洪涝灾害监测是农情监测的主要任务之一,遥感监测可以弥补地面观测耗人、耗财、信息滞后等诸多不足,已成为洪涝灾害研究领域的重要发展方向。该文基于HJ-1A/1B-CCD数据,以海南岛为研究区,选取研究区内400个训练样本,利用区分度(division degree,DD)对归一化差异水体指数(normalized difference water index,NDWI)、基于蓝光的归一化差异水体指数(normalized difference water index based on blue light,NDWI-B)和混合水体指数(combined index of NDVI and NIR for water body identification,CIWI)3种水体指数进行比较分析。分析结果显示,在应用HJ-1CCD数据进行纯水体、湿地识别时,NDWI-B模型效果最好(综合区分度分别为31.30%、28.13%),是海南岛洪涝灾害监测的最优模型。经验证,NDWI-B模型的水体识别总体精度达91.50%。通过对采样点的水体指数值与地物类型的反复对比确定NDWI-B模型的水体识别阈值为-0.015。利用NDWI-B模型对海南岛2010年9月25日至10月25日的洪涝灾情进行监测。结果表明,10月12日的灾情最为严重,全岛洪水淹没面积达到监测期内最高值,为120.22km2,除东方、昌江、乐东外所有市县均出现新增水体,新增水体主要分布于村庄、耕地、道路、城镇居民地等。从区域上看,东部的文昌、琼海、海口、定安为洪涝重灾区,西部的东方、昌江、乐东为洪涝轻灾区。全岛洪涝影响最大的土地利用类型为水田,其次为旱地。10月12日,水田、旱地的淹没面积分别为61.46和29.59 km2,耕地(水田和旱地)淹没面积占总淹没面积的比例为75.73%。NDWI-B模型具有水陆区分度较大和水体面积提取精度较高的优点外,还能够识别小范围水体和湿地,是海南岛洪涝灾害监测较为理想的模型。该文为海南岛水资源管理、洪涝灾害动态监测及防灾减灾提供参考。
The winter oilseed rape（Brassica napus L.） accounts for about 90% of the total acreage of oilseed rape in China. However, it suffers the risk of freeze injury during the winter. In this study, we used Chinese HJ-1A/1B CCD sensors, which have a revisit frequency of 2 d as well as 30 m spatial resolution, to monitor the freeze injury of oilseed rape. Mahalanobis distance-derived growing regions in a normal year were taken as the benchmark, and a mask method was applied to obtain the growing regions in the 2010–2011 growing season. The normalized difference vegetation index（NDVI） was chosen as the indicator of the degree of damage. The amount of crop damage was determined from the difference in the NDVI before and after the freeze. There was spatial variability in the amount of crop damage, so we examined three factors that may affect the degree of freeze injury： terrain, soil moisture, and crop growth before the freeze. The results showed that all these factors were significantly correlated with freeze injury degree（P0.01, two-tailed）. The damage was generally more serious in low-lying and drought-prone areas; in addition, oilseed rape planted on south- and west-oriented facing slopes and those with luxuriant growth status tended to be more susceptible to freeze injury. Furthermore, land surface temperature（LST） of the coldest day, soil moisture, pre-freeze growth and altitude were in descending order of importance in determining the degree of damage. The findings proposed in this paper would be helpful in understanding the occurrence and severity distribution of oilseed rape freeze injury under certain natural or vegetation conditions, and thus help in mitigation of this kind of meteorological disaster in southern China.
Bao SHEJing-feng HUANGRui-fang GUOHong-bin WANGJing WANG
Accurate estimation of rice phenology is of critical importance for agricultural practices and studies. However, the accuracy of phenological parameters extracted by remote sensing data cannot be guaranteed because of the influence of climate, e.g. the monsoon season, and limited available remote sensing data. In this study, we integrate the data of H J-1 CCD and Landsat-8 operational land imager （OLI） by using the ordinary least-squares （OLS） and construct higher temporal resolution vegetation indices （VIs） time-series data to extract the phenological param- eters of single-cropped rice. Two widely used VIs, namely the normalized difference vegetation index （NDVI） and 2-band enhanced vegetation index （EVI2）, were adopted to minimize the influence of environmental factors and the intrinsic difference between the two sensors. Savitzky-Golay （S-G） filters were applied to construct continuous VI profiles per pixel. The results showed that, compared with NDVI, EVI2 was more stable and comparable between the two sensors. Compared with the observed phenological data of the single-cropped rice, the integrated VI time-series had a relatively low root mean square error （RMSE）, and EVI2 showed higher accuracy compared with NDVI. We also demonstrate the application of phenology extraction of the single-cropped rice in a spatial scale in the study area. While the work is of general value, it can also be extrapolated to other regions where qualified remote sensing data are the bottleneck but where complementary data are occasionally available.
