不同养分供应状况对水稻荧光光谱特征的影响及其在遥感应用上的意义     

李德成  徐彬彬  石晓日  周斌  沈玉其  朱永豪  张崇静  吴荣  刘南英 《遥感学报》1992,(Z1)

在不同的养分供应状况下,对水稻在几个生育期的荧光光谱特征的研究表明:氮素供应的减少会引起水稻叶片荧光光谱中蓝绿波段峰的强度在有效分蘖期时降低,无效分蘖期始升高,并使红波段峰的强度和特征峰之间的强度比值(如440nm/550nm)在各生育期均有所降低;利用水稻叶片荧光光谱特征的变化监测其养分供应状况是可能的;监测波段以400—800nm为宜,监测时期应为分蘖盛期一孕穗期。  相似文献   

中国CH_4排放量的估算   总被引：32，自引：0，他引：32  

王明星  戴爱国  黄俊 《大气科学》1993,17(1):52-64

1987—1989年用自动连续采样分析设备对杭州地区的稻田CH_4排放进行了三年连续观测,1988—1990年用定期采样、分析方法对四川乐山地区的稻田CH_4排放进行了三年观测.发现稻田CH_4排放率有很大的日变化、季节变化和年际变化,这些变化主要是由土壤特性、水稻生长状况以及气象条件的变化造成的.不同地区稻田CH_4排放率差别很大,变化规律也不同,这种差别主要是由土壤特性,水稻品种及气候条件的差别造成的.根据现有观测资料,估计中国稻田CH_4年排放总量为17×10~(12)g.对沼气池的CH_4泄漏进行了三年系统观测研究,发现沼气池泄漏变化范围很大,但泄漏量总起来都很小,中国1000万个沼气池不构成大气CH_4的重要源.根据沼气池的泄漏估计了农村堆肥的CH_4排放量为3.2×10~(12)g/a;根据城市、稻田附近和沙漠地区大气CH_4浓度的测量结果推算了城市CH_4排放量;根据反刍动物消耗食物总量估计了反刍家畜的CH_4排放量;根据瓦斯排放资料估计了煤矿CH_4排放量;根据文献资料估计了中国天然湿地的CH_4排放量.最后估计了中国各种源的CH_4排放总量及未来变化趋势.1988年中国CH_4排放总量为40×10~(12)g,其中一半以上来自稻田.2000年中国CH_4排放总量可达45×10~(12)g,主要是反刍动物和煤矿排放量增加.  相似文献   

Influence of Different Seedling‐Nursing Methods on Methane and Nitrous Oxide Emissions in the Double Rice Cropping System of South China          下载免费PDF全文

Ying L. Liu  Mario E. Dokohely  Chang H. Fan  Qiao L. Li  Xiao X. Zhang  Hia Y. Zhao  Zheng Q. Xiong 《洁净——土壤、空气、水》2016,44(12):1733-1738

  相似文献   

On the performance of surface renewal analysis to estimate sensible heat flux over two growing rice fields under the influence of regional advection   总被引：1，自引：0，他引：1  

F. Castellví  R.L. Snyder   《Journal of Hydrology》2009,375(3-4):546-553

High-frequency temperature data were recorded at one height and they were used in Surface Renewal (SR) analysis to estimate sensible heat flux during the full growing season of two rice fields located north–northeast of Colusa, CA (in the Sacramento Valley). One of the fields was seeded into a flooded paddy and the other was drill seeded before flooding. To minimize fetch requirements, the measurement height was selected to be close to the maximum expected canopy height. The roughness sub-layer depth was estimated to discriminate if the temperature data came from the inertial or roughness sub-layer. The equation to estimate the roughness sub-layer depth was derived by combining simple mixing-length theory, mixing-layer analogy, equations to account for stable atmospheric surface layer conditions, and semi-empirical canopy–architecture relationships. The potential for SR analysis as a method that operates in the full surface boundary layer was tested using data collected over growing vegetation at a site influenced by regional advection of sensible heat flux. The inputs used to estimate the sensible heat fluxes included air temperature sampled at 10 Hz, the mean and variance of the horizontal wind speed, the canopy height, and the plant area index for a given intermediate height of the canopy. Regardless of the stability conditions and measurement height above the canopy, sensible heat flux estimates using SR analysis gave results that were similar to those measured with the eddy covariance method. Under unstable cases, it was shown that the performance was sensitive to estimation of the roughness sub-layer depth. However, an expression was provided to select the crucial scale required for its estimation.  相似文献   

贵州省桐梓县表层土壤硒地球化学特征及影响因素分析          下载免费PDF全文

张钟华  季国松  王小洪  安泉  刘浩  潘有良  吴鹏  肖波  王艇  罗建均  王华斌 《贵州地质》2020,37(3):289-297

针对复杂背景桥梁裂缝图像难以提取真实裂缝细节的技术问题,提出基于海森矩阵旋转矢量不变性的滤波去噪和局部区域裂缝走势生长方向连接的联合提取裂缝算法。利用海森矩阵增强图像突显裂缝区域,利用自适应阈值分割算法对图像进行二值分割;采用基于旋转矢量不变性的滤波算法滤掉团状噪声,根据裂缝的走势对生长方向一致的局部区域裂缝进行连接,排除条状非裂缝的影响和部分噪声的干扰;解决了图像处理中存在大量污渍残留和裂缝不连续现象。实验结果表明,本算法对复杂背景桥梁图像提取裂缝的准确度高于其它算法。  相似文献   

Mercury speciation,bioavailability and risk assessment on soil–rice systems from a watershed impacted by abandoned Hg mine-waste tailings     

Jian Pang  Jialiang Han  Xuelu Fan  Chan Li  Xian Dong  Longchao Liang  Zhuo Chen 《Acta Geochimica》2019,(3):391-403

Mercury(Hg) is a global pollutant and can be accumulated in the food chain, posing exposure risks to humans. In this study, rice plants and corresponding rhizosphere soil samples were collected from a watershed of the Wawu River Basin that is heavily impacted by historic Hg mining and retorting activities. Total mercury(THg)and methylmercury(MeHg) in rice grains, as well as other tissues and soil samples, were measured. Five soil Hg fractions, as well as soil parameters, were also determined.The results show that the average concentrations of THg and MeHg in rice grains were 14 ± 7.0 lg kg^-1 and7.2 ± 4.0 lg kg^-1. Soil organic-bound(Hg-o) and strong complex-bound(Hg-s) were the main Hg fractions,accounting for 44% of the total. To estimate the Hg–ligand interaction in the soils, soil-N/Hg(R = 0.451, p \ 0.05),-S/Hg(R = 0.372, p \ 0.1), and-OM/Hg ratio(R = 0.320,p \ 0.5) with MeHg_soil were observed with significant positive correlations, indicating that the formation of Hg–OM, Hg–N–OM or Hg–S–OM complexes could prevent Hg(II) from methylation in soils. The significant positive correlations of the-N/Hg ratio,-S/Hg ratio and-OM/Hg ratio with MeHg in rice tissues suggested that Hg methylation and MeHg demethylation occurred throughout the rice paddy ecosystem. The estimated MeHg daily intake(EDI) was 0.075 ± 0.041 lg kg^-1 bw d^-1 and was lower than the RfD level of 0.1 lg kg^-1 bw d^-1 recommended by the US EPA. However, approximately 29% of the hazardous index(HI) of MeHg in grain exceeded 1, posing a potential threat to local populations, particularly pregnant women and children.  相似文献   

Estimating aboveground and organ biomass of plant canopies across the entire season of rice growth with terrestrial laser scanning     

《International Journal of Applied Earth Observation and Geoinformation》2020

Non-destructive and accurate estimation of crop biomass is crucial for the quantitative diagnosis of growth status and timely prediction of grain yield. As an active remote sensing technique, terrestrial laser scanning (TLS) has become increasingly available in crop monitoring for its advantages in recording structural properties. Some researchers have attempted to use TLS data in the estimation of crop aboveground biomass, but only for part of the growing season. Previous studies rarely investigated the estimation of biomass for individual organs, such as the panicles in rice canopies, which led to the poor understanding of TLS technology in monitoring biomass partitioning among organs. The objective of this study was to investigate the potential of TLS in estimating the biomass for individual organs and aboveground biomass of rice and to examine the feasibility of developing universal models for the entire growing season. The field plots experiments were conducted in 2017 and 2018 and involved different nitrogen (N) rates, planting techniques and rice varieties. Three regression approaches, stepwise multiple linear regression (SMLR), random forest regression (RF) and linear mixed-effects (LME) modeling, were evaluated in estimating biomass with extensive TLS and biomass data collected at multiple phenological stages of rice growth across the entire season. The models were calibrated with the 2017 dataset and validated independently with the 2018 dataset.The results demonstrated that growth stage in LME modeling was selected as the most significant random effect on rice growth among the three candidates, which were rice variety, growth stage and planting technique. The LME models grouped by growth stage exhibited higher validation accuracies for all biomass variables over the entire season to varying degrees than SMLR models and RF models. The most pronounced improvement with a LME model was obtained for panicle biomass, with an increase of 0.74 in R² (LME: R² = 0.90, SMLR: R² = 0.16) and a decrease of 1.15 t/ha in RMSE (LME: RMSE =0.79 t/ha, SMLR: RMSE =2.94 t/ha). Compared to SMLR and RF, LME modeling yielded similar estimation accuracies of aboveground biomass for pre-heading stages, but significantly higher accuracies for post-heading stages (LME: R² = 0.63, RMSE =2.27 t/ha; SMLR: R² = 0.42, RMSE =2.42 t/ha; RF: R² = 0.57, RMSE =2.80 t/ha). These findings implied that SMLR was only suitable for the estimation of biomass at pre-heading stages and LME modeling performed remarkably well across all growth stages, especially for post-heading. The results suggest coupling TLS with LME modeling is a promising approach to monitoring rice biomass at post-heading stages at high accuracy and to overcoming the saturation of canopy reflectance signals encountered in optical remote sensing. It also has great potential in the monitoring of other crops in cloud-cover conditions and the instantaneous prediction of grain yield any time before harvest.  相似文献   

Mapping Rice Paddies in Complex Landscapes with Convolutional Neural Networks and Phenological Metrics     

Shuang Zhao  Chao Ding  Shuyuan Liu  Changshan Wu  Ling Wu 《地理信息系统科学与遥感》2020,57(1):37-48

ABSTRACT

Rice mapping with remote sensing imagery provides an alternative means for estimating crop-yield and performing land management due to the large geographical coverage and low cost of remotely sensed data. Rice mapping in Southern China, however, is very difficult as rice paddies are patchy and fragmented, reflecting the undulating and varied topography. In addition, abandoned lands widely exist in Southern China due to rapid urbanization. Abandoned lands are easily confused with paddy fields, thereby degrading the classification accuracy of rice paddies in such complex landscape regions. To address this problem, the present study proposes an innovative method for rice mapping through combining a convolutional neural network (CNN) model and a decision tree (DT) method with phenological metrics. First, a pre-trained LeNet-5 Model using the UC Merced Dataset was developed to classify the cropland class from other land cover types, i.e. built-up, rivers, forests. Then, paddy rice field was separated from abandoned land in the cropland class using a DT model with phenological metrics derived from the time-series data of the normalized difference vegetation index (NDVI). The accuracy of the proposed classification methods was compared with three other classification techniques, namely, back propagation neural network (BPNN), original CNN, pre-trained CNN applied to HJ-1 A/B charge-coupled device (CCD) images of Zhuzhou City, Hunan Province, China. Results suggest that the proposed method achieved an overall accuracy of 93.56%, much higher than those of other methods. This indicates that the proposed method can efficiently accommodate the challenges of rice mapping in regions with complex landscapes.  相似文献   

2005年江西晚稻飞虱大发生气象成因初步分析   总被引：7，自引：0，他引：7  

杜筱玲  郭瑞鸽  魏丽 《气象与减灾研究》2006,29(2):48-52

介绍了江西省稻飞虱历年发生概况,以及2005年江西稻飞虱发生特点,并从2005年副高脊线位置与飞虱早迁、汛期降雨峰值与飞虱多迁、台风环流与飞虱秋季回迁、夏秋季温湿条件与飞虱生长繁殖的关系等方面,进行了稻飞虱大发生的气象成因分析,力图为今后建立稻飞虱发生发展气象预测模型,开展灾害监测与预报提供依据.分析结果表明,2005年江西省晚稻飞虱具有发生时间早、面积广、为害重等特点,为历史罕见大爆发;稻飞虱主要来源于外地迁入,有春季迁入及秋季回迁2种;副高脊线位置偏南,西南气流强盛,导致春季气候适宜,暖湿气流活跃,强降水多,有利春季稻飞虱早迁、多迁、大量迁,早稻发生后的残留虫量是晚稻飞虱大发生的虫源基础;7～10月台风频频影响江西,特别是后期台风的影响,加速了秋季稻飞虱随东北气流的回迁,为晚稻飞虱大爆发起到促进作用;盛夏不热且秋季不凉的气象条件,有利于当地稻飞虱的生长繁殖,导致飞虱代数多.  相似文献   

基于遗传优化BP神经网络的水稻气象产量预报模型   总被引：9，自引：4，他引：5  

罗梦森  景元书  熊世为 《气象科学》2012,32(6):665-670

利用1951—2010年江苏省水稻产量及同期14个气象站点的逐日平均气温、降水资料,采用因子膨化及相关分析,研究了水稻气象产量的影响因子及影响时段。在此基础上建立了逐步回归、PCA-BP神经网络以及PCA-GA-BP神经网络3种产量预报模型。结果表明:(1)7—9月份是水稻产量形成的关键时期,对气温、降水的变化最为敏感,气温对气象产量的影响大于降水;(2)两种神经网络模型预报效果好于回归模型;(3)遗传优化的神经网络模型比未优化模型的训练速度提高了70%左右,预报精度也提高了4.3%。  相似文献