秸秆焚烧导致湖北中东部一次严重霾天气过程的分析   总被引：1，自引：0，他引：1  

周悦  岳岩裕  李兰  刘敏  周婷 《气候与环境研究》2016,21(2):141-152

利用地面气象要素、火点信息及污染物资料,研究了2014年6月12~13日湖北省中东部地区一次重度霾天气的成因及污染特征。结果表明:导致此次霾天气的主要原因是安徽省北部大面积秸秆焚烧所形成污染气团受偏东北气流输送的影响,12日在湖北中东部形成了两条"带状"的能见度低值区,最低能见度仅为2.1 km。秸秆焚烧污染物输送气流由北向南影响湖北,主要作用于孝感—武汉—咸宁一带,3个地区细颗粒物(PM2.5)峰值浓度均超过了600μg/m3,且武汉和孝感的PM2.5与PM10质量浓度比值在12日增加到0.76和0.77,并出现了0.96和0.93的最大值,随着污染气团的传输,其中PM2.5所占比例会出现明显下降。SO2质量浓度的变化特征不显著,NO2质量浓度在污染物质量浓度达到峰值前1~3 h达到峰值,而CO是秸秆焚烧产生的主要污染气体,其质量浓度变化与PM2.5和PM10呈正相关关系,相关系数分别为0.66和0.67。风矢量和分析表明:6月12日湖北省中东部存在明显的东北来向气流输送,污染物的输送是该时段霾天气发生的主要影响因子,而6月13日湖北省东北边界处的输送气流已经明显减弱消失,东南部风矢量和异常偏小导致的污染物堆积是该地区污染持续的主要原因。  相似文献   

大面积小麦遥感估产模型的构建与调试方法的研究     

王乃斌  周迎春  林耀明  邓坤枚 《遥感学报》1993,(4)

本文重点研究大面积冬小麦遥感估产模型构建及其调试方法。通过分析冬小麦生长发育过程,对光、温、水、肥等必须条件需求规律研究的基础上,提出了以绿度指数、温度和绿度变化速率等因子,构建大面积冬小麦遥感估产模型。为了适应大面积遥感估产运行系统的需要,在变量获取及模型调试等方面进行了一些新的探索。  相似文献   

气候变暖对中国水稻生产可能影响的研究   总被引：17，自引：0，他引：17  

张宇  王馥棠 《气象学报》1998,56(3):369-376

利用随机天气模型,将大气环流模式预测的气候情景与水稻模式相链接,研究了气候变暖对中国水稻生产的可能影响。结果表明,大气中ＣＯ２浓度加倍,中国水稻主产区适宜水稻生长的日数将延长６～１１ｄ,积温增加２２０～３３０℃·ｄ。积温的相对增长率由南向北呈增长趋势。水稻产量形成期低温天气出现频率将减少,而高温天气出现的频率增加。若品种与播种、移栽期不变,水稻产量将下降;而若通过改变品种使作物生育期基本保持目前的状况,减产幅度将比品种不变时明显偏小,部分地区还有可能增产。  相似文献   

种植业可持续发展的支持系统——农作物卫星遥感估产   总被引：14，自引：0，他引：14  

陈沈斌 《地理科学进展》1998,17(2):73-79

本文从种植业可持续发展需要出发,指出建立农作物卫星遥感估产系统是实现种植业可持续发展地面信息准确、快速、经济获取的最有效手段,并能成为种植业可持续发展的支持系统。本文还分析了我国种植业地面信息统计现状和卫星遥感估产国内外研究进展,一个实际运行的农作物卫星遥感估产系统可以监测作物长势、种植面积和作物产量,与其它信息相配合有利于种植业可持续发展战略的实施。  相似文献   

全球农作物对大气CO₂及其倍增的吸收量估算   总被引：15，自引：0，他引：15  

王修兰 《气象学报》1996,54(4):466-473

根据农作物产量资料（ＦＡＯ１９９２年），计算出中国和全球各种作物对ＣＯ２的吸收总量分别为５．５×１０８ｔ／ａＣ和２８．９×１０８ｔ／ａＣ。同时以不同ＣＯ２浓度下小麦、玉米、大豆等全生育期光合速率实验数据直接计算的Ｃ吸收量为对照，与相应的中国产量资料计算结果比较，两者相差２．６％。从而进一步依据作物对ＣＯ２倍增反应诊断实验结果，推算出大气ＣＯ２浓度比目前倍增（７００ｐｐｍ）条件下，中国和全球农作物吸收ＣＯ２总量将增长２１％－２６％，分别为６．６×１０８ｔ／ａ—６．９×１０８ｔ／ａ和３４．１×１０８ｔ／ａ—３６．２×１０８ｔ／ａＣ。研究还表明，单位面积作物年吸Ｃ量全球（３．２ｔ／（ｈｍ２·８））比中国（４．２ｔ／（ｈｍ２·ａ））低２５．４％，而且Ｃ４作物普遍高于同类Ｃ３作物。  相似文献   

农业气候年景预测和决策服务系统的研究   总被引：1，自引：2，他引：1       下载免费PDF全文

魏丽  陆叔鸣 《应用气象学报》1997,8(2):224-229

农业气候年景预测和决策服务系统（ACHPDS）由三部分组成。介绍了农业气象灾害预测（AMDP）、农作物产量年景预测（COHP）及农作物种植面积合理配置决策系统（CAAD）的研究方法和应用情况。  相似文献   

Disaggregating and mapping crop statistics using hypertemporal remote sensing     

M.R. Khan  C.A.J.M. de Bie  H. van Keulen  E.M.A. Smaling  R. Real 《International Journal of Applied Earth Observation and Geoinformation》2010

Governments compile their agricultural statistics in tabular form by administrative area, which gives no clue to the exact locations where specific crops are actually grown. Such data are poorly suited for early warning and assessment of crop production. 10-Daily satellite image time series of Andalucia, Spain, acquired since 1998 by the SPOT Vegetation Instrument in combination with reported crop area statistics were used to produce the required crop maps. Firstly, the 10-daily (1998–2006) 1-km resolution SPOT-Vegetation NDVI-images were used to stratify the study area in 45 map units through an iterative unsupervised classification process. Each unit represents an NDVI-profile showing changes in vegetation greenness over time which is assumed to relate to the types of land cover and land use present. Secondly, the areas of NDVI-units and the reported cropped areas by municipality were used to disaggregate the crop statistics. Adjusted R-squares were 98.8% for rainfed wheat, 97.5% for rainfed sunflower, and 76.5% for barley. Relating statistical data on areas cropped by municipality with the NDVI-based unit map showed that the selected crops were significantly related to specific NDVI-based map units. Other NDVI-profiles did not relate to the studied crops and represented other types of land use or land cover. The results were validated by using primary field data. These data were collected by the Spanish government from 2001 to 2005 through grid sampling within agricultural areas; each grid (block) contains three 700 m × 700 m segments. The validation showed 68%, 31% and 23% variability explained (adjusted R-squares) between the three produced maps and the thousands of segment data. Mainly variability within the delineated NDVI-units caused relatively low values; the units are internally heterogeneous. Variability between units is properly captured. The maps must accordingly be considered “small scale maps”. These maps can be used to monitor crop performance of specific cropped areas because of using hypertemporal images. Early warning thus becomes more location and crop specific because of using hypertemporal remote sensing.  相似文献   

PhenoCrop: An integrated satellite-based framework to estimate physiological growth stages of corn and soybeans     

《International Journal of Applied Earth Observation and Geoinformation》2020

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   

Crop characterization using an improved scattering power decomposition technique for compact polarimetric SAR data     

《International Journal of Applied Earth Observation and Geoinformation》2020

Crop characterization using Compact-Pol Synthetic Aperture Radar (CP-SAR) data is of prime interest with the rapid advancements of SAR systems towards operational applications. It is noteworthy that as a good compromise between the dual and quad-polarized SAR systems, the CP-SAR offer advantages in terms of the larger swath and lower data rate. The m − χ CP decomposition considers two out of the three Stokes child parameters: degree of polarization (m), ellipticity (χ), and orientation angle (ψ) to describe the polarized part of the quasi-monochromatic partially polarized wave. An improvement in the scattering powers was proposed in the S − Ω decomposition, which takes into accounts both the transmitted and received wave ellipticities (χ_t, χ_r) and the orientation angles (ψ_t, ψ_r). In this decomposition, S denotes the Stokes vector and Ω is the polarized power fraction. However, it may be noted that the S − Ω decomposition intrinsically ignores dominance in the target scattering mechanism while calculating the powers. In this work, improvement is proposed for the S − Ω decomposition by utilizing the degree of dominance in the scattering mechanism. The improved S − Ω (named as iS − Ω) decomposition powers are first compared with the existing m − χ and S − Ω powers for elementary (viz., trihedral and dihedral corner reflectors) and distributed targets using simulated CP-SAR data from quad-pol RADARSAT-2 data. An increase of ∼2% for odd and even-bounce powers obtained from the iS − Ω decomposition is observed for the trihedral and dihedral corner reflectors respectively. The analysis of the scattering powers for distributed targets shows that an increase of 15% and 12% in the even and odd-bounce powers is observed from iS − Ω for urban and bare soil areas respectively as compared to the m − χ and S − Ω decompositions. Besides, temporal variations in the scattering powers obtained from the iS − Ω decomposition are also analyzed for rice, cotton, and sugarcane crops at different growth stages.  相似文献   

Crop classification from full-year fully-polarimetric L-band UAVSAR time-series using the Random Forest algorithm     

《International Journal of Applied Earth Observation and Geoinformation》2020

Accurate and timely information on the distribution of crop types is vital to agricultural management, ecosystem services valuation and food security assessment. Synthetic Aperture Radar (SAR) systems have become increasingly popular in the field of crop monitoring and classification. However, the potential of time-series polarimetric SAR data has not been explored extensively, with several open scientific questions (e.g. the optimal combination of image dates for crop classification) that need to be answered. In this research, the usefulness of full year (both 2011 and 2014) L-band fully-polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data in crop classification was fully investigated over an agricultural region with a heterogeneous distribution of crop categories. In total, 11 crop classes including tree crops (almond and walnut), forage crops (grass, alfalfa, hay, and clover), a spring crop (winter wheat), and summer crops (corn, sunflower, tomato, and pepper), were discriminated using the Random Forest (RF) algorithm. The SAR input variables included raw linear polarization channels as well as polarimetric parameters derived from Cloude-Pottier (CP) and Freeman-Durden (FD) decompositions. Results showed clearly that the polarimetric parameters yielded much higher classification accuracies than linear polarizations. The combined use of all variables (linear polarizations and polarimetric parameters) produced the maximum overall accuracy of 90.50 % and 84.93 % for 2011 and 2014, respectively, with a significant increase of approximately 8 percentage points compared with linear polarizations alone. The variable importance provided by the RF illustrated that the polarimetric parameters had a far greater influence than linear polarizations, with the CP parameters being much more important than the FD parameters. The most important acquisitions were the images dated during the peak biomass stage (July and August) when the differences in structural characteristics between most crops were the largest. At the same time, the images in spring (April and May) and autumn (October) also contributed to the crop classification since they respectively provided unique information for discriminating fruit crops (almond and walnut) as well as summer crops (corn, sunflower, and tomato). As a result, the combined use of only four acquisitions (dated May, July, August, and October for 2011 and April, June, August, and October for 2014) was adequate to achieve a nearly-optimal overall accuracy. In light of the promising classification accuracies demonstrated in this research, it becomes increasingly viable to provide accurate and up-to-date crops inventories over large areas based solely on multitemporal polarimetric SAR.  相似文献