Soil moisture retrieval from satellite images and its application to heavy rainfall simulation in eastern China

The soil water index (SWI) from satellite remote sensing and the observational soil moisture from agricultural meteorological stations in eastern China are used to retrieve soil moisture. The analysis of correlation coefficient (CORR), root-mean-square-error (RMSE) and bias (BIAS) shows that the retrieved soil moisture is convincible and close to the observation. The method can overcome the difficulties in soil moisture observation on a large scale and the retrieved soil moisture may reflect the distribution of the real soil moisture objectively. The retrieved soil moisture is used as an initial scheme to replace initial conditions of soil moisture (NCEP) in the model MM5V3 to simulate the heavy rainfall in 1998. Three heavy rainfall processes during 13–14 June, 18–22 June, and 21–26 July 1998 in the Yangtze River valley are analyzed. The first two processes show that the intensity and location of simulated precipitation from SWI are better than those from NCEP and closer to the observed values. The simulated heavy rainfall for 21–26 July shows that the update of soil moisture initial conditions can improve the model’s performance. The relationship between soil moisture and rainfall may explain that the stronger rainfall intensity for SWI in the Yangtze River valley is the result of the greater simulated soil moisture from SWI prior to the heavy rainfall date than that from NCEP, and leads to the decline of temperature in the corresponding area in the heavy rainfall days. Detailed analysis of the heavy rainfall on 13–14 June shows that both land-atmosphere interactions and atmospheric circulation were responsible for the heavy rainfall, and it shows how the SWI simulation improves the simulation. The development of mesoscale systems plays an important role in the simulation regarding the change of initial soil moisture for SWI.     

利用反射GPS信号遥感土壤湿度

在水文、气候、农业等问题的研究中,土壤湿度信息十分重要。近年来,利用GPS系统进行遥感的新方法,已经越来越多地引起研究者们的关注。特别是GPS卫星广播的L1（1.57542GHz）频率信号,是土壤湿度遥感的最佳频率。反射GPS信号功率是土壤介电常数的函数,而土壤介电常数又和土壤湿度有关。根据这一原理,美国国家航空与航天局于2002年进行了基于地表面反射GPS信号的遥感土壤湿度实验。结果证明反射GPS信号对土壤湿度特性十分敏感,由于植被的影响对反射率进行了修正,并用此方法模型与其它方法进行了比较。在利用GPS遥感土壤湿度方面,研制高增益天线,更好的修正信号波动的影响,发展更加完善的遥感土壤湿度方法等是今后在此项研究中所要解决的问题。     

冬小麦蒸腾速率在不同土壤水分条件下的修正

为找到不同土壤含水量对蒸腾速率的影响程度,通过对L1-6200便携式作物生理测定仪在不同土壤水分处理下的蒸腾速率观测值与气象条件计算得出的理论值进行比较,确定不同土壤水分条件下的蒸腾速率修正系数.分析发现:①在不同的土壤含水量下,未考虑修正系数的情况时,土壤含水量越小,理论计算误差越大;土壤含水量越大,理论计算误差越小.②土壤含水量小时蒸腾速率计算值的修正系数大,而土壤含水量偏大时修正系数小.③考虑修正系数后,计算误差显著减小,除了土壤含水量为14.89％稍有偏差外,其它土壤含水量状态下的理论计算值非常接近实际观测值.同时验证了不同土壤水分下的修正系数足显著的.     

BDS-3新频点信号双频精密单点定位精度分析

北斗卫星导航系统(BeiDou navigation satellite System, BDS)已于2020年7月正式建成并开通, 北斗三号(BDS-3)在旧信号B1I和B3I的基础上, 增加了B1C、B2a新信号. 为了全面评估BDS-3的新信号B1C、B2a的定位性能, 试验了GPS (Global Positioning System)、BDS-3、BDS-2/BDS-3新旧信号的定位性能和BDS系统不同频点与GPS组合定位性能, 对BDS (B1I+B3I、B1C/B2a)+GPS (L1+L2)组合静态PPP (Precise Point Positioning)定位性能进行分析, 并与单卫星系统对比分析. 试验结果表明: BDS-3 (B1C/B2a)在East (E)、\lk North (N)、Up (U)方向的定位精度优于1.25cm、0.89cm、1.67cm, BDS-3新旧频点在E、N方向上定位精度与GPS L1/L2在同一水平上, U方向上新频点定位精度高于GPS L1/L2和BDS-3旧频点, 较旧频点定位精度提升了34.2%, 新频点收敛时间25.9min比旧频点提升了12.7%; 相较于BDS、GPS单系统, 组合系统BDS/GPS定位精度和收敛时间有了明显的提高, BDS-3 (B1C/B2a)+GPS在E、N方向上与BDS-3 (B1I/B3I)+GPS定位精度相当, 在U方向上定位精度前者较后者有了明显的提升, 提升了17.2%, 组合系统新频点收敛时间20.1min比旧频点提升了17.6%.     

前期土壤湿度、海表面温度对中国夏季极端气温的预测能力评估

基于中国587站日最高、最低气温观测资料、月平均的ERA_interim土壤湿度（Soil Moisture,SM）再分析资料及扩展重建的海表面温度（Sea Surface Temperature,SST）资料（ERSST）,对极端气温指数进行了定义,利用变形的典型相关分析和集合典型相关分析方法（Ensemble Canonical Correlation,ECC）,分析了1979-2009年我国夏季极端气温与前期（春、前冬）SM、SST间的线性联系,建立了中国夏季极端气温预测模型,并对独立样本检验的效果进行了评估。结果表明：1）与中国夏季极端气温联系密切的前期SST异常的空间分布为类PDO（Pacific Decadal Oscillation）型,前期土壤湿度异常的区域为华南、青藏高原、东北和西北地区。2）交叉检验结果表明基于前冬预测因子的极端气温预测模型技巧高于春季,基于SM的极端气温预测模型技巧高于SST。3）独立样本检验表明基于前期SM、SST的ECC模型对中国东部夏季极端气温有一定的预测能力。因此,可以在夏季极端气温的预测业务中考虑前期SM、SST的影响。     

发展地理学研究进展与展望

本文回顾和总结了20世纪以来发展地理学的研究进展,涉及发展地理学内涵和理论、领域和方法及其发展趋势。系统梳理了发展地理学在欠发达国家或地区发展收敛、发达地区或国家生活品质提升过程中的收敛及其路径等研究内容。通过分析国外研究进展,指出了发展地理学在中国的发展条件与学科优势,并着眼于国际学术前沿最新动向与国家宏观战略需求,提出了今后中国发展地理学研究需重点关注的领域。未来发展地理学研究应以可持续发展理论为指引,以提升欠发达地区可持续生计能力与区域绿色发展水平为核心,以构建发展地理学理论和跨学科综合集成研究体系为目标,聚焦区域发展的空间格局、扩散特征与收敛研究,探索出服务于区域经济建设与产业发展的调控政策与科学路径。     

基于深度置信网络的GNSS-IR土壤湿度反演

基于大地测量型GNSS接收机获取的反射信号反演土壤湿度是GNSS领域的研究热点。为克服常规线性回归和BP神经网络算法等的缺陷,本文提出了一种基于深度置信网络的GNSS-IR土壤湿度反演方法。试验结果表明,基于该方法得到的决定系数、土壤湿度平均绝对误差和均方根误差分别为0.909 8、0.017、0.021,与线性回归和BP神经网络算法相比,与实测数据吻合度更高,可有效提高土壤湿度反演精度,证明了方法的有效性和可靠性。     

SMAP卫星海表盐度产品的检验与校正

In this study, sea surface salinity(SSS) Level 3(L3) daily product derived from soil moisture active passive(SMAP)during the year 2016, was validated and compared with SSS daily products derived from soil Moisture and ocean salinity(SMOS) and in-situ measurements. Generally, the root mean square error(RMSE) of the daily SSS products is larger along the coastal areas and at high latitudes and is smaller in the tropical regions and open oceans. Comparisons between the two types of daily satellite SSS product revealed that the RMSE was higher in the daily SMOS product than in the SMAP, whereas the bias of the daily SMOS was observed to be less than that of the SMAP when compared with Argo floats data. In addition, the latitude-dependent bias and RMSE of the SMAP SSS were found to be primarily influenced by the precipitation and the sea surface temperature(SST). Then, a regression analysis method which has adopted the precipitation and SST data was used to correct the larger bias of the daily SMAP product. It was confirmed that the corrected daily SMAP product could be used for assimilation in high-resolution forecast models, due to the fact that it was demonstrated to be unbiased and much closer to the in-situ measurements than the original uncorrected SMAP product.     

中国耕地土壤相对湿度时空分异

以全国653个农业气象站1993-2013年耕地的土壤相对湿度数据为基础,运用地统计方法,分析中国耕地土壤相对湿度时空分异特征与规律。结果表明:自1993年以来全国耕地的土壤相对湿度呈现波动上升趋势。全国耕地的土壤相对湿度普遍大于60%,分布区域自4月中旬开始随夏季风推移不断向北向西扩大,自10月下旬开始向东、南方向缩小。耕地土壤相对湿度值随土壤深度的增加而增加。年际尺度上,耕地的土壤相对湿度在夏秋季上升速度最快,变化幅度随土层深度增加而变小。土壤相对湿度与降水量存在较强的正相关关系,与潜在蒸发量、气温普遍存在较强的负相关关系。土壤相对湿度与各气象要素的相关性随深度加深而减弱。春、夏、秋季气象因素对旱地土壤相对湿度影响较大,冬季气象因素对水田影响更大。