The characteristics of Asian dust events in Northeast Asia during the springtime from 1993 to 2004

The characteristics of Asian dust events that occurred in Northeast Asia during the springtime from 1993 to 2004 are investigated using 3-hourly SYNOP reports (World Meteorological Organization). Occurrences of blowing sand and dust storm are low in 1997 and 1999, but have increased rapidly since 2000. The maximum occurrence was recorded in 2002. Wind velocity of 6.5 m s^− 1 as a threshold wind velocity is not so exactly consistent with the occurrence of blowing sand. However, wind velocity of 14 m s^− 1 as a strong wind causing dust storm had similar tendency to those of dust storm and Dust Storm Index.Source regions of Asian dust are divided into three regions (A: dry arid, B: semi-arid, and C: cultivated), based upon the occurrence of blowing sand and dust storm. Eight meteorological stations are selected in three regions, which have frequent occurrences of blowing sand. Source regions of Asian dust that affect the Korean peninsula are gradually extending eastward. Positive anomalies of NDVI occurred in 1994, 1995, and 1998 when temperature was high and precipitation was heavy. However, the frequent occurrence of the dust phenomena is not always consistent with lots of vegetation, high temperature, and much precipitation in this study.     

STUDY ON THE INTERACTION BETWEEN NDVI PROFILE AND THE GROWING STATUS OF CROPS

Daily and ten-day Normalized Difference Vegetation Index(NDVI) of crops were retrieved from meteorological statellite NOAA AVHRR images ,The temporal variations of the NDVI were analyzed during the whole growing season,and thus the principle of the interaction between NDIV profile and the growing status of crops was discussed,As a case in point,the relationship between integral NDVI and winter wheat yield of Henan Province in 1999 had been analyzed.By putting integral NDVI values of 60 sample counties into the winter wheat yield-integral NDVI coordination,scattering map was plotted. It demonstrated that integral NDVI had a close relation with winter wheat yield.These relation could be described with linear,cubic polynomial ,and exponential regression,and the cubic polynomial regression was the best way,In general ,NDVI reflects growing status of green vegetation ,so crop monitoring and crop yield estimation could be realized by using remote sensing technique on the basis of time serial NDVI data together with agriculture calendars.     

Implications of low-pass filtering on power spectra and autocorrelation functions of turbulent velocity signals

Filtering either through the electronics of an instrument or through digital procedure is performed routinely on geophysical data. When velocity fluctuations are measured in turbulent flows using electromagnetic current meters (ECMs), a builtin lowpass Butterworth filter of order n usually attenuates fluctuations at high frequencies. However, the effects of this filter may not be acknowledged in turbulence studies, thus impeding comparisons between data collected with different ECMs. This paper explores the implications of the filters on the characteristics of velocity signals, mainly on variance, power spectra, and correlation analyses. Variance losses resulting from filtering can be important but will vary with the order n of the Butterworth filter, decreasing as n increases. Knowing the filter response, it is possible to reconstruct the original signal spectrum to evaluate the effect of filtering on variance and to allow comparisons between data collected with different instruments. The autocorrelation function also is affected by filtering which increases the value of the coefficients in the first lags, resulting in an overestimation of the integral length scale of coherent structures. These important effects add to those related to size and shape differences in ECM sensors and must be taken into account in comparative studies.     

中国西部地区基于经验模型的土壤水分反演

地表土壤水分含量的时空分布信息是十分重要的,常常作为水文模型、气候模型、生态模型的输入参数,同时,也是干旱预报、农作物估产等工作的重要指标。被动微波遥感是监测土壤含水量最有效的手段之一。相比红外与可见光,它具有波长长,穿透能力强的优势。相比主动微波雷达,被动微波辐射计具有监测面积大、周期短,受粗糙度影响小,对土壤水分更为敏感,算法更为成熟的优势。目前,已研究出许多反演土壤水分的方法.本课题的主要内容是借助AMSR-E土壤水分影像数据、MODIS归一化植被指数(NDVI)影像数据和MODIS分类影像数据,利用ENVI软件进行遥感图像数据处理,运用统计分析方法建立NDVI与土壤水分的经验模型,研究中国西部地区稀疏植被覆盖区土壤水分的反演。     

Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China

The significance of crop yield estimation is well known in agricultural management and policy development at regional and national levels. The primary objective of this study was to test the suitability of the method, depending on predicted crop production, to estimate crop yield with a MODIS-NDVI-based model on a regional scale. In this paper, MODIS-NDVI data, with a 250 m resolution, was used to estimate the winter wheat (Triticum aestivum L.) yield in one of the main winter-wheat-growing regions. Our study region is located in Jining, Shandong Province. In order to improve the quality of remote sensing data and the accuracy of yield prediction, especially to eliminate the cloud-contaminated data and abnormal data in the MODIS-NDVI series, the Savitzky–Golay filter was applied to smooth the 10-day NDVI data. The spatial accumulation of NDVI at the county level was used to test its relationship with winter wheat production in the study area. A linear regressive relationship between the spatial accumulation of NDVI and the production of winter wheat was established using a stepwise regression method. The average yield was derived from predicted production divided by the growing acreage of winter wheat on a county level. Finally, the results were validated by the ground survey data, and the errors were compared with the errors of agro-climate models. The results showed that the relative errors of the predicted yield using MODIS-NDVI are between −4.62% and 5.40% and that whole RMSE was 214.16 kg ha⁻¹ lower than the RMSE (233.35 kg ha⁻¹) of agro-climate models in this study region. A good predicted yield data of winter wheat could be got about 40 days ahead of harvest time, i.e. at the booting-heading stage of winter wheat. The method suggested in this paper was good for predicting regional winter wheat production and yield estimation.