frequency, for example, north-east region only need to sample once a year, whereas in south China, three times are needed for sameplaces. For each region, the deadlines are settled. The sampling works should be completed and the results have to be delivered before the deadline. Crop type proportions for sampling lines, counties, strata and province were calculated. Guideline and operational criteria are formulated to ensure the quality,and intensive training was made every year for the sampling operators. There are many factors affecting the sampling works. The costs is very high, including wages, vehicles, equipments, training, technical support, etc. The sampling efficiency needs to be improved. Sometimes, proportion data of only a few crop types were collected with many times,consuming a large amount of energy and people resources. How to manage and supervise the sampling team always is a difficult question, and the sampling frequency and schedule should be exactly followed. The quality control is also a problem. It is mostly rely on the sampling team themselves. There are 6 steps to implement it every year. The first step is training and planning at the beginning of the year. The second one is the supervision and technical assistance before and during the sampling procession. Third is the sampling in the field, and then is the calculation in house, and then is the quality control and delivery, and the last step is the summarizing to evaluate the sampling work at the end of the year. The sampling network has been used for 6 years. It shows the sampling network is efficiency.关键词：transect sampling;implementation;crop proportion
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发布时间：2021-06-10 - 摘要：In this paper, the authors develop an operational method to predict crop yield in China. Crop yield stratification is fundament, in which each stratum has own yield model for different crops. The level of crop yield （winter wheat, corn, rice, et al.） as well as physical factors of temperature, precipitation, soil type and sun radiation are considered. There are about 11 strata in China at the first level based on physical factors, 39 strata at the second level based on crop yield and 133 strata at the third level based on agro-meteorology stratification. Literature study goes review has been made through the journals and books since 1980s for collecting agro-meteorological models and relevant application area. There are 114 models for wheat, 25 models for maize, 70 models for rice and 36 models for soybean. For every model, the suitable area has been defined by considering the original application area and crop yield stratification, and the parameters are generated by regression method of historical crop yield data and meteorological data. The crop yield prediction is stratum by stratum. To one stratum, there are many meteorological stations and counties. It is impossible to do the model calibration for each station or each county due to the lack of data. It may have the yield data for each county, but it is difficult to have the meteorological data at the same period for this county. Only those counties with both yield and meteorological data are selected to calibrate the yield model. The yield predictions are done for those counties. Spatial interpolation is used to extrapolate the yield at a station to whole county or whole stratum. Each pixel has its own yield data. The non-arable land is masked with landuse map and the average yield at a county or a stratum is calculated. At the end of this paper, a case study is presented to predict the yield of winter wheat in 2003.关键词：crop yield prediction;operational;agro-meteorological model
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发布时间：2021-06-10 - 摘要：In this paper we developed an approach using time series Normalize Difference Vegetation Index （NDVI） derived from SPOT VGT for crop yield predicting in American during a five-year span （1998—2002）. In order to remove cloud and extract the characteristics of the vegetation dynamics,the Harmonic Analysis of Time Series （HANTS） algorithm was used on the time series of NDVI image.To exploit effectively the time series of NDVI,linking them as much as possible to crop growing conditions,indicators which can be related closely to crop yield were extracted and used for building the predicting models. The weight average method was used to extract crop growth profile with land cover and SPOT Vegetation data. And then indicators were retrieved from the crop growth profiles, including ascend speed,maximum, descend speed, accumulative total before maximum and accumulative total after maximum. At the mean time, the time series of winter wheat yield are processed using a linear upward trend function in 1980 to 2002 to reduce the tendency of the yield. The weather yield is the difference of the actual yield and the trend yield. The weather yield will be predicted with remote sensing indicators. The weather yield and corresponding indicators are regressed. Only those indicators with high correlation coefficient are selected. The wheat yield are the summary of weather yield and the trend yield. The model was used to predict winter wheat yield in America. The difference is about -11.4% to 7.01% by comparing with USDA NASS data. And the relative coefficient between predicting yield and NASS yield is 0.89.关键词：crop yield;remote sensing;American;winter wheat
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发布时间：2021-06-10 - 摘要：Usually crop planting structure derived from the statistical data are quite later due to time consuming of statistic method. The government normally needs the crop planting structure as early as possible, better if it can be obtained during the crop season, so that it allows the government has enough time to make decision for next crop season. This paper presents a fast inventory method of crop planting structure, based on the GVG instrument and transect sampling framework. Then crop planting structure inventory for summer and autumn crop over China in 2002 have been carried out. It is found that the rate of cereal to cash crop within summer crop is 58%: 21%, and that of autumn crop 79%: 14%. It is very obvious that cereal crops still account on very high proportion in the crop structure. According to surveyed results, the difference of crop planting structure over China varies temporally and spatially great. The soybean proportion of Hei Longjiang province ranks first, up to 38%, and Hei Longjiang is main producing area of soybean in China. Jilin and Liaoning provinces almost have the same proportion of spring maize, more than 71%. Winter wheat is a major crop of summer crop in the Huanghuihai area, in which the winter wheat proportion of Hebei province is more than 97%, and summer maize is a major crop of autumn crop, in which the summer maize proportion of Henan province is up to 82%. On the two sides of the Yangtse River there exists very big change of crop proportion between winter wheat and oil rapeseeds. On the north side of Yangtse River, winter wheat and oil rapeseeds almost have the same rank, but on the south side of Yangtse River oil rapeseeds ranks first in the summer season, and middle rice and later rice are the major crops of autumn crop, more than 66%. Rice is major crop of summer and autumn crop in the southern China, and in which the proportion of vegetable and fruit of Guangdong province is up to 29%. Middle rice and summer maize are the major crops of autumn crop in the southwest region, in which the proportion of cotton, flux seed and sugar of Yunnan province is up to 19. Tobacco of Yunnan province ranks first in China. Effects of agricultural structure adjustment are very great in recent years, especially in the developed and adjacent region where the proportion of vegetable and fruit is very high, such as Tianjin city up to 34%.关键词：crop planting structure;inventory;GVG instrument;transect sampling framework
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发布时间：2021-06-10 - 摘要：Cropping index is a very important indicator, which reflects the situation and degree of arable land to be used by a certain planting system at a certain period. Crop growth dynamic can be monitored by the time series of NDVI data. The differences of crop growth show in the curve of time series of NDVI clearly. The curve of time series of NDVI describes the crop process of seeding, jointing, tasseling, and harvesting and so on. There exist some peaks and valleys on the curve of time series of NDVI. These peaks correspond the period of crop tasseling, and valleys the period of crop harvesting. The link that connects the cropping index with a time series of NDVI is the seasonal rhythm of agricultural crops in a year. Time series of NDVI contain the rhythm of vegetation growth and wilt. But due to cloud contamination, the curve of time series of NDVI has a lot of noise. This paper tried to remove the cloud contamination from the curve of time series of NDVI with the assistance of HANTS software. The reconstructed time series of NDVI can accurately reflect the biophysical processes of planting, seedling, elongating, heading, harvesting of agricultural crops. So according to the period of time series of NDVI, the dynamic information of crop under cultivated land can be extracted and cropping index of arable land can be further calculated. The paper presentsu remote sensing method for extracting the cropping index and then extracted the cropping index from 4 years of VEGETATION decadal composite time series of NDVI over the period of 1999 to 2002. The validation results show a high accuracy compared to the 4 test sites ground data and other available information.关键词：cropping index;time series of NDVI;remote sensing
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发布时间：2021-06-10 - 摘要：Cropping index is a very important indicator. Food production potential in the future can be forecasted by the cropping index potential. However,there were little studies on the cropping index potential. Several references estimated the cropping index potential over China by the very simple means or with the provincial statistical data. The results were different. This paper put forward the methods of calculating cropping index potential based on GIS technology. The agricultural statistical data were processed. The statistical data of the area of arable land of each county were replaced by the data of the area of arable land extracted from remotely sensed data due to the quality of the statistical data. The maximum of cropping index of each county then was calculated further. The climatic data were collected and processed, too. After analyzing the relationship between the maximum of cropping index and the accumulated temperature, precipitation and sun hour data, this paper put forward the model of cropping index potential by the means of the outside envelope. Then this paper calculated the cropping index potential over China with 1 km resolution based on the model the paper proposed. The cropping index potential of the whole China and each province were obtained by using the spatial statistical methods. According to the results, the cropping index potential of the whole China is 198.5%.关键词：cropping index potential;GIS;accumulated temperature;precipitation
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发布时间：2021-06-10 - 摘要：In China, the technology of remote sensing for crop monitoring is already mature. It can provide an important data source, just as crop production, for monitoring the national grain supply and demand balance. Also, it can monitor crop growth in crop life. The data from the technology not only has spatiality and multi-type, but also is timelier than the agriculture statistical data, especially for monitoring the annual or seasonal grain supply and demand balance. Therefore, the technology should be integrated into the operational system of national grain management. In this paper, firstly, a short-term model for the national grain supply-demand balance, which can be applying to predict grain balance before the end of year, was brought forward. Then, applying the results of remote sensing and statistical data in the model constructed, the authors studied grain supply-demand balance in China at the turning point of the century. By comparing the two results derived from remote sensing monitoring and agriculture statistical data, the authors believed that the information of remote sensing monitoring could be applied to analyze grain supply-demand balance, while the reliability can be improved and periodicity can be shortened. Hence, the results of remote sensing monitoring should be taken as one of the most important data sources to monitor grain supply-demand balance by the departments of regulating and controlling grain. Moreover, by calculating and analyzing the results of China’s grain supply-demand balance during 1999—2002. It was Suggested that China’s grain at the turning point of the century was approximately balanceable. But because the grain stocks change of government and farmer household was very large at the end of the last century, moreover, with the descending farmer household’s grain stock in recent three years, it was formed that the surplus complexion of grain in market, which should be valued and taken some measures timely by the government during the process of adjusting the agricultural structure.关键词：grain;balance;supply and demand;remote sensing;statistic
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发布时间：2021-06-10 - 摘要：Agricultural information is various and from different sources ,such as remote sensing monitoring, statistic data, market research, meteorological information, and in-situ measurement. These data distribute dispersedly in different government departments or units, and lack mutual exchange and validation, comprehensive analysis and organic integration. In order to take advantage of various kinds of data, comprehensive analysis should be strengthened. In this paper, agricultural information from various sources, such as cropland acreage, crop acreage, crop yield, crop growth and grain yield, is compared and analyzed preliminarily on the aspect of content and expression fashion,and statistic approach and so on. Compared agricultural information by remote sensing with other information, cropland acreage by remote sensing is accurate and credible. Crop growth monitored by remote sensing is much more direct and comprehensive. Crop acreage by remote sensing is larger than that by statistic data（statistic crop acreage maybe lesser）. Crop yield of per units estimated by remote sensing is lower than statistic data（statistic data maybe higher）. Crop yield estimated by remote sensing is approaching relatively to statistic data,but there is still difference. Crop yield estimated by remote sensing maybe higher,and by statistic data maybe lower . Through comparison of various agricultural information from different sources, it is affirmative that agricultural information by remote sensing has advantages in objectivity, spatio-temporal continuity, comparability, forecast-ability and lower cost. On the other hand, agricultural information by remote sensing has deficiency and localization. Though analysis in this paper it is indicated that both remote sensing information and other information are valuable in existence, and their accuracy need be enhanced. Different agricultural information could not be substituted they are complementary and validated for each other. In order to reflect agricultural circumstances more entirely and exactly, agricultural information from different sources should be comprehensively analyzed and integrated.关键词：agricultural information;comparison;remote sensing;statistics
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发布时间：2021-06-10 - 摘要：Thailand is the largest exporter of rice in the world grain market with a reputation of high grain quality.One-half of the rice land was located in the Northeast region.Planted area changes monthly in tropical agriculture,unlike that of agriculture in temperate zones.This paper estimated the area planted with rice using remote sensing data in center-northeast of Thailand.We propose a method which can be used to estimated the rice planted area in tropical regions by RADAR data.The arable land area was measured using Landsat Thematic Mapper （TM） data acquired in the dry season and identified monthly rice planted fields using Synthertic Aperture Radar（SAR） data acquired in the rainy season（planting season）.Landsat TM data （path-row:128-49） acquired on 7 April 2002 was used to identify the agricultural area.To detect the planted fields, RADARSAT SCN（ScanSAR Narrow）data were employed. The parameters of SHR are as follows:C-band,HH（horizontal transmit and horizontal receive） polarization,beam mode: W2,s5,s6,orbit: 35606 descending,scene center: 15°54′N and 102°41′E.Four images were acquired on 02-05-2002, 9-06-2002, 01-08-2002 and 31-08-2002.Arable land area was labeled using unsupervised classification of the TM data.According to statistic result about time series of backscatter coefficients of rice, four type of models were built to estimate the monthly change in planted area.Since rice-planting is not carried out simultaneously and the planted area change monthly, some assumptions were necessary for estimating the planted area.Assuming that the intensity of planting exhibits a normal distribution,there are five peaks due to the monthly planting.The monthly rice-planted area was estimated based on supervised classification using the defined model during the rice-planting season.The overall classification accuracy was 91%,and the rice information including arable land is 90%.
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发布时间：2021-06-10 - 摘要：Leaf area index（LAI） is an important bio-physical parameter of paddy rice. The LAI will provide important information of the growth of paddy rice. In an effort to develop the quantitative relationships between field measured leaf area index and Vegetation-derived vegetation indices for paddy rice fields,we have measured the leaf area index of paddy rice at 10-day intervals at five sampling sites in Jiangning county, Jiangsu province during the rice growing season （July to October） of 1999, using a LI-COR LAI-2000 plant canopy analyser. After being transplanted in mid to late June, paddy rice LAI increased quickly and reached its plateau by early to mid August. Compared with the VGT sensor derived Normalized Difference Vegetation Index （NDVI）, we found that there were similar dynamics of LAI and Vegetation Index over the growing season of paddy rice in 1999.关键词：paddy rice;leaf area index;Vegetation sensor
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发布时间：2021-06-10 - 摘要：Application and analysis of the algorithm called CLAVR were made for the remote sensing of cloud cover using multispectral radiance measurements from the Advanced Very High Resolution Radiometer （AVHRR） on board National Oceanic and Atmospheric Administration （NOAA） polar-orbiting satellites. The algorithm classifies 2×2 pixel arrays from the HRPT 1.1-km-resolution data into CLEAR, MIXED, and CLOUDY categories. It uses a sequence of multispectral contrast, spectral, and spatial signature threshold tests to perform the classification. The algorithms on China land area were applied to realtime NOAA-14 data and about 10 years dataset of China land area have been processed. It did well in classifying perfectly clear and cloudy pixel arrays except existing some errors for mixed pixel arrays. Different images in China land area show some difference, and the complicacy of landform and underlying surface influences cloud’s appearance or structure, thereby resulting contrast for different steps of the algorithm. The thresholds of main steps （RRCT, TUT and RUT） have been modified in the algorithm that required in the application of decision tree tests. It retrieves some CLOUDY and CLEAR pixels from the original classified MIXED pixels, separately the 4.31% and 1.09%. The CLAVR has been integrated into the Crop Growing Monitoring system of China.关键词：CLAVR;cloud detection;NOAA AVHRR;China territory
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发布时间：2021-06-10 - 摘要：With a vast territory, complicated natural conditions, multiplicity of crop structure, small and dispersive distribution of parcel, the precision of crop distribution maps based on remote sensing imagery can’t satisfy the need of crop yield forecasting. For this study, the paper using QuickBird high spatial resolution satellite imagery created detailed crop pattern map in a test site of Taigu, Shanxi province, where the crop pattern is very complexity in autumn. First,the QuickBird image was divided into segments by using object-oriented image segmentation technique. Second, the main land cover was classified by using spectral, spatial and contextual information based on fuzzy logic. Finally the detailed crop distribution map with high accuracy was made by combining the classification result and the field investigation. In spite of the high spatial resolution of the QuickBird BIRD image, classes such as different crops are still fairly difficult to identify. So the field investigation is very important. The map provides a more accurate spatial pattern of crops,and is useful for crop yield forecasting.关键词：crop pattern map;QuickBird;object-oriented
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发布时间：2021-06-10
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