首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献，搜索用时 15 毫秒
1.
Abstract

Multi‐temporal ERS‐1 SAR data acquired over a large agricultural region in West Bengal was used to classify kharif crops like rice, jute and sugarcane. Rice crop grown under lowland management practice showed a temporal characteristic. The dynamic range of backscatter was highest for this crop in temporal SAR data. This was used to classify rice using temporal SAR data. Such temporal character was not observed for the other study crops, which may be due to the difference in cultivation practice and crop calendar. Significant increase in backscatter from the ploughed fields was used to derive information on onset and duration of land preparations. Synergistic use of optical remote sensing data and SAR data increased the separability of rice crop from homesteads and permanent vegetation classes.  相似文献   

2.
This paper introduces ENVISAT ASAR data application on rice field mapping in the Fuzhou area, using multi-temporal ASAR dual polarization data acquired in 2005. The procedure for ASAR data processing here includes data calibration, image registration, speckle reduction and conversion of data format from amplitude to dB for backscatter. The backscatter of rice increases with the rice growing stages, which was much different from other land covers. Based on image difference techniques, 6 schemes were designed with ASAR different temporal and polarization data for rice field mapping. Difference images between images in the early period of rice crop and growing or ripening period, are more suitable for rice extraction than those difference images between different polarizations in the same date. The most accurate result of late rice extraction was achieved based on the difference of HH polarization data acquired in October and August. Therefore, for rice field mapping, the temporal information is more important than polarization information. The data during the early growing season of rice is very important for high accuracy rice mapping.  相似文献   

3.
Microwave sensors having all-weather capabilities provide an opportunity to monitor rice grown in monsoon season. An attempt has been made to identify rice crop using multitemporal ERS-1 SAR data in C-band (5.3 GHz). Data acquired on August 15 (D1), September 19 (D2), October 24 (D3) and November 28 (D4) 1993 were taken. Combinations of data acquired on different dates were used for identification of rice crop. Single-date IRS-1B LISS II data in visible and NIR bands acquired on October 23, 1993 was also used for comparison of estimated rice area. Analysis of the results has shown that a combination of SAR data acquired at the tillering (August), booting (September) and heading (October) stages of rice crop enabled identification and area estimation of rice crop grown under lowland conditions. Single-date SAR data acquired in the month of October was found to be better for identification of rice compared to other dates.  相似文献   

4.
In this study we used the PhenoRice algorithm to track recent variations of rice cultivation practices along the Senegal River Valley. Time series of MODIS imagery with 250 m spatial resolution and a nominal 8-days frequency were used as input for the algorithm to map the spatial and temporal variations of rice cultivated area and of several important phenological metrics (e.g., crop establishment and harvesting dates, length of season) for the 2003–2016 period in both the dry and the wet rice cultivation seasons. Comparison between PhenoRice results and ancillary and field data available for the Senegal part of the study area showed that the algorithm is able to track the interannual variations of rice cultivated area, despite the total detected rice area being consistently underestimated. PhenoRice estimates of crop establishment and harvesting dates resulted accurate when compared with field observations available for two sub-regions for a period of 10 years, and thus allow assessing interannual variability and tracking changes in agronomic practices. An analysis of interannual trends of rice growing practices based on PhenoRice results highlighted a clear shift of rice cultivation from the wet to the dry season starting approximately from 2008. The shift was found to be particularly evident in the delta part of the SRV. Additionally, a statistically significant trend was revealed starting 2006 towards a longer dry season (r2 = 0.81; Slope = 1.24 days y−1) and a shorter wet season (r2 = 0.65; Slope = 0.53 days y−1). These findings are in agreement with expert knowledge of changes ongoing in the area. In particular the shorter wet season is attributed to shortage of labor and equipment leading to a delay in completion of harvesting operations in the dry season, which led to the adoption of short-duration rice varieties by farmers in the wet season to avoid risk of yield losses due to climatic constraints. Aforementioned results highlight the usefulness of the PhenoRice algorithm for providing insights about recent variations in rice cultivation practices over large areas in developing countries, where high-quality up to date information about changes in agricultural practices are often lacking.  相似文献   

5.
A national level project on kharif rice identification and acreage estimation is being carried out successfully for several states in the country. A similar methodology based on the temporal profile for identification and delineation of various land cover classes has been followed for the Rabi rice acreage estimation. To define rabi rice, rabi season in India starts from November — February to March — June. Though the main growing season is predominantly winter but the uncertainty of getting cloud free data during the season has resulted in the use of microwave data. A feasibility study was taken up for early forecasting of the rabi rice area using microwave data. Hierarchical decision rule classification technique was used for the identification of the different land cover classes. Land preparation, puddling and transplantation were the reasons for the specific backscatter of rice growing areas. The increase or decrease in the SAR backscatter due to progress in the crop phenology or due to delayed sowing respectively forms the basis for identifying the rice areas. In addition the potential of optical data of a later date has been utilized in the form of various indices from bands including MIR to distinctly separate the late sown areas and also the puddled areas from other areas. This study emphasizes the synergistic use of SAR and optical data for delineating the rabi rice areas which is of immense use in giving an early forecast.  相似文献   

6.
This study assesses the use of TerraSAR-X data for monitoring rice cultivation in the Sanjiang Plain in Heilongjiang Province, Northeast China. The main objective is the understanding of the coherent co-polarized X-band backscattering signature of rice at different phenological stages in order to retrieve growth status.For this, multi-temporal dual polarimetric TerraSAR-X High Resolution SpotLight data (HH/VV) as well as single polarized StripMap (VV) data were acquired over the test site. In conjunction with the satellite data acquisition, a ground truth field campaign was carried out.The backscattering coefficients at HH and VV of the observed fields were extracted on the different dates and analysed as a function of rice phenology to provide a physical interpretation for the co-polarized backscatter response in a temporal and spatial manner. Then, a correlation analysis was carried out between TerraSAR-X backscattering signal and rice biomass of stem, leaf and head to evaluate the relationship with different vertical layers within the rice vegetation.HH and VV signatures show two phases of backscatter increase, one at the beginning up to 46 days after transplanting and a second one from 80 days after transplanting onwards. The first increase is related to increasing double bounce reflection from the surface–stem interaction. Then, a decreasing trend of both polarizations can be observed due to signal attenuation by increasing leaf density. A second slight increase is observed during senescence. Correlation analysis showed a significant relationship with different vertical layers at different phenological stages which prove the physical interpretation of X-band backscatter of rice. The seasonal backscatter coefficient showed that X-band is highly sensitive to changes in size, orientation and density of the dominant elements in the upper canopy.  相似文献   

7.
水稻生长期微波介电特性研究   总被引：4，自引：0，他引：4  
利用植被介电常数的Debye-Cole双频色散模型，模拟计算了广东肇庆水稻试验区1996年晚稻和1997年早稻人插秧期、发蘖期、扬花期到成熟期各生长期的介电常数值，并根据计算结果，探讨了电磁波频率、水稻含水量、温度、含盐度及水稻冠层干体密度对介电常数的影响。其中，不同生长期水稻的介电常数各不相同，不同水稻类型（早稻和晚稻），介电常数的变化趋势不尽相同。电磁波频率、水稻含水量、温度和水稻冠层干体密度均对介电常数有不同程度的影响，而含盐度却对介电常数影响不大。  相似文献   

8.
The Canadian satellite RADARSAT launched in November 1995 acquires C-band HH polarisation Synthetic Aperture Radar (SAR) data in various incident angles and spatial resolutions. In this study, the Standard Beam S7 SAR data with 45°–49° incidence angle has been used to discriminate rice and potato crops grown in the Gangetic plains of West Bengal state. Four-date data acquired in the 24-day repeat cycle between January 2 and March 15, 1997 was used to study the temporal backscatter characteristics of these crops in relation to the growth stages. Two, three and four-date data were used to classify the crops. The results show that the backscatter was the lowest during puddling of rice fields and increased as the crop growth progressed. The backscatter during this period changed from −18 dB to −8 dB. This temporal behaviour was similar to that observed in case of ERS-SAR data. The classification accuracy of rice areas was 94% using four-date data. Two-date data, one corresponding to pre-field preparation and the other corresponding to transplantation stage, resulted in 92% accuracy. The last observation is of particular interest as one may estimate the crop area as early as within 20–30 days of transplantation. Such an early estimate is not feasible using optical remote sensing data or ERS-SAR data. The backscatter of potato crop varied from −9 dB to −6 dB during the growth phase and showed large variations during early vegetative stage. Two-date data, one acquired during 40–45 days of planting and another at maturing stage, resulted in 93% classification accuracy for potato. All other combinations of two-date data resulted in less than 90% classification accuracy for potato.  相似文献   

9.
In-season rice area estimation using C-band Synthetic Aperture Radar (SAR) data from RADARSAT-1 is being done in India for more than a decade. Decision rule based models in backscatter domain have been calibrated and validated using extensive field data and a long term backscatter signature bank of rice fields has been developed. Since the rice crop growing environment in India is a diverse one in the world having all the rice cultural types, the rice backscatter is quite exhaustive. This paper highlights the results of classification of rice lands in Bangladesh using the signature bank of India. The results showed that the Aman rice crop of Bangladesh has a typical temporal backscatter of shallow and intermediate rice fields of that of West Bengal state. The mean backscatter of the intermediate/deep water fields in southern Bangladesh was ?19?dB, while that of shallow cultural types mostly in northern Bangladesh was ?17?dB. The signature of the rice crop in Southern Bangladesh matched well with that of Gangetic West Bengal, particularly that of the 24 Parganas, Howrah and Hughli districts. The signature of rice crop in the Sub-Himalayan West Bengal particularly that of Dinajpur and Maldah districts matched well with that of the northern area of Bangladesh. State level rice area estimated using the selected models was found with in 5% deviation from that of the reported acreage.  相似文献   

10.
This study investigates the potential of multi-temporal signature analysis of satellite imagery to map rice area in South 24 Paraganas district of West Bengal. Two optical data (IRS ID LISS III) and three RADARSAT SAR data of different dates were acquired during 2001. Multi-temporal SAR backscatter signatures of different landcovers were incorporated into knowledge based decision rules and kharif landcover map was generated. Based on the spectral variation in signature, the optical data acquired during rabi (January) and summer (March) season were classified using supervised maximum likelihood classifier. A co-incidence matrix was generated using logical approach for a combined “rabi-summer” and “kharif-rabi-summer” landcover mapping. The major landcovers obtained in South 24 Paraganas using remote sensing data are rice, water, aquaculture ponds, homestead, mangrove, and urban area. The classification accuracy of rice area was 98.2% using SAR data. However, while generating combined “kharif-rabi-summer” landcovers, the classification accuracy of rice area was improved from 81.6% (optical data) to 96.6% (combined SAR-Optical). The primary aim of the study is to achieve better accuracy in classifying rice area using the synergy between the two kinds of remotely sensed data.  相似文献   

11.
This research letter presents preliminary results of mapping rice crop growth using ENVISAT advanced synthetic aperture radar (ASAR) alternating polarization HH/HV data. Four ASAR HH/HV images were collected in the early rice-growth cycle in the test site in 2006, and the temporal response of ASAR data to the rice field was analyzed. The height and biomass of rice were measured during acquisition of ASAR data, and empirical relationships were established between the backscattering coefficient and these two parameters. Based on the temporal variation of the radar response, a method for mapping a rice growth area was developed using the combination of ASAR HH and HV polarization data between two acquisition dates. The results confirm that C-band SAR data have great potential in the development of an operational system for monitoring rice crop growth in Southern China.  相似文献   

12.
多时相MODIS影像水田信息提取研究   总被引：5，自引：0，他引：5  
水稻种植及其分布信息是土地覆被变化、作物估产、甲烷排放、粮食安全和水资源管理分析的重要数据源。基于遥感的水田利用监测中,通常采用时序NDVI植被指数法和影像分类法分别进行AVHRR和TM影像的水田信息获取。针对8天合成MODIS陆地表面反射比数据的特点和水稻生长特征,选取水稻种植前的休耕期、秧苗移植期、秧苗生长期和成熟期等多时相MODIS地表反射率影像数据,通过归一化植被指数、增强植被指数及利用对土壤湿度和植被水分含量较敏感的短波红外波段计算得到的陆表水指数进行水田信息获取。将提取结果与基于ETM＋影像的国土资源调查水田数据,通过网格化计算处理并进行对比分析,结果表明,利用MODIS影像的8天合成地表反射率数据,进行区域甚至全国的水田利用监测是可行的。  相似文献   

13.
The rice land is linked to the climate change due to its methane emission potential. The systems of growing rice and associated soil and crop management practices that have evolved are varied and complex. However, from the methane emission point of view, water regime is a crucial parameter. According to IPCC guidelines the rice ecosystem need to be categorized into four strata for methane emission study. The remote sensing based stratification map previously developed was used for in-situ weekly/monthly measurements of methane emission from the representative ecosystems, samples were collected and analysed using gas chromatography following the IPCC standards for three consecutive years; 2003, 2004 and 2005. This paper highlights the results of methane emission measurement and pattern from rice lands of India based on in-situ measurements. The CH4 emission pattern of irrigated crop in dry season showed a steady increase in the beginning which peaks during flowering stage, decreasing gradually thereafter. The results were consistent for different varieties and across the years. The emission pattern of irrigated wet season crop showed two peaks. The emission pattern also showed the influence of crop variety as well as year (of observation). The mean emission coefficient derived from all categories and all samples (n = 471) weighted for the Indian rice crop was 74.05 + 43.28 kg/ha.  相似文献   

14.
Rice is the most consumed staple food in the world and a key crop for food security. Much of the world’s rice is produced and consumed in Asia where cropping intensity is often greater than 100% (more than one crop per year), yet this intensity is not sufficiently represented in many land use products. Agricultural practices and investments vary by season due to the different challenges faced, such as drought, salinity, or flooding, and the different requirements such as varietal choice, water source, inputs, and crop establishment methods. Thus, spatial and temporal information on the seasonal extent of rice is an important input to decision making related to increased agricultural productivity and the sustainable use of limited natural resources. The goal of this study was to demonstrate that hyper temporal moderate-resolution imaging spectroradiometer (MODIS) data can be used to map the spatial distribution of the seasonal rice crop extent and area. The study was conducted in Bangladesh where rice can be cropped once, twice, or three times a year.MODIS normalized difference vegetation index (NDVI) maximum value composite (MVC) data at 500 m resolution along with seasonal field-plot information from year 2010 were used to map rice crop extent and area for three seasons, boro (December/January–April), aus (April/May–June/July), and aman (July/August–November/December), in Bangladesh. A subset of the field-plot information was used to assess the pixel-level accuracy of the MODIS-derived rice area. Seasonal district-level rice area statistics were used to assess the accuracy of the rice area estimates. When compared to field-plot data, the maps of rice versus non-rice exceeded 90% accuracy in all three seasons and the accuracy of the five rice classes varied from 78% to 90% across the three seasons. On average, the MODIS-derived rice area estimates were 6% higher than the sub-national statistics during boro, 7% higher during aus, and 3% higher during the aman season. The MODIS-derived sub-national areas explained (R2 values) 96%, 93%, and 96% of the variability at the district level for boro, aus, and aman seasons, respectively.The results demonstrated that the methods we applied for analysing and interpreting moderate spatial and high temporal resolution imagery can accurately capture the seasonal variability in rice crop extent and area. We discuss the robustness of the approach and highlight issues that must be addressed before similar methods are used across other areas of Asia where a mix of rainfed, irrigated, or supplemental irrigation permits single, double, and triple cropping in a single calendar year.  相似文献   

15.
Planting a cover crop between the main cropping seasons is an agricultural management measure with multiple potential benefits for sustainable food production. In the maize production system of the Netherlands, an effective establishment of a winter cover crop is important for reducing nitrogen leaching to groundwater. Cover crop establishment after maize cultivation is obliged by law for sandy soils and consequently implemented on nearly all maize fields, but the winter-time vegetative ground cover varies significantly between fields. The objectives of this study are to assess the variability in winter vegetative cover and evaluate to what extent this variability can be explained by the timing of cover crop establishment and weather conditions in two growing seasons (2017–2018). We used Sentinel-2 satellite imagery to construct NDVI time series for fields known to be cultivated with maize within the province of Overijssel. We fitted piecewise logistic functions to the time series in order to estimate cover crop sowing date and retrieve the fitted NDVI value for 1 December (NDVIDec). We used NDVIDec to represent the quality of cover crop establishment at the start of the winter season. The Sentinel-2 estimated sowing dates compared reasonably with ground reference data for eight fields (RMSE = 6.6 days). The two analysed years differed considerably, with 2018 being much drier and warmer during summer. This drought resulted in an earlier estimated cover crop sowing date (on average 19 days) and an NDVIDec value that was 0.2 higher than in 2017. Combining both years and all fields, we found that Sentinel-2 retrieved sowing dates could explain 55% of the NDVIDec variability. This corresponded to a positive relationship (R2 = 0.50) between NDVIDec and the cumulative growing degree days (GDD) between sowing date and 1 December until reaching 400 GDD. Based on cumulative GDD derived from two weather stations within Overijssel, we found that on average for the past three decades a sowing date of 19 September (± 7 days) allowed to attain these 400 GDD; this provides support for the current legislation that states that from 2019 onwards a cover crop should be sown before 1 October. To meet this deadline, while simultaneously ascertaining a harvest-ready main crop, in practice implies that undersowing of the cover crop during spring will gain importance. Our results show that Sentinel-2 NDVI time series can assess the effectiveness and timing of cover crop growth for small agricultural fields, and as such has potential to inform regulatory frameworks as well as farmers with actionable information that may help to reduce nitrogen leaching.  相似文献   

16.
全球农情遥感速报系统20年   总被引：2，自引：0，他引：2  
吴炳方  张淼  曾红伟  闫娜娜  张鑫  邢强  常胜 《遥感学报》2019,23(6):1053-1063
面向国家粮食安全的重大战略需求,1998年中国科学院建立了"中国农情遥感速报系统"(CropWatch),持续运行20年后,现已发展成为"全球农情遥感速报系统"(CropWatch)。本文重点论述了2013年建立参与式全球农情遥感监测云平台(CropWatch-Cloud)以来,所采用的农情监测体系、可定制的农情监测云平台理念以及CropWatch-Cloud在国内外的应用推广情况,介绍了技术方法与农情信息服务方式的创新与进步带来的国际影响力的提升。系统总结了全球农情遥感速报系统发展的农情监测指标、农情预警能力、作物长势综合监测方法以及众源数据支持的作物面积监测方法,论文进一步阐述了CropWatch未来的发展方向,借助众源地理信息、大数据技术等的发展,打通从地块—村—镇—县—市—省—国家—全球的体系化全链条监测,满足从农户到政府决策部门对农情信息的差异化需求。  相似文献   

17.
The accurate and timely estimates of crop physiological growth stages are essential for efficient crop management and precise modeling of agricultural systems. Satellite remote sensing has been widely used to retrieve vegetation phenology metrics at local to global scales. However, most of these phenology metrics (e.g., green-up) are different from crop growth stages (e.g., emergence) used in crop management and modeling. As such, an integrated framework referred to as PhenoCrop was developed to: 1) establish a connection between remote sensing-derived phenology metrics and key crop growth stages based on Wang and Engle plant phenology model and 2) use fused MODIS-Landsat 30 m 8-day reflectance data generated using Kalman Filter-based data fusion technique to produce onset dates of key growth stages of corn (Zea mays L.) and soybeans (Glycine max L.) at 30 m spatial resolution. In this paper, we described the PhenoCrop framework, and tested its performance for the State of Nebraska for 2012–2016 by comparison to observations of estimated key growth stages at four experimental sites, and state-level statistical data from Crop Progress Reports (CPRs) published by the United States Department of Agriculture’s (USDA) National Agricultural Statistical Services (NASS). In addition, to evaluate the suitability of using coarse or high spatial resolution satellite imagery, fused MODIS-Landsat-based estimates were compared with those produced using EOS MODIS 250 m (MOD9Q1) reflectance data.The PhenoCrop estimates captured the typical spatial trends of gradual delay in the progression of the growing season from southeast to northwest Nebraska. Also inter-annual differences due to factors such as weather fluctuations and change in management strategies (e.g., early season in 2012) were evident in the estimates. Validation results revealed that average root mean square error (RMSE) of the state-level estimates of corn and soybean growth stages ranged from 1.10 to 4.20 days and from 3.81 to 7.89 days, respectively, while pixel level estimates had a RMSE ranging from 3.72 to 8.51 days for corn and 4.76–9.51 days for soybean growth stages. Although MODIS 250 m based estimates showed similar general spatial patterns observed in the fused MODIS-Landsat based estimates, the accuracy and ability to capture field scale variations was improved with fused MODIS-Landsat data. Overall, results showed the ability of PhenoCrop framework to provide reliable estimates of crop growth stages that can be highly useful in crop modeling and crop management during the growing season.  相似文献   

18.
Some of the basic requirements for cropping system analysis are updated information on crops grown, their phenological behaviour, method and duration of establishment and harvest, inter and intra crop variability, sequential cropping patterns. The next generation Indian Remote Sensing Satellite with high repeat cycle opens new possibility of crop surveys to derive such information. In this study, an attempt has been made to analyse cropping system at district level using simulated IRS-1C Wide Field Sensor (WiFS) data. Data acquired for nineteen dates during 1992–93 season for Bardhaman district, West Bengal has been used. It was feasible to derive accurate information on cropping pattern, crop rotation, crop duration, progress of harvest, crop growth profiles and annual crop acreage using multidate data. It was observed that even a seven to eight day interval of data acquisition during critical growth periods significantly affected classification and identification accuracy.  相似文献   

19.
邵芸  郭华东  范湘涛  刘浩 《遥感学报》2001,5(4):340-345
通过对肇庆试验区1996年和1997年获取的多时相、多模式雷达卫星（RADARSAT）数据分析,从图像上直接提取地物的后向散射系数,结合实地测量水稻的生长结构参数,建立了水稻生长模型,分析了不同生长周期（从80天到120－125天）4种类型水稻的时域散射特性。利用1997年4月至7月获取的7景标准模式雷达卫星数据,对试验区内三个县和两个行政区共5000km^2面积范围内的作物进行分类和水稻产量预估算,水稻类型分类及面积量算精度达91％。结果表明：利用雷达遥感数据进行水稻种植面积量算和估产需要水稻生长期间三个时相的数据,即插秧期、抽穗期、收割前期。若能够获得多参数雷达图像,可以用插秧期和收割前期的两个时相图像来代替上述的三个时相图像同样可以达到种植面积量算和估产的效果。这一结果充分说明多时相雷达卫星数据对我国南方水稻长势监测及估产具有明显优势和潜力。  相似文献   

20.
水稻时域散射特征分析及其应用研究   总被引：14，自引：3，他引：14  
邵芸  郭华东  范湘涛  刘浩 《遥感学报》2001,5(5):340-345
通过对肇庆试验区1996年和1997年获取的多时相、多模式雷达卫星（RADARSAT）数据分析，从图像上直接提取地物的后向散射系数，结合实地测量水稻的生长结构参数，建立了水稻生长模型，分析了不同生长周期（从80天到120－125天）4种类型水稻的时域散射特性。利用1997年4月至7月获取的7景标准模式雷达卫星数据，对试验区内三个县和两个行政区共5000km^2面积范围内的作物进行分类和水稻产量预估算，水稻类型分类及面积量算精度达91％。结果表明：利用雷达遥感数据进行水稻种植面积量算和估产需要水稻生长期间三个时相的数据，即插秧期、抽穗期、收割前期。若能够获得多参数雷达图像，可以用插秧期和收割前期的两个时相图像来代替上述的三个时相图像同样可以达到种植面积量算和估产的效果。这一结果充分说明多时相雷达卫星数据对我国南方水稻长势监测及估产具有明显优势和潜力。  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号