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地理研究 >

2017 , Vol. 36 >Issue 5: 837 - 849

DOI: https://doi.org/10.11821/dlyj201705003

研究论文

土壤相对湿度在东北地区农业干旱监测中的适用性分析

  • 安雪丽 , 1, 2, 3 ,
  • 武建军 , 1, 2, 3 ,
  • 周洪奎 1, 2, 3 ,
  • 李小涵 1, 2, 3 ,
  • 刘雷震 1, 2, 3 ,
  • 杨建华 1, 2, 3
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  • 1. 北京师范大学民政部/教育部减灾与应急管理研究院,北京 100875
  • 2. 北京师范大学地表过程与资源生态国家重点实验室,北京 100875
  • 3. 北京师范大学环境演变与自然灾害教育部重点实验室,北京 100875
通讯作者:武建军（1971- ）,男,陕西榆林人,教授,博士生导师,研究方向为灾害与风险管理、遥感与GIS。E-mail:

作者简介：安雪丽（1991- ）,女,河南濮阳人,硕士,研究方向为农业干旱监测、评估。E-mail:

收稿日期: 2016-12-04

  要求修回日期: 2017-03-09

  网络出版日期: 2017-05-20

基金资助

国家国际科技合作专项（2013DFG21010）

中央高校基本科研业务费专项资金和教育部创新团队资助项目（IRT1108）

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Assessing the relative soil moisture for agricultural drought monitoring in Northeast China

  • AN Xueli , 1, 2, 3 ,
  • WU Jianjun , 1, 2, 3 ,
  • ZHOU Hongkui 1, 2, 3 ,
  • LI Xiaohan 1, 2, 3 ,
  • LIU Leizhen 1, 2, 3 ,
  • YANG Jianhua 1, 2, 3
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  • 1. Academy of Disaster Reduction and Emergency Management Ministry of Civil Affairs & Ministry of Education, Beijing Normal University, Beijing 100875, China
  • 2. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
  • 3. Key Laboratory of Environment Change and Natural Disaster, Ministry of Education (MOE), Beijing Normal University, Beijing 100875, China

Received date: 2016-12-04

  Request revised date: 2017-03-09

  Online published: 2017-05-20

Copyright

《地理研究》编辑部
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摘要

分析土壤相对湿度（RSM）与标准化植被指数（SVI）、站点农气灾情数据及产量数据的关系,探究土壤相对湿度对东北地区农业干旱的监测能力。结果表明：① 土壤相对湿度与SVI有较好的相关关系,76%的站点能够通过0.05的检验;水分胁迫下,作物生长状态对土壤湿度的滞后时间为10天。② 土壤相对湿度低于60时,超过58%的作物生长状态受到影响;土壤相对湿度低于35时,超过92%的作物生长状态受到影响。③ 土壤相对湿度对农气灾情数据记录的不同等级干旱的正确检测概率都超过了50%。④ 7月上旬土壤相对湿度和产量的相关关系最好。土壤相对湿度在东北地区农业干旱监测中具有较好的适用性,本文可为农业干旱监测提供理论支持。

关键词： 农业干旱; 土壤相对湿度; 标准化植被指数; 作物产量

本文引用格式

安雪丽 , 武建军 , 周洪奎 , 李小涵 , 刘雷震 , 杨建华 . 土壤相对湿度在东北地区农业干旱监测中的适用性分析[J]. 地理研究, 2017 , 36(5) : 837 -849 . DOI: 10.11821/dlyj201705003

Abstract

Soil moisture is an important factor affecting crop growth, development and production. Currently, the presence of a growing number of long-term soil moisture networks allowed users to obtain precise soil moisture data. Therefore, it is reasonable to consider soil moisture observation data as a potential approach for monitoring agricultural drought. In Northeast China, the soil moisture dataset at agro-meteorological stations is relatively complete. In order to study the ability of Relative Soil Moisture (RSM) monitoring agricultural drought, we firstly analyzed the correlation and lag time between relative soil moisture and Standardized Vegetation Index (SVI), and investigated the response of crop growth state to soil moisture. Secondly, by the comparison between relative soil moisture and the drought disaster data recorded by the national agro-meteorological stations, we analyzed the probability of detection of relative soil moisture to drought disaster record data. Finally, the relationship between relative soil moisture and crop yield was analyzed. The results are as follows: (1) The RSM has good correlations with SVI in the growing season, 76% of the stations can pass the 0.05 test. Under water stress, SVI and RSM have the best correlation at 10-day lag. (2) Through the analysis of corresponding relationship between RSM and the 10-day lagged SVI, we point out that the RSM is able to depict the influence of different drought intensities on crop growth status. With the decrease of RSM, the effect on both the crop growth status and the probability are increasing. When RSM is below 60, more than 58% of the crop growth status was affected; When RSM is below 35, more than 92% of the crop growth status was affected. (3) The probabilities of detection of RSM on the different drought grades recorded by the national agro-meteorological stations are all more than 50%. But if we do not classify the RSM into drought grades, the probability of detection of RSM on moderate drought recorded by the national agro-meteorological stations reaches 73%. (4) The impacts of RSM on crop yield during the main growing season were also explored using 10-day RSM data. The result shows that the key period was the first dekad in July. RSM has a good applicability in agricultural drought monitoring in Northeast China, and this study can provide theoretical support for agricultural drought monitoring.

Key words： agricultural drought; relative soil moisture; standardized vegetation index; crop yield

1 引言

干旱被认为是最复杂、影响人口最多、危害最为严重的自然灾害类型之一[1]。受全球变暖引发的降水时空分布不均匀性加剧的影响,近年来中国西南、华北、东北等地气候呈显著的干旱化趋势,极端干旱事件的发生频率也在增加[2-4]。中国是一个农业大国,随着气候变化的不断加剧,农业干旱的风险也在增加[5]。农业干旱监测是减轻干旱对农业的影响的重要途径[6],因此,系统开展农业干旱监测的研究已刻不容缓,应受到政府和学者更多的关注。
农业干旱通常被定义为土壤水分供给无法满足作物水分需求而导致的作物水分亏缺现象[7,8]。农业干旱的研究涉及到各种气象干旱特征如降水短缺、实际蒸散和潜在蒸散的差异、土壤水分亏缺等对农业造成的影响和危害,如植被和农作物的长势受到影响、产量降低等[9]。农业干旱监测以地表温度、土壤湿度和植被生长状态监测为主要内容[10,11]。地气系统中,土壤湿度是一个关键的变量,通过影响地表反照率、热容量、地表蒸发、植被生长状况等影响气候变化[12]。土壤湿度是影响作物生长、发育及产量的重要因素[13],比降水更加关键[14]。
目前利用微波遥感反演土壤湿度是研究的热点,但微波遥感探测的土壤湿度一般表层几厘米,同时受植被及地表粗糙度影响较大,在高密度植被区微波信号甚至无法穿透植被[15],因此目前无法利用微波遥感反演的土壤湿度有效地监测农业干旱。站点观测土壤湿度数据精确度高,但由于土壤湿度观测起步晚,先前利用观测土壤湿度数据直接进行农业干旱监测的研究较少。虽然中国气象局出版的《气象干旱等级国家标准（GB/T 20481-2006）》已经对土壤相对湿度进行了干旱等级的划分,但土壤相对湿度在农业干旱监测中的适用性及其监测农业干旱的能力并未得到关注。目前站点观测土壤湿度数据的时间序列在增长,全球观测网点在增多,如美国的土壤气候分析网络（Soil Climate Analysis Network,SCAN）、国际土壤水分观测网络（International Soil Moisture Network,ISMN）、中国的农气站点等,土壤湿度数据的可获得性在增强,将观测土壤湿度作为农业干旱监测潜在的研究方法是合理的[16],应充分利用站点土壤湿度数据加强农业干旱监测的研究。
东北地区日照充足、雨热同期,适宜作物生长,是我国重要的商品粮食生产基地。近年来,受气候变化的影响,东北地区干旱加剧,年平均干旱面积逐步上升[17],2004年因旱损失118.9亿元,2007年高达303.7亿元[18]。同时,东北大部分地区为雨养农业[19],农气站点土壤湿度的观测资料较为完整[20]。本文旨在通过土壤相对湿度（relative soil moisture,RSM）与标准化植被指数（standardized vegetation index,SVI）、产量及站点农气灾情数据的对比,分析土壤干湿状况对作物生长状态及产量的影响及土壤湿度对农气旱灾灾情记录数据的正确检测概率,以评估土壤相对湿度在东北地区农业干旱监测中的适用性,探究土壤相对湿度监测东北地区生长季（5-9月）农业干旱的能力。

2 研究方法与数据来源

2.1 研究区概况

本文以中国东北地区为研究区,包括黑龙江省、吉林省、辽宁省及内蒙古自治区的呼伦贝尔市、兴安盟、通辽市和赤峰市,地处38°42'N~53°35'N、115°32'E~135°09'E,总面积为124万km2,如图1所示。该区三面环山,中部和南部为平原,地貌类型丰富。东北地区属温带季风气候,四季分明,夏季温热多雨,冬季寒冷干燥,雨热同期的特点对农业生产较为有利。自东南向西北,年降水量自1000 mm降至300 mm以下,从湿润区、半湿润区过渡到半干旱区。区域内河流分布广泛,日照充足,为粮食生产提供了有利条件[21]。地表覆盖类型丰富多样,以针叶林、针阔混交林、草甸草原和农田为主。东北平原区土壤肥沃,主要以黑土型土壤为主体,土壤的农业生产能力较高[10]。主要农作物是大豆、玉米、小麦和水稻,其中玉米的播种面积占全国玉米总播种面积的26.6%[22]。
Fig.1 Study area showing the MODIS cropland and agro-meteorological stations in this study

图1 研究区作物及农气站点分布图

2.2 数据来源与预处理

本文使用的数据包括遥感数据、土壤相对湿度数据、农业气象灾情数据、农作物产量数据等。
2.2.1 遥感数据及预处理 遥感数据包含归一化植被指数（normalized different vegetation index,NDVI）数据和地表覆盖类型数据。其中NDVI为2000-2013年东北地区生长季（5-9月）逐旬SPOT-VEGETATION数据,空间分辨率为1 km,数据来源于VITO影像下载中心（http://free.vgt.vito.be/）。该产品基于最大值合成法合成,经过了大气校正、辐射校正、几何校正等,并将-1~-0.1的NDVI值设置为-0.1。该数据为HDF格式的DN值信息,需使用专门的VEGETATION数据处理工具VGTExtract软件下的VGTExtract Batch对下载的数据进行批量化的范围提取和格式转换,将HDF格式转换为TIFF格式,同时获得各波段DN值信息。SPOT-VEGETATION原始数据集DN值域范围为0~255,有效值为0~250。从DN值转化成NDVI值的关系式为[23]：
NDVI = DN × 0.004 - 0.1 （1）
以站点所在像元为中心,在遥感影像上设定一个3×3像元窗口,选择窗口内所有有效像元的平均值作为与该站点匹配的NDVI值。3×3像元窗口在研究中常被用来匹配站点观测数据和卫星遥感数据[24-25],该窗口的选择避免了遥感数据极端值的出现[26]。
地表覆盖类型数据为MODIS数据产品,来源于美国LPDAAC（Land Processes Distributed Active Archive Center）网站（https://lpdaac.usgs.gov/）,空间分辨率为1 km,采用国际地圈生物圈计划（IGBP）的全球植被分类方案,在ArcGIS中提取地表覆盖类型为农作物的像元作为本文的农业区（图1）。
2.2.2 土壤相对湿度数据及预处理 土壤相对湿度数据（实测土壤重量含水率和田间持水量的比值,单位为%）来源于中国气象数据网（http://cdc.cma.gov.cn/）,该数据观测时间暖季为每10天一次（每个月的8日、18日、28日）,冻土地区冬季没有观测。本文选用2000-2013年深度分别为10 cm、20 cm、50 cm的土壤相对湿度数据,同时还计算了10 cm、20 cm、50 cm土壤相对湿度均值作为根区的土壤相对湿度（简称根区土壤相对湿度）。
本文主要探究土壤相对湿度在农业干旱监测中的适用性,因此选择的农气站点应该是周围无建筑物影响、下垫面为作物的像元纯度较高的站点,借助谷歌地球对农气站点进行筛选。东北地区2000-2013年生长季（5-9月）土壤相对湿度数据较为完整的农气站点共69个,利用谷歌地球对这69个农气站进行一一验证,最终筛选出下垫面覆盖为农作物且周围无建筑物影响的站点29个（图1）。
2.2.3 农业气象灾情数据及预处理 农业气象灾情数据来自中国气象局收集整理的《中国农业气象灾情旬值数据集》,由中国气象数据网获得。数据集包括全国588个农业气象台站逐旬发生的农业气象灾害名称、受害作物、灾害发生日期、受害程度、灾害强度、受害面积以及受害百分比。本文将2000-2013年东北地区各农气站点每个旬的一条灾害名称为干旱灾害的记录定义为一次干旱事件。通过对比分析根区土壤相对湿度和农业气象灾情记录的干旱事件,计算了根区土壤相对湿度对不同等级干旱的正确检测概率。
2.2.4 农作物产量数据及预处理 农作物产量数据来自中国气象局收集整理的《中国农作物产量资料旬值数据集》,包括作物名称、耕作制度、亩实际产量等。从29个农气站点中共筛选出17个产量数据较为完整的农气站点。

2.3 研究方法

2.3.1 标准化植被指数 标准化植被指数由Peter等[27]基于NDVI距平建立。首先计算植被指数的距平,然后利用标准差对计算结果进行标准化,计算公式为：
SVI = NDV I ijk - NDVI ¯ ij σ ij （2）
式中：NDVIijk为i像元（或站点）第k年j旬的NDVI值; NDVI ¯ ij 为i像元（或站点）第j旬的NDVI的平均值; σ ij 为i像元（或站点）第j旬的NDVI的标准差。SVI指数反映了NDVI偏离正常值的程度,且该指数消除了植被类型空间变异,使不同地区植被指数的变化具有空间可比性[28]。本文使用NDVI表征作物生长状态,通过SVI指数衡量区域作物生长状态的异常情况。SVI小于0的像元认为是作物生长状态异常的像元。
2.3.2 减产率 本文通过减产率指标探究干旱对作物产量的影响,其计算方法如下[29]：
YLR = Y - Y ̅ Y ̅ （3）
式中：YLR为某站点某一年的减产率;Y为某站点某一年的产量; Y ̅ 为某站点产量均值。
2.3.3 正确检测概率 以农气灾情旬值数据记录的干旱事件为评定标准,通过分析正确检测概率（POD）[30],探究土壤相对湿度对不同等级干旱的监测能力,POD的计算公式为：
POD = H H + M （4）
式中：H为土壤相对湿度和站点农气灾情数据同时识别为某一等级干旱的次数;M为站点农气灾情数据记录为某一等级干旱而土壤相对湿度指示的不是该等级干旱的次数。
2.3.4 皮尔逊相关系数 本文通过计算土壤相对湿度和SVI、产量等的皮尔逊相关系数,评价土壤相对湿度在东北地区的干旱监测能力,探究不同生长阶段的水分亏缺对作物产量的影响,其计算公式为[29]：
R = ∑ i = 1 n ( x i - x ̅ ) ( y i - y ̅ ) ∑ i = 1 n ( x i - x ̅ ) 2 ∑ i = 1 n ( y i - y ̅ ) 2 （5）
式中： x ̅ 、 y ̅ 分别为x、y样本的平均数。

3 结果分析

3.1 土壤相对湿度与标准化植被指数的关系

3.1.1 标准化植被指数对土壤相对湿度的响应 NDVI可用来表征作物生长状态,SVI指数可用来衡量区域作物生长状态的异常情况。干旱发生时,作物生长状态对水分胁迫的响应存在一定的滞后性[31]。已有研究都集中于NDVI对降水[32]、气象干旱指数的响应[31,33,34],分析农业区NDVI对土壤湿度的响应的文章较少[35]。本文计算了2000-2013年生长季5-9月各旬SVI分别与当旬、前1旬、前2旬、前3旬、前4旬、前5旬,前6旬的各层深度土壤相对湿度的相关系数,在得出的7个相关系数中取最大相关系数对应的滞后时间为作物生长状态对土壤湿度响应的滞后时间,各站点最大相关系数和滞后时间空间分布如图2所示（其中各站点SVI与各层土壤相对湿度的最大相关系数单独列出,见表1）。由图可知,76%的站点相关性都能通过0.05的检验,相同站点不同深度的土壤相对湿度和SVI的相关性也不同,但总体上黑龙江旱作黑土区土壤相对湿度和SVI相关性较其他地方好。
Fig.2 Spatial distribution of the maximum correlation coefficient by significance test site and lag time for SVI and relative soil moisture at different depths

图2 各站点SVI与土壤相对湿度的最大相关系数及各站点SVI对土壤相对湿度响应的滞后时间空间分布图

Tab.1 The maximum correlation coefficient between SVI and RSM at different depths

表1 SVI与各层土壤相对湿度最大相关系数表

农气站点 RSM（10 cm） RSM（20 cm） RSM（50 cm） RSM（0~50 cm）
50468（黑河） 0.37** 0.36** 0.37** 0.38**
50564（孙吴） 0.22** 0.24** 0.45** 0.31**
50655（德都） 0.44** 0.48** 0.45** 0.48**
50658（克山） 0.30** 0.32** 0.35** 0.35**
50742（富裕） 0.41** 0.49** 0.45** 0.47**
50756（海伦） 0.20** 0.25** 0.29** 0.23**
50788（富锦） 0.32** 0.32** 0.16* 0.33**
50879（桦南） 0.43** 0.47** 0.48** 0.45**
50949（前郭尔罗斯） 0.14* 0.19** -0.14 0.08
50953（哈尔滨） 0.26** 0.16* 0.05 0.19**
50954（肇源） 0.15* 0.06 0.01 0.11
50958（阿城） 0.24** 0.22** 0.32** 0.24**
50964（方正） 0.28** 0.24** -0.01 0.19**
54049（长岭） 0.22** 0.25** 0.17* 0.24**
54064（农安） 0.23** 0.19** 0.25** 0.23**
54072（榆树） 0.17* 0.15* 0.16* 0.16*
54080（五常） 0.07 0.09 -0.01 0.02
54154（梨树） 0.25** 0.30** 0.42** 0.34**
54165（双阳） 0.17* 0.08 0.02 0.10
54213（翁牛特旗） 0.09 0.07 0.18** 0.09
54236（彰武） 0.23** 0.30** 0.29** 0.24**
54266（梅河口） 0.18** 0.22** 0.24** 0.21**
54291（珲春） 0.08 0.08 0.20** 0.13
54292（延吉） -0.05 -0.04 -0.17 -0.09
54326（叶柏寿） 0.10 0.11 0.18** 0.13
54335（黑山） 0.07 0.14* 0.14* 0.08
54454（绥中） 0.23** 0.25** 0.18** 0.23**
54563（瓦房店） 0.29** 0.34** 0.42** 0.37**
54584（庄河） 0.21** 0.25** 0.16* 0.25**

注：*、**分别表示0.05显著性水平和0.01显著性水平。

各站点SVI对土壤相对湿度的响应都有一定的滞后时间,但不同站点滞后时间不同,分析各站点SVI对根区（0~50 cm均值）土壤相对湿度的滞后时间可知,东北北部地区,SVI对土壤湿度的滞后时间较长,约为1个月,该地区为旱作黑土区,土壤保水性强。东北地区中、南部SVI对土壤湿度的滞后时间多为10天。
基于根区（0~50 cm均值）土壤相对湿度,探究土壤相对湿度小于60[36]的事件下作物生长状态对土壤湿度的响应时间。分析得出土壤相对湿度小于60的事件下SVI与根区土壤相对湿度的相关关系在滞后10天时达到最大（n=168,R=0.19）,说明在受到水分胁迫时,作物生长状态对土壤水分的响应时间约为10天。其他研究中,也得到了相似的结论,Adegoke等[37]选择了美国两个站点,一个为玉米种植带,一个为林地,研究了1990-1994年生长季NDVI和根区土壤湿度的关系,指出NDVI对土壤湿度的滞后时间在2周时达到最大;Li等[38]通过分析中国内蒙古海拉尔站点作物湿度指数（CMI）和NDVI距平（NDVIA）的相关关系指出,NDVI和土壤湿度的滞后时间为1旬。因为地域不同气候条件不同以及作物的差异,上述研究和本文得出的作物生长状态对土壤湿度的滞后时间不同,但结果相似。
SVI数据较为完整的农气站点共29个,时间序列为2000-2013年,共14年。理想情况下,站点时间序列样本数量为406个（样本数量=14年×29站点）。但除去一些数据缺失,最终参与分析的有效样本数为348个。但由前面的分析可知,SVI和土壤相对湿度存在一定的滞后关系,若分析所有站点生长季各旬土壤相对湿度和SVI时间序列相关性（图3）,并不是所有旬的相关性都能通过显著性检验,但7月2旬土壤相对湿度和SVI的关系最好,都通过了0.05的显著性检验,其中0~50 cm土壤相对湿度和SVI的关系要优于其他几层土壤相对湿度。
Fig.3 Correlation between dedak RSM and SVI during the main growing season in Northeast China (n=348)

图3 东北地区生长季不同时段（旬尺度）土壤相对湿度和SVI的相关关系分析（n=348）

3.1.2 土壤相对湿度对标准化植被指数的影响 土壤相对湿度可表征一段时期内该站点土壤的干湿情况,SVI可表征NDVI相对于正常值的偏离程度。对根区土壤相对湿度（0~50 cm均值）进行分级（表2）,在土壤相对湿度小于60的区间内划分7个等级,提取各等级内根区土壤相对湿度对应的区站号、时间等信息,每个站点每旬的一条记录定义为一次水分亏缺事件,因为东北地区重度和极端干旱事件较少,轻度和中等干旱事件偏多,因此土壤相对湿度越小时提取的水分亏缺事件总数越少。同时提取对应的滞后各等级土壤相对湿度10天的SVI,统计各土壤湿度等级下SVI<0的事件数占总事件数的比例并计算SVI的均值,SVI<0的事件占总事件数的比例表征作物生长状态受土壤湿度影响的概率,SVI的均值表示作物生长状态受土壤湿度的影响程度。对结果进行趋势回归分析,得出土壤干湿状况对作物生长状态的影响结果。
Tab.2 Proportion of SVI in relative soil moisture at different degrees

表2 不同土壤相对湿度分级下SVI变化情况

等级 土壤相对湿度（%） 水分亏缺事件总数 SVI<0事件数 比例（%） SVI均值
1 55≤RSM<60 177 103 0.5820 -0.2637
2 50≤RSM<55 119 73 0.6134 -0.2562
3 45≤RSM<50 82 52 0.6341 -0.3137
4 40≤RSM<45 56 38 0.6786 -0.4235
5 35≤RSM<40 35 24 0.6857 -0.4884
6 30≤RSM<35 13 12 0.9231 -0.6657
7 RSM<30 4 4 1.0000 -1.0569
土壤相对湿度能够很好地刻画不同干湿状况对作物生长状态的影响。随着土壤湿度的降低,农业区作物生长状态受影响情况加剧,同时作物生长状态受影响的概率增加。SVI<0的比例从62%增加至100%。回归分析显示,SVI<0的事件比例随土壤相对湿度的降低呈线性增长趋势（图4）,回归方程R=0.911（P<0.005）。
Fig.4 Variation of NDVI anomaly proportion along with the drought intensity

图4 作物生长状态异常（SVI<0）的事件比例随土壤水分胁迫加剧的变化趋势

3.2 土壤相对湿度与农气旱灾灾情数据的对比

中国气象数据网的农业气象灾情旬值数据集是根据中国农业气象台站上报的农业气象旬月报报文资料整理而得,在农气旱灾灾情记录方面,具有一定的代表性。为了更好地表达土壤相对湿度对不同等级干旱的正确检测概率,本文对土壤相对湿度进行了干旱等级划分：RSM<40为重度干旱,40≤RSM<50为中度干旱[36]。提取根区土壤相对湿度（0~50 cm均值）小于40的事件表达土壤水分的重度亏缺状况,与站点记录资料对比结果如表3所示。
Tab.3 Comparison between severe drought event monitored by root zone relative soil moisture and historical records

表3 土壤相对湿度指示的重度干旱事件和站点农气旱灾灾情数据对比

区站号 年份 月份 土壤相对湿度 农业旱灾灾情记录
50468 2007 7月2旬-8月1旬 RSM<40(重旱) 2007年7月2旬至8月1旬,春小麦和大豆发生了重度干旱,受旱面积近100万亩,大豆受害百分比接近100%
50655 2001 6月1旬-7月2旬 RSM<40(重旱) 2001年6月1旬至7月2旬发生了中度干旱事件,其中6月3旬发生重度干旱事件
50742 2007 8月1旬-9月1旬 RSM<40(重旱) 2007年8月1旬至9月2旬都发生了重度干旱
54049 2007 8月1旬-9月3旬 RSM<40(重旱) 2007年8月1旬为重度干旱,8月2旬为中度干旱,8月3旬至9月3旬为重度干旱,受旱面积超过100万亩,受旱百分比为90%~100%
54213 2003 8月2旬-9月3旬 RSM<40(重旱) 2003年8月2旬至9月2旬,发生重度干旱灾害,受旱面积超过100万亩,受旱百分比为90%~100%
54326 2009 9月2旬 RSM<40(重旱) 2009年9月1旬至9月3旬发生重度干旱灾害,受旱面积超过100万亩,受旱百分比达80%~89%
为统计根区土壤相对湿度对不同等级干旱的正确检测概率,以农业气象灾情旬值数据集记录的干旱灾害事件作为基础评价数据,计算了根区土壤相对湿度对重旱和中旱的正确检测概率（表4）。同等程度干旱POD表示站点农气旱灾灾情记录资料为重旱且根区土壤相对湿度指示为重旱的概率,而干旱POD表示站点农气旱灾灾情记录资料为重旱而根区土壤相对湿度指示为干旱（RSM<60）的概率,“中旱”的表达意义同“重旱”。由表可知,根区土壤相对湿度数据对农业气象灾情旬值数据集中记录的重旱和中旱事件的正确检测概率都超过了50%,对重旱的正确检测概率要高于对中旱的正确检测概率。但若不对根区土壤相对湿度划分干旱等级,只分为干旱（RSM<60）和非干旱（RSM≥60）时,土壤相对湿度对中旱的正确检测概率较高,达到了73%,说明根区土壤相对湿度监测的干旱和农气旱灾灾情资料能较好的吻合,根区土壤相对湿度能较好的指示和监测东北地区农业干旱。
Tab.4 The probability of different degree drought monitored by root zone soil moisture

表4 根区土壤相对湿度对不同等级干旱的正确检测概率

重旱 中旱
同等程度干旱POD 0.58 0.5
干旱POD 0.65 0.73

3.3 生长季土壤相对湿度和产量的关系

本文通过分析生长季不同时段土壤相对湿度和作物减产率的关系,探究了不同生长阶段的水分亏缺对作物产量的影响,以期为干旱预警确定关键期。但一个地区的产量同时还可能受其他因素的影响,如洪涝灾害或虫灾等,为排除其他因素对产量造成损失和影响,本文分析了2000-2012年生长季各研究站点中国农业气象灾害资料数据集,干旱灾害和冷害发生频率较高,但冷害的受灾强度和受害面积均不明,对产量影响较小。其他灾害如暴雨和洪涝对产量有一定的影响,但发生频率较低。总体上干旱灾害发生频率高,受灾面积大,受灾强度严重。故在研究的过程中,先假设产量的异常主要和干旱灾害相关。产量数据较为完整的农气站点共17个,时间序列为2000-2012年,共13年。理想情况下,站点时间序列样本数量为221个（样本数量=13年×17站点）但除去一些数据缺失,最终参与分析的有效样本数为178个。东北地区生长季各旬土壤相对湿度和产量的相关关系如图5所示。由图可知,7月1旬土壤相对湿度和产量的关系最好,除50 cm土壤相对湿度外,都通过了0.05的显著性检验,其中10 cm土壤相对湿度和产量的关系要优于其他几层土壤相对湿度。Li等[38]研究东北海伦站CMI和产量的相关关系时指出,6月下旬和7月上旬是作物的关键期,和本文的结果相似。
Fig.5 Correlation between dedak relative soil moisture and crop yield during the main growing season in Northeast China （n=178）

图5 东北地区生长季不同时段（旬尺度）土壤相对湿度和产量的相关关系分析（n=178）

4 结论与讨论

4.1 结论

受全球变暖引发的降水时空分布不均匀性加剧的影响,近年来中国西南、华北、东北等地气候呈显著的干旱化趋势,系统开展农业干旱监测的研究已刻不容缓。目前,全球土壤湿度观测网在增多,土壤湿度数据的可得性和精确性在提高。土壤湿度在中国也已得到了大范围的观测,特别是东北地区,农气站点较多,土壤湿度资料较为完整,站点观测土壤湿度数据应成为研究农业干旱监测的一个重要数据源,基于站点观测土壤湿度数据的农业干旱监测的研究应得到更为广泛的关注。本文通过土壤相对湿度和SVI、站点农气灾情数据及产量的对比,评估了土壤相对湿度在东北地区生长季农业干旱监测中的适用性和干旱监测能力。主要结论为：
（1）土壤相对湿度能够较好地表达水分亏缺对作物生长状态的影响。黑龙江旱作黑土区土壤相对湿度和SVI相关性较其他地方好,雨养条件下,土壤相对湿度能够很好地监测作物的生长状态。不同站点作物生长状态对土壤湿度的滞后时间不同,水分胁迫下SVI和土壤相对湿度的相关关系在滞后10天时达到最大,说明在受到水分胁迫时,作物生长状态对土壤水分的响应时间约为10天。
（2）土壤相对湿度能够较好地指示东北地区农业干旱的严重性。作物生长状态异常（SVI<0）的事件比例随干旱程度的加剧呈线性增长趋势,随着土壤湿度的降低,农业区作物生长状态受影响情况加剧,同时作物生长状态受影响概率增加。
（3）根区土壤相对湿度监测的水分亏缺事件和站点农业气象旱灾灾情资料能较好的吻合,根区土壤相对湿度数据对农业气象灾情旬值数据集中记录的重旱和中旱事件的正确检测概率都超过了50%。
（4）7月上旬土壤相对湿度和产量及SVI的相关关系最好。

4.2 讨论

本文表明土壤相对湿度干旱指标在东北地区农业干旱监测中有较好的适用性和监测能力,但还存在一些不足。本文的土壤相对湿度为旬值数据,不能够连续的反映土壤湿度的变化,若有灌溉事件发生,其结果也会表现在测定的土壤湿度数据中,可能会存在灌溉第2天测量或者灌溉后10天再测量土壤湿度的极端情况,使土壤相对湿度出现一定的波动,从而对分析的结果产生一定的影响。有研究基于站点实验观测日值土壤湿度数据进行干旱的研究,王文等[39]利用中国气象局兰州干旱气象研究所及南京信息工程大学干旱监测联合科学试验站土壤湿度数据,分析了2011年长江中下游冬春连旱期土壤湿度的变化,同时分析了气象干旱和农业干旱的变化关系等。Wang等[40]选择了土壤气候分析网络（SCAN）中的三个位于不同气候区的站点,基于日观测土壤湿度数据,分析了不同气候区划（湿润与半干旱）、不同植被类型（灌木和草地）下,NDVI对土壤湿度的响应。但上述研究都是基于一个或几个站点开展,大区域尺度下土壤湿度日值数据的实验观测站点较少。2015年后,中国部分农气站点开始进行土壤湿度的自动观测,数据为0~200 cm逐时数据,每10 cm为一监测层,该数据可以准确的表达水分的动态变化。采用实时数据分析可以更好地克服灌溉的影响,随着数据时间序列的增长,时间分辨率的减小,基于站点土壤湿度数据的农业干旱监测的研究可能会成为热点,可通过逐时的土壤湿度数据分析水分的动态变化特征,探究土壤湿度对植被生长状态、植被净初级生产力（NPP）以及产量等的影响规律。
黑龙江地区土壤相对湿度和SVI相关性较好,与灌溉因素有一定的关系。因为SVI对土壤相对湿度有一定的滞后,灌溉因素会破坏数据的原始特征,影响土壤相对湿度和SVI的相关性。结合国际水资源管理协会（International Water Management Institute,IWMI）发布的全球灌溉区分布图（GIAM10 km-8classes：Version2.0）数据,黑龙江地区的多数站点都位于非灌区,吉林和辽宁的70%以上的站点都位于灌区。但影响土壤相对湿度与SVI相关性的因素不只是灌溉,还可能与作物类型、土壤类型等有关,值得进一步探究。
本文因NDVI遥感数据的空间分辨率为1 km,不能提取出相邻区域不同作物的NDVI值,因此在探讨土壤相对湿度与SVI及产量的关系时,并未分作物探讨,在以后的研究中可以实地选点观测研究地区的NDVI,和土壤相对湿度匹配,探讨不同作物类型下NDVI对土壤湿度的响应。通过研究土壤湿度对不同作物类型下NDVI的影响规律,预测作物的生长状态,可为干旱灾害的监测及预警提供数据及理论基础。
近期,随着土壤湿度研究的增多,基于土壤湿度数据的干旱指数的提出也随之增多,这些指数可以成为水灾害管理特别是干旱、洪涝等研究的有用工具,同时其在各地区的适用性及干旱监测的能力有待进行更深入的研究。

The authors have declared that no competing interests exist.

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马柱国, 任小波. 1951-2006年中国区域干旱化特征. 气候变化研究进展, 2007, 3(4): 195-201.

[Ma Zhuguo, Ren Xiaobo.Drying trend over China from 1951 to 2006. Advances in Climate Change Research, 2007, 3(4): 195-201.]

[4]
邹旭恺, 张强. 近半个世纪我国干旱变化的初步研究. 应用气象学报, 2008, 19(6): 679-687.利用我国地面606个气象观测台站1951—2006年的逐日降水量和平均气温资料,使用《气象干旱等级》国家标准中推荐使用的综合气象干旱指数（IC）,分析了近半个世纪以来全国及不同地区干旱变化情况。结果表明：总体而言,全国干旱面积在近50年没有显著增加或减少的趋势,但不同地区差异较大;其中东北和华北地区干旱化趋势显著,特别是20世纪90年代后期至21世纪初,上述地区发生了连续数年的大范围严重干旱,在近半个世纪中十分罕见;东北、华北和西北地区东部的大部分地区在近50年中持续时间最长的干旱事件多发生在1980年以后的20多年中,而且上述地区在近20多年来干旱发生得更加频繁。另外,我国干旱化趋势最显著的地区与增暖幅度最大的地区有很大的一致性,表明区域增暖在干旱变化中起着一定作用。

DOI

[Zou Xukai, Zhang Qiang.A preliminary study of drought changes in China for nearly half a century. Journal of Applied Meteorological Science, 2008, 19(6): 679-687.]

[5]
Li Y, Ye W, Wang M, et al.Climate change and drought: A risk assessment of crop-yield impacts. Climate Research, 2009, 39(1): 31-46.We assessed the drought risk for world crop production under current and future climatic conditions by using an integrated approach to analyze the correlation between historical crop yield and meteorological drought. Future drought frequencies are estimated based on ensemble results from 20 general circulation model (GCM) climate change patterns and 6 emissions scenarios from SRES (Special Repo...

DOI

[6]
刘宪锋, 朱秀芳, 潘耀忠, 等. 农业干旱监测研究进展与展望.地理学报, 2015, 70(11): 1835-1848.本文全面分析了农业干旱的概念内涵及其与其他干旱类型之间的关系，进而从基于站点监测和基于遥感监测两个方面，系统梳理了国内外农业干旱监测的近今进展，对比了不同干旱监测指标的适用范围和局限性；同时，通过文献统计和重要文献引用揭示了国内外农业干旱监测研究的发展历程和最新进展，即农业干旱监测指标从传统的单一气象监测指标逐渐向气象与遥感相结合的综合监测指标转变。最后，在分析农业干旱监测现有挑战和困境的基础上，将农业干旱监测未来发展趋向归纳为5点展望，即进一步明晰农业干旱发生机理和受旱过程、识别农业干旱影响因素及其相互作用关系、构建多时空尺度农业干旱监测模型、耦合农业干旱定性表征与定量评估模型以及提高农业干旱监测模型中遥感数据的应用水平。

DOI

[Liu Xianfeng, Zhu Xiufang, Pan Yaozhong, et al.Agricultural drought monitor: Progress, challenges and prospect. Acta Geographica Sinica,2015, 70(11): 1835-1848.]

[7]
Mishra A K, Singh V P.A review of drought concepts. Journal of Hydrology, 2010, 391(1): 202-216.Owing to the rise in water demand and looming climate change, recent years have witnessed much focus on global drought scenarios. As a natural hazard, drought is best characterized by multiple climatological and hydrological parameters. An understanding of the relationships between these two sets of parameters is necessary to develop measures for mitigating the impacts of droughts. Beginning with a discussion of drought definitions, this paper attempts to provide a review of fundamental concepts of drought, classification of droughts, drought indices, historical droughts using paleoclimatic studies, and the relation between droughts and large scale climate indices. Conclusions are drawn where gaps exist and more research needs to be focussed.

DOI

[8]
Wilhite D A, Glantz M H.Understanding the drought phenomenon: The role of definitions. Water International, 1985, 10(3): 111-120.Abstract Numerous definitions of drought are reviewed to determine those characteristics scientists consider most essential for a description and an understanding of the phenomenon. Discusses the far-reaching impacts of drought on society, and suggests that definitions of drought are typically simplistic, and, in that way, often lead to a rather poor understanding of the dimensions of the concept.-from Authors

DOI

[9]
何斌, 武建军, 吕爱锋. 农业干旱风险研究进展. 地理科学进展, 2010, 29(5): 557-564.农业干旱风险分析是近年来兴起的一个新的研究领域,这一研究不仅是农业旱灾风险管理的基础和前提,也是农业干旱风险区划和灾前损失预评估的理论基础。本文系统阐述了农业旱灾风险的内涵及构成要素、风险分析体系及研究现状,在此基础上指出,依据自然灾害风险分析基本原理,从农业旱灾危害性以及承灾体脆弱性角度系统地建立农业旱灾综合风险分析程序框架和指标体系,尤其是开发针对每一风险要素的、动态的数学模型和指标体系是当前干旱灾害风险时空格局研究的当务之急。

DOI

[He Bin, Wu Jianjun, Lv Aifeng.New advances in agricultural drought risk study. Progress in Geography, 2010, 29(5): 557-564.]

[10]
唐鹏钦, 杨鹏, 陈仲新, 等. 利用交叉信息熵模拟东北地区水稻种植面积空间分布. 农业工程学报, 2013, 29(17): 96-104.作物时空分布变化是农业研究的重要内容。近30a来,东北地区水稻种植面积显著增加,为探讨 东北地区水稻时空变化特征,进一步丰富和完善作物空间分布信息获取方法,研究作物空间分布对包括气候变化在内的多种影响因素的响应关系,该研究综合80年 代以来的作物面积与产量统计数据、耕地数据、农业灌溉数据以及作物生长适宜性分布等多源数据,利用基于交叉信息熵原理的作物空间分配模型（spatial production allocation model,SPAM）构建了针对中国作物分布特点的SPAM-China模型,模拟了中国东北地区1980-2008年像元尺度上水稻空间分布信息。结 果表明,模拟结果能较好地反映出东北地区水稻主要种植区域,近30a东北地区水稻种植时空变化特征显著,水稻种植区域向北向东扩展,种植重心北移了约 1.76个纬度,中北部地区水稻种植面积增加且趋势明显,南部地区变化趋势不显著。

DOI

[Tang Pengqin, Yang Peng, Chen Zhongxin, et al.Using cross-entropy method simulates spatial distribution of rice in Northeast China. Transaction of the Chinese Society of Agricultural Engineering, 2013, 29(17): 96-104.]

[11]
杨绍锷, 闫娜娜, 吴炳方. 农业干旱遥感监测研究进展. 遥感信息, 2010, (1): 103-109.

[Yang Shaoe, Yan Nana, Wu Bingfang.Advances in agricultural drought monitoring by remote sensing. Remote Sensing Information, 2010, (1): 103-109.]

[12]
左志燕, 张人禾. 中国东部春季土壤湿度的时空变化特征. 中国科学: 地球科学, 2008, 38(11): 1428-1437.

[Zuo Zhiyan, Zhang Renhe.Spatial and temporal variations of soil moisture in spring in East China. Scientia Sinica: Earth Science, 2008, 38(11): 1428-1437.]

[13]
孙倩倩. 东北地区土壤湿度的时空分布特征及其预报方法的研究. 北京: 中国气象科学研究院硕士学位论文, 2013.本文基于REOF与CAST相结合的区划方法，以及小波分析方法，结合东北地区的气候背景，探讨了东北地区近30年（1981-2010）年3-10月份土壤湿度的时空变化特征，分析了土壤湿度的主要气候影响因子，利用逐步回归方法建立了东北地区不同区域的土壤湿度预报模型。结果表明： （1）与土壤湿度有关的气候背景通过干燥度指标进行分析，降水量和蒸散量变化的不一致直接导致了干燥度的变化，使得东北地区西部的气候呈显著变干趋势，而其它区域变化趋势不明显。 （2）东北地区的土壤湿度可划分为五个区域：第一区位于整个东北地区的北部，第二区位于大兴安岭以西的部分，第三区位于松辽平原的南部，第四区位于嫩江和松花江流域一带，第五区则位于东北地区的东南部。东北地区中部的土壤湿度在近30年内有一个上升的趋势，而其余部分则有下降的趋势，但西部下降趋势较大。从垂直方向上看，除去东北部之外，其余地区20-30cm层的土壤湿度变化幅度皆比0-10cm和10-20cm层的土壤湿度变化幅度大。同时，东北地区的土壤湿度存在着3-5年的震荡周期，并以5年的震荡周期为主。 （3）将东北地区1981-2010年的3-10月份的平均气温和降水量进行区划，得到东北地区的气温和降水的变化区域与土壤湿度的变化区域有很大的相似性，特别是降水量，因此降水对土壤湿度变化的贡献大于气温。整个东北地区的3-10月份的平均气温在近30年来有上升的趋势，而降水量则有下降的趋势。但北部的气温和降水量的变化幅度较小，而西部地区气温和降水量变化幅度较大。东北地区近30年内的平均气温有3-5年的变化周期，且以3年为主，降水量则有5-6年的变化周期。土壤湿度的变化与气温和降水的变化密切相关，西部和南部气温明显上升而降水明显下降，使得此地区的土壤湿度在近30年内减小的幅度较大。中部气温的升高幅度较小，而降水量基本维持不变，使这部分的土壤湿度变化不大，略微有升高的趋势。 （4）每个区域土壤湿度的显著影响因子有所不同，但都与前一旬的土壤湿度和降水量相关性很大，相关系数都通过了置信度为0.05的检验。除此之外，西南部地区与气温的相关性比较小，相关系数在0.05以下，但与日照时数的相关性则比较大。利用1981—2007年的数据资料建立各区域的预报方程，利用2008—2010年的数据对预报方程进行验证，得到各区土壤湿度的预报平均相对误差分别为7.47%、11.59%、12.34%、13.67%和12.16%，且从预报值与观测值的比较分析来看，各个区域的土壤湿度预报方程计算出的土壤湿度预测值与实际的土壤湿度观测值较为接近，基本可反应东北地区土壤湿度的实际情况。

[Sun Qianqian.The spatial-temporal variation and prediction method of soil moisture in Northeast China. Beijing: Master Dissertation of Chinese Academy of Meteorological Sciences, 2013.]

[14]
Wu Z Y, Lu G H, Wen L, et al.Reconstructing and analyzing China's fifty-nine year (1951-2009) drought history using hydrological model simulation. Hydrology and Earth System Sciences, 2011, 15(9): 2881-2894.The 1951-2009 drought history of China is reconstructed using daily soil moisture values generated by the Variable Infiltration Capacity (VIC) land surface macroscale hydrology model. VIC is applied over a grid of 10 458 points with a spatial resolution of 30 km 30 km, and is driven by observed daily maximum and minimum air temperature and precipitation from 624 long-term meteorological stations. The VIC soil moisture is used to calculate the Soil Moisture Anomaly Percentage Index (SMAPI), which can be used as a measure of the severity of agricultural drought on a global basis. We have developed a SMAPI-based drought identification procedure for practical uses in the identification of both grid point and regional drought events. As a result, a total of 325 regional drought events varying in time and strength are identified from China's nine drought study regions. These drought events can thus be assessed quantitatively at different spatial and temporal scales. The result shows that the severe drought events of 1978, 2000 and 2006 are well reconstructed, which indicates that the SMAPI is capable of identifying the onset of a drought event, its progression, as well as its termination. Spatial and temporal variations of droughts in China's nine drought study regions are studied. Our result shows that on average, up to 30% of the total area of China is prone to drought. Regionally, an upward trend in drought-affected areas has been detected in three regions (Inner Mongolia, Northeast and North) from 1951-2009. However, the decadal variability of droughts has been weak in the rest of the five regions (South, Southwest, East, Northwest, and Tibet). Xinjiang has even been showing steadily wetter since the 1950s. Two regional dry centres are discovered in China as the result of a combined analysis on the occurrence of drought events from both grid points and drought study regions. The first centre is located in the area partially covered by the North and the Northwest, which extends to the southeastern portion of Inner Mongolia and the southwest part of Northeast. The second one is found on the central to southern portion of the South. Our study demonstrates the applicability and the value of using modeled soil moisture for reconstructing drought histories, and the SMAPI is useful for analyzing drought at different spatial and temporal scales.

DOI

[15]
Jackson T J.Measuring surface soil moisture using passive microwave remote sensing. Hydrological Processes, 1993, 7(2): 139-152.

[16]
Martínez-Fernández J, González-Zamora A, Sánchez N, et al.A soil water based index as a suitable agricultural drought indicator. Journal of Hydrology, 2015, 522: 265-273.Currently, the availability of soil water databases is increasing worldwide. The presence of a growing number of long-term soil moisture networks around the world and the impressive progress of remote sensing in recent years has allowed the scientific community and, in the very next future, a diverse group of users to obtain precise and frequent soil water measurements. Therefore, it is reasonable to consider soil water observations as a potential approach for monitoring agricultural drought. In the present work, a new approach to define the soil water deficit index (SWDI) is analyzed to use a soil water series for drought monitoring. In addition, simple and accurate methods using a soil moisture series solely to obtain soil water parameters (field capacity and wilting point) needed for calculating the index are evaluated. The application of the SWDI in an agricultural area of Spain presented good results at both daily and weekly time scales when compared to two climatic water deficit indicators (average correlation coefficient, R, 0.6) and to agricultural production. The long-term minimum, the growing season minimum and the 5th percentile of the soil moisture series are good estimators (coefficient of determination, R, 0.81) for the wilting point. The minimum of the maximum value of the growing season is the best estimator (R, 0.91) for field capacity. The use of these types of tools for drought monitoring can aid the better management of agricultural lands and water resources, mainly under the current scenario of climate uncertainty.

DOI

[17]
王春乙, 娄秀荣, 王建林. 中国农业气象灾害对作物产量的影响. 自然灾害学报, 2007, 16(5): 37-43.

[Wang Chunyi, Lou Xiurong, Wang Jianlin.Influence of agricultural meteorological disaster on output of crop In China. Journal of Natural Disaster, 2007, 16(5): 37-43.]

[18]
李明星, 马柱国, 牛国跃. 中国区域土壤湿度变化的时空特征模拟研究. 科学通报, 2011, 56(16): 1288-1300.

[Li Mingxing, Ma Zhuguo, Niu Guoyue.Modeling spatial and temporal variations in soil moisture in China. Chinese Science Bulletin, 2011, 56(16): 1288-1300.]

[19]
毛德华. 定量评价人类活动对东北地区沼泽湿地植被NPP的影响. 沈阳: 中国科学院研究生院东北地理与农业生态研究所博士学位论文, 2014.植被净初级生产力(NPP)是沼泽湿地生态系统功能的重要参数， 定量区分气候变化和人类活动对NPP的影响，是全球变化背景下沼泽湿地保护、恢复与管理所面临的关键问题。东北地区是我国沼泽湿地的重要分布区，由于人类 活动扰动，沼泽湿地植被净初级生产力变化显著。本文选择中国东北地区沼泽湿地为研究对象，通过实地观测、模型模拟、遥感和GIS分析相结合的方法，开展定 量评价人类活动对沼泽湿地植被净初级生产力的影响研究。　　依托2000和2010年沼泽湿地空间分布数据，分析东北地区沼泽湿地景 观格局与动态特征;基于CASA模型基本结构式和MODIS遥感数据产品等...

[Mao Dehua.Quantitative assessment in the impacts of human activities on net primary productivity of wetlands in the Northeast China. Shenyang: Doctoral Dissertation of Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, 2014.]

[20]
李奇峰, 张海林, 陈阜. 东北农作区粮食作物种植格局变化的特征分析. 中国农业大学学报, 2008, 13(3): 74-79.从农作制分区角度,采用聚类分析法研究东北农作区种植结构及其空间分布的演变情况,利用综合比较优势法探讨种植结构变化的主要影响因素,并对主要作物的发展趋势进行判断。结果表明:1985-2005年粮食作物种植结构变化主要表现为小麦种植比重的减少和大豆种植比重的增加,种植结构整体上从多元化向专业化方向发展,各农作亚区形成了特色种植结构。主要粮食作物空间变化呈现较强的规律性,生产呈现集中趋势,形成了各自主要的生产区域。主要变化为:水稻和玉米种植区域增加,小麦种植区域大幅缩减,大豆种植范围重心北移和种植区域增加。资源、技术和市场等因素共同影响粮食作物种植结构的变化,而作物综合比较优势是种植结构变化的内在动力。

DOI

[Li Qifeng, Zhang Hailin, Chen Fu.Changes in spatial distribution and planting structure of major crops in Northeast China. Jounal of China Agricultural University, 2008, 13(3): 74-79.]

[21]
袭祝香, 杨雪艳, 刘实, 等. 东北地区夏季干旱风险评估与区划. 地理科学, 2013, 33(6): 735-740.利用东北地区逐日平均气温、降水量资料,定义了夏季干旱指数,分析了东北地区夏季干旱的时间和空间演变规律和特点,计算了东北地区夏季降水变异系数、夏季干旱风险指数和风险概率并进行了分析,定义了夏季干旱综合风险指数,并进行了综合风险分区。结果表明东北地区夏季干旱在空间分布上呈西重东轻的特点,20世纪90年代以来,东北地区夏季干旱处于前所未有的多发阶段。黑龙江西南部,吉林、辽宁两省西部为夏季干旱高或较高风险区;黑龙江中北部和东部以及吉林、辽宁两省中部为夏季干旱较低风险区;黑龙江中南部、吉林东部,辽宁东南部为夏季干旱低风险区。对于夏季干旱的高风险区和较高风险区要采取重点防御、大力推广抗旱农业生产技术、加大气候预测研究力度、加强东北地区抗旱能力建设等措施,以减轻夏季干旱损失。

[Xi Zhuxiang, Yang Xueyan, Liu Shi, et al.The risk evaluation and division of the summer drought in Northeast China. Scientia Geographica Sinica, 2013, 33(6): 735-740.]

[22]
杜国明, 张露洋, 徐新良, 等. 近50年气候驱动下东北地区玉米生产潜力时空演变分析.地理研究, 2016, 35(5): 864-874.利用GAEZ模型,综合考虑气象、土壤、地形等因素,估算1961-2010年东北玉米生产潜力,分析50年来气候变化导致的东北玉米生产潜力时空格局演变特征。研究发现:1 1961-2010年,东北玉米平均生产潜力波动较大,整体上以每10年80 kg/hm2的线性倾向率增加;2由于气候变化,20世纪末、21世纪初玉米生产潜力变化较为频繁;3玉米生产潜力总值黑龙江省始终处于最高,近50年间增长幅度黑龙江省吉林省辽宁省;4近50年来,黑龙江省玉米生产潜力的波动较为剧烈,吉林省和辽宁省相对稳定;5近50年东北玉米适宜种植区有所增加,主要集中在黑龙江省西北地区,高生产潜力区域增加明显,呈现北移趋势。研究可为东北地区高效利用气候和土地资源,优化玉米生产布局提供依据。

DOI

[Du Guoming, Zhang Luyang, Xu Xinliang, et al.Spatial-temporal characteristics of maize production potential change under the background of climate change in Northeast China over the past 50 years. Geographical Research, 2016, 35(5): 864-874.]

[23]
Maisongrande P, Duchemin B, Dedieu G.VEGETATION/SPOT: An operational mission for the Earth monitoring; presentation of new standard products. International Journal of Remote Sensing, 2004, 25(1): 9-14.The VEGETATION instrument is the starting point of a European Earth monitoring system that was developed jointly by France, the European Commission, Belgium, Italy and Sweden. Since April 1998, VEGETATION has provided a high quality global monitoring of the day-to-day land cover dynamics at 1 km resolution. The whole dataset is now available free of charge to the broad range of potential users and applications. The quality of delivered products in terms of radiometry, geometry and additional processing for directional and atmospheric effects stands VEGETATION as an excellent tool for the monitoring of surface hydrology, crops, forest and land cover. Surface reflectances that are delivered by the operational VEGETATION system are corrected for molecular and aerosol scattering, for water vapour, ozone and other gas absorption. So far, the only well known Maximum Value Composite (MVC) technique was used in the construction of 10-day synthesis. An additional enhanced composite product is now available, which evaluates the atmospheric optical depth and normalizes angular Sun-target-sensor variations. After presenting the VEGETATION instrument and products, this paper introduces the new compositing schemes ( Duchemin and Maisongrande 2002, Duchemin 2002) and presents samples of the new products.

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[24]
Aguado I, Chuvieco E, Martin P, et al.Assessment of forest fire danger conditions in southern Spain from NOAA images and meteorological indices. International Journal of Remote Sensing, 2003, 24(8): 1653-1668.Traditionally, the estimation of fire danger is performed from meteorological danger indices that are computed for single locations, where the weather stations are located. Frequently, these locations are far from forested areas, and there is a need to spatially interpolate danger variables. Methods for spatial interpolation are always prone to error, especially for those variables that show a greater spatial variability (wind, mainly). Satellite images may be considered a good alternative for interpolation of danger values, since they perform a spatially exhaustive observation of the territory. This paper analyses the spatial distribution of the Canadian Drought Code (DC), part of the Canadian Forest Fire Weather Index System (CFFWIS), in the region of Andaluc}i a (south Spain) following two procedures. First, maps of DC values were obtained from spatial interpolation of a network of 30 weather stations using the squared inverse distance algorithm. These results were compared with interpolation based on linear regression analysis, using National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) derived bands as independent variables. The most significant variables found for these empirical fittings were relative greenness, the ratio of Normalized Difference Vegetation Index (NDVI) and surface temperature, and a temporal variable, which accounts for the variations in day length throughout the fire season. After several empirical fittings were obtained, the most precise estimation was found after adjusting the coefficients to the time period considered.

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[25]
Oldford S, Leblon B, Maclean D, et al.Predicting slow-drying fire weather index fuel moisture codes with NOAA-AVHRR images in Canada's northern boreal forests. International Journal of Remote Sensing, 2006, 27(18): 3881-3902.Fire danger predicted by the Canadian Fire Weather Index, a system based on point‐source weather records, is limited spatially. NOAA‐AVHRR images were used to model two slow‐drying fuel moisture codes, the duff moisture code and the drought code of the fire weather index, in boreal forests of a 250,00002km 2 portion of northern Alberta and the southern Northwest Territories, Canada. Temporal and spatial factors affecting both codes and spectral variables (normalized difference vegetation index, surface temperature, relative greenness, and the ratio between normalized difference vegetation index and surface temperature) were identified. Models were developed on a yearly and seasonal basis. They were strongest in spring, but had a tendency to saturate. Drought code was best modelled ( R 2 02=020.34–0.75) in the spring of 1995 when data were categorized spatially by broad forest cover types. These models showed improved spatial resolution by mapping drought code at the pixel level compared to broadly interpolated weather station‐based estimates. Limitations and possible improvements of the study are also discussed.

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[26]
Caccamo G, Chisholm L A, Bradstock R A, et al.Assessing the sensitivity of MODIS to monitor drought in high biomass ecosystems. Remote Sensing of Environment, 2011, 115(10): 2626-2639.Drought monitoring is important to analyse the influence of rainfall deficiency patterns on bushfire behaviour. Remote sensing provides tools for spatially explicit monitoring of drought across large areas. The objective of this study was to assess the performance of MODIS-based reflectance spectral indices to monitor drought across forest and woodland vegetation types in the fire prone Sydney Basin Bioregion, NSW, Australia. A time series of eight spectral indices were created from 2000 to 2009 to monitor inter-annual changes in drought and were compared to the Standardized Precipitation Index (SPI), a precipitation deficit/surplus indicator. A pixel-to-weather station paired correlation approach was used to assess the relationship between SPI and the MODIS-based spectral indices at different time scales. Results show that the Normalised Difference Infrared Index and 6 (NDIIb6) provided the most suitable indicator of drought for the high biomass vegetation types considered. The NDIIb6 had the highest sensitivity to drought intensity and was highly correlated with SPI at all time scales analysed (i.e., 1, 3 and 6-month SPI) suggesting that variations in precipitation patterns have a stronger influence on vegetation water content than vegetation greenness properties. Spatial similarities were also found between patterns of NDIIb6-based drought maps and SPI values distribution. NDIIb6 outperformed the spectral index currently in use for operational drought monitoring systems in the region (Normalised Difference Vegetation Index, NDVI) and its implementation in existing drought-monitoring systems is recommended.

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[27]
Peters A J, Walter-Shea E A, Ji L, et al. Drought monitoring with NDVI-based standardized vegetation index. Photogrammetric engineering and remote sensing, 2002, 68(1): 71-75.Drought is one of the major natural hazards affecting the environment and economy of countries worldwide, Reliance on weather data alone is not sufficient to monitor areas of drought, particularly when these data can be untimely, sparse, and incomplete. Augmenting weather data with satellite images to identify the location and severity of droughts is a must for complete, up-to-date, and comprehensive coverage of current drought conditions, The objective of this research was to standardize, by time of year, the Normalized Difference Vegetation Index (NDVI) to augment drought-monitoring techniques. The Standardized Vegetation Index (SVI) describes the probability of vegetation condition deviation from "normal," based on calculations from weekly NDVI values, The study was conducted with 12 years (1989-2000) of Advanced Very High-Resolution Radiometer (AVHRR) satellite images. Z-scores of the NDVI distribution are used to estimate the probability of occurrence of the present vegetation condition at a given location relative to the possible range of vegetative vigor, historically. The SVI can be interpreted as vegetation condition based on the fact that vegetation is an efficient integrator of climatic and anthropogenic impacts in the boundary layer of the atmosphere. It thereby provides a spatially and temporally continuous short-term indicator of climatic conditions. Findings indicate that the SVI, along with other drought monitoring tools, is useful for assessing the extent and severity of drought at a spatial resolution of 1 km. The SVI is capable of providing a near-real-time indicator of vegetation condition within drought regions, and more specifically areas of varying drought conditions.

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[28]
李华朋, 张树清, 高自强, 等. MODIS 植被指数监测农业干旱的适宜性评价. 光谱学与光谱分析, 2013, 33(3): 756-761.MODIS传感器提供的短波红外光谱波段为农业干旱遥感监测带来了新的机遇, 因为它对植被水分十分敏感。 本文选择中国东北松嫩平原为研究区, 旱田为农业干旱的监测目标。 基于2001—2010年的8天合成MODIS产品（MOD09A1）, 分别计算了四种基于可见光和近红外光谱的植被绿度指数和四种基于近红外和短波红外光谱的植被水分指数, 并以多尺度标准化降水指标(SPI)为判别植被指数农业干旱敏感性的标准, 利用一种气象站点与象元配对关联的方法计算了不同植被指数与多尺度SPI的皮尔逊相关系数。 研究表明, 在农业干旱监测敏感性方面, MODIS植被水分指数（NDII6和NDII7）明显好于植被绿度指数。 其中NDII7的表现最为出色, 研究证实了MODIS短波红外光谱在监测农业干旱方面的潜力, 为今后相关研究提供了新的见解。

[Li Huapeng, Zhang Shuqing, Gao Ziqiang, et al.Evaluating the utility of MODIS vegetation index for monitoring agricultural drought. Spectroscopy and Spectral Analysis, 2013, 33(3): 756-761.]

[29]
Zhang J, Mu Q, Huang J.Assessing the remotely sensed drought severity index for agricultural drought monitoring and impact analysis in North China. Ecological Indicators, 2016, 63: 296-309.Remote sensing can provide real-time and dynamic information for terrestrial ecosystems, facilitating effective drought monitoring. A recently proposed remotely sensed Drought Severity Index (DSI), integrating both vegetation condition and evapotranspiration information, shows considerable potential for drought monitoring at the global scale. However, there has been little research on regional DSI applications, especially concerning agricultural drought. As the most important winter wheat producing region in China, North China has suffered from frequent droughts in recent years, demonstrating high demand for efficient agricultural drought monitoring and drought impact analyses. In this paper, the capability of the MODIS DSI for agricultural drought monitoring was evaluated and the drought impacts on winter wheat yield were assessed for 5 provinces in North China. First, the MODIS DSI was compared with precipitation and soil moisture at the province level to examine its capability for characterizing moisture status. Then specifically for agricultural drought monitoring, the MODIS DSI was evaluated against agricultural drought severity at the province level. The impacts of agricultural drought on winter wheat yield during the main growing season were also explored using 8-day MODIS DSI data. Overall, the MODIS DSI is generally effective for characterizing moisture conditions at the province level, with varying ability during the main winter wheat growing season and the best relationship observed in April during the jointing and booting stages. The MODIS DSI agrees well with agricultural drought severity at the province level, with better performance in rainfed-dominated than irrigation-dominated regions. Drought shows varying impacts on winter wheat yield at different stages of the main growing season, with the most significant impacts found during the heading and grain-filling stages, which could be used as the key alert period for effective agricultural drought monitoring.

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[30]
Ma M, Ren L, Singh V P, et al.Evaluation and application of the SPDI-JDI for droughts in Texas, USA. Journal of Hydrology, 2015, 521: 34-45.The lack of a system/model for integration of drought-related information has been an important obstacle in efforts for accurate and reliable drought monitoring and prediction. This study proposes an integrated multivariate standardized drought index (i.e. standardized Palmer drought index-based joint drought index, SPDI-JDI), where the Palmer scheme/model is accepted as a multivariate multi-index conceptual framework that integrates multiple drought-related indicators for characterizing drought. Using the meteorological data of ten climate divisions from Texas, the computed SPDI-JDI index is first compared to various ground observations (e.g. streamflow, lake/reservoir water level, soil moisture content and groundwater level) for the reflection of drought/wetness conditions. Moreover, the SPDI-JDI is also evaluated against Palmer drought indices and US Drought Monitor for drought detection. Results indicate that the SPDI-JDI is in good/acceptable agreements with surface and subsurface water anomalies and performs well with respect to the integrated use of Palmer drought severity index, Palmer modified drought index, Palmer hydrologic drought index and Palmer Z index as well as to the US Drought Monitor observations. Potential implications are that the SPDI-JDI allows for insights into different impacts of drought and leads to high probability of drought detection versus multi-source drought information, implying attractive properties originating from its physically inclusive and multivariate joint probabilistic combined nature.

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[31]
Ji L, Peters A J.Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices. Remote Sensing of Environment, 2003, 87(1): 85-98.The Normalized Difference Vegetation Index (NDVI) derived from the Advanced Very High Resolution Radiometer (AVHRR) has been widely used to monitor moisture-related vegetation condition. The relationship between vegetation vigor and moisture availability, however, is complex and has not been adequately studied with satellite sensor data. To better understand this relationship, an analysis was conducted on time series of monthly NDVI (1989-2000) during the growing season in the north and central U.S. Great Plains. The NDVI was correlated to the Standardized Precipitation Index (SPI), a multiple-time scale meteorological-drought index based on precipitation. The 3-month SPI was found to have the best correlation with the NDVI, indicating lag and cumulative effects of precipitation on vegetation, but the correlation between NDVI and SPI varies significantly between months. The highest correlations occurred during the middle of the growing season, and lower correlations were noted at the beginning and end of the growing season in most of the area. A regression model with seasonal dummy variables reveals that the relationship between the NDVI and SPI is significant in both grasslands and croplands, if this seasonal effect is taken into account. Spatially, the best NDVI-SPI relationship occurred in areas with low soil water-holding capacity. Our most important finding is that NDVI is an effective indicator of vegetation-moisture condition, but seasonal timing should be taken into consideration when monitoring drought with the NDVI.

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[32]
崔林丽, 史军. 中国华东及其周边地区NDVI对气温和降水的季节响应. 资源科学, 2012, 34(1): 81-90.地表植被与大气的相互作用过程是地球科学领域的研究重点和热点。本文基于SPOTVGT-NDVI数据和气象站点的气温和降水资料,采用时滞相关分析法,研究了1998年-2011年我国华东及其周边地区四季NDVI对气温和降水的时空响应特征。结果表明,在整个研究区,气温对NDVI的影响大于降水,NDVI与气温在夏季和秋季相关性较高,与降水在秋季和春季相关性较高,冬季NDVI与气温和降水相关性都最低。NDVI对气温响应的滞后期在春季和秋季较短,对降水响应的滞后期在冬季较短,夏季NDVI对气温和降水响应的滞后期都较长。在冬季、春季和秋季,NDVI对气温和降水最大相关系数的空间分布在研究区的南北部差异不明显,在夏季则具有较明显的南北差异。NDVI对气温变化响应的滞后期在春季、夏季和秋季具有较明显的南北差异,对降水变化响应的滞后期除在夏季具有一定的南北差异外,在其他季节空间分布规律性不显著。

[Cui Linli, Shi Jun.Characteristics of seasonal response of NDVI to Variations intemperature and precipitation in East China and its surrounding areas. Resources Science, 2012, 34(1): 81-90.]

[33]
严建武, 陈报章, 房世峰, 等. 植被指数对旱灾的响应研究: 以中国西南地区2009-2010年特大干旱为例. 遥感学报, 2012, 16(4): 720-737.基于中国西南地区5个省（市）2001年—2010年期间由中分辨率成像光谱仪MODIS影像资料反演得到的归一化植被指数NDVI产品数据和区内气象站点的连续观测资料,提取了研究区内各气象站点印迹区的NDVI值,计算了降水距平百分率Pa和D指数（降水量与潜在蒸散量之差）这两种气象干旱指数。依据全国植被类型图（2000年版）,对研究区内的主要植被类型在季节时间尺度上开展了这两种气象干旱指数与距平NDVI的相关性分析。研究结果表明：距平NDVI对D指数的最大响应滞后约一个月,在此尺度上表现出明显的线性相关性,所选取的6个季度的相关系数均接近或大于0.7,显著性水平小于0.01;对干旱敏感的植被类型如旱地和草地等,表现出更显著的相关性,其相关系数分别达到了0.83和0.71（平均）;在干旱季节,D指数与距平NDVI表现出较为一致的空间分异规律,而Pa指数仅在旱情比较严重的情况下或对干旱比较敏感的植被类型区与距平NDVI表现出一致性分布。

DOI

[Yan Jianwu, Chen Baozhang, Fang Shifeng, et al.The response of vegetation index to drought: Taking the extreme drought disaster between 2009 and 2010 in Southwest China as an example. Journal of Remote Sensing, 2012, 16(4): 720-737.]

[34]
卓义. 基于遥感与GIS技术的内蒙古东部草原地区干旱灾害监测、评估研究. 北京: 中国农业科学院博士学位论文, 2011.干旱灾害一直是影响草原牧区畜牧业生产最主要的气象灾害。在信息 化飞速发展的时代背景下,利用新的遥感与地理信息系统(GIS)技术深入研究草原地区干旱灾害,建立有效的草原地区干旱监测、评估体系,可为抗灾决策部门 提供更为丰富、有力的信息支持。本文以MODIS1B数据为遥感信息源,结合地面样方调查数据、长序列的气象站点数据以及社会经济统计资料,利用遥感、 GIS技术手段,通过对干旱对草原植被的影响、草原干旱灾害监测预警、草原干旱灾害评估以及草原干旱灾害风险评价与区划四个方面的研究,实现了对草原地区 干旱灾害的监测与评估。　　 利用近40年的...

[Zhuo Yi.Grassland drought disaster monitoring, evaluation research based on RS and GIS technology of eastern Inner Mongolia. Beijing: Doctoral Dissertation of Chinese Academy of Agricultural Sciences, 2011.]

[35]
Gu Y, Eric H, Brian W, et al.Evaluation of MODIS NDVI and NDWI for vegetation drought monitoring using Oklahoma Mesonet soil moisture data. Geophysical Research Letters, 2008, 35(22): 1092-1104.The evaluation of the relationship between satellite-derived vegetation indices (normalized difference vegetation index and normalized difference water index) and soil moisture improves our understanding of how these indices respond to soil moisture fluctuations. Soil moisture deficits are ultimately tied to drought stress on plants. The diverse terrain and climate of Oklahoma, the extensive soil moisture network of the Oklahoma Mesonet, and satellite-derived indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) provided an opportunity to study correlations between soil moisture and vegetation indices over the 2002-2006 growing seasons. Results showed that the correlation between both indices and the fractional water index (FWI) was highly dependent on land cover heterogeneity and soil type. Sites surrounded by relatively homogeneous vegetation cover with silt loam soils had the highest correlation between the FWI and both vegetation-related indices (r~0.73), while sites with heterogeneous vegetation cover and loam soils had the lowest correlation (r~0.22).

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[36]
王素萍, 张存杰, 宋连春, 等. 多尺度气象干旱与土壤相对湿度的关系研究. 冰川冻土, 2013, 35(4): 865-873.

[Wang Suping, Zhang Cunjie, Song Lianchun, et al.Relationship between soil relative humidity and the multiscale meteorological drought indexes. Journal of Glaciologyand Geocryology, 2013, 35(4): 865-873.]

[37]
Adegoke J O, Carleton A M.Relations between soil moisture and satellite vegetation indices in the US Corn Belt. Journal of Hydrometeorology, 2002, 3(4): 395-405.Satellite-derived vegetation indices extracted over locations representative of midwestern U.S. cropland and forest for the period 1990–94 are analyzed to determine the sensitivity of the indices to neutron probe soil moisture measurements of the Illinois Climate Network (ICN). The deseasoned (i.e., departures from multiyear mean annual cycle) soil moisture measurements are shown to be weakly correlated with the deseasoned full resolution (1 km × 1 km) normalized difference vegetation index (NDVI) and fractional vegetation cover (FVC) data over both land cover types. The association, measured by the Pearson-moment-correlation coefficient, is stronger over forest than over cropland during the growing season (April–September). The correlations improve successively when the NDVI and FVC pixel data are aggregated to 3 km × 3 km, 5 km × 5 km, and 7 km × 7 km areas. The improved correlations are partly explained by the reduction in satellite navigation errors as spatial aggregation occurs, as well as the apparent scale dependence of the NDVI–soil moisture association. Similarly, stronger relations are obtained with soil moisture data that are lagged by up to 8 weeks with respect to the vegetation indices, implying that soil moisture may be a useful predictor of warm season satellite-derived vegetation conditions. This study suggests that a “long-term” memory of several weeks is present in the near-surface hydrological characteristics, especially soil water content, of the Midwest Corn Belt. The memory is integrated into the satellite vegetation indices and may be useful for predicting crop yield estimates and surface temperature anomalies.

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[38]
Li R, Tsunekawa A, Tsubo M.Index-based assessment of agricultural drought in a semi-arid region of Inner Mongolia, China. Journal of Arid Land, 2014, 6(1): 3-15.Agricultural drought is a type of natural disaster that seriously impacts food security. Because the rela-tionships among short-term rainfall, soil moisture, and crop growth are complex, accurate identification of a drought situation is difficult. In this study, using a conceptual model based on the relationship between water deficit and crop yield reduction, we evaluated the drought process in a typical rainfed agricultural region, Hailar county in Inner Mongolia autonomous region, China. To quantify drought, we used the precipitation-based Standardized Precipita-tion Index (SPI), the soil moisture-based Crop Moisture Index (CMI), as well as the Normalized Difference Vegeta-tion Index (NDVI). Correlation analysis was conducted to examine the relationships between dekad-scale drought indices during the growing season (May-September) and final yield, according to data collection from 2000 to 2010. The results show that crop yield has positive relationships with CMI from mid-June to mid-July and with the NDVI anomaly throughout July, but no correlation with SPI. Further analysis of the relationship between the two drought indices shows that the NDVI anomaly responds to CMI with a lag of 1 dekad, particularly in July. To examine the feasibility of employing these indices for monitoring the drought process at a dekad time scale, a detailed drought assessment was carried out for selected drought years. The results confirm that the soil moisture-based vegetation indices in the late vegetative to early reproductive growth stages can be used to detect agricultural drought in the study area. Therefore, the framework of the conceptual model developed for drought monitoring can be employed to support drought mitigation in the rainfed agricultural region of Northern China.

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[39]
王文, 段莹. 2011 年长江中下游冬春连旱期土壤的湿度变化. 干旱气象, 2012, 30(3): 305-314.利用2010年7月至2011 年6月南京站逐日平均气压、日平均气温、日最高及最低气温、平均相对湿度、降水量、平均风速、日照时数资料计算了该站综合气象干旱指数,用中国气象局兰州 干旱气象研究所及南京信息工程大学干旱监测联合科学试验站2010年10月至2011年6月逐日实时降水量、及10～100 cm土壤含水量资料计算了土壤相对湿润指数,将它们与试验站各土壤层水分含量变化进行对比分析,结果表明:2010/2011年冬春季,南京地区发生了严 重的气象干旱,2010年11月5日开始出现轻旱,11月12日达到中旱,28日达重旱,此后维持在中到特旱之间;土壤相对湿度在11月13日达到中旱, 在2011年5月2日,气象连续特旱15 d后,土壤达到重旱;气象干旱与农业干旱变化趋势整体上一致,但气象干旱程度更严重,且农业干旱开始和缓解时间比气象上滞后1～3 d,干旱发展滞后5 d以上;气象及表层土壤对降水敏感性较高,而中层土壤干旱过程持续性较好;此外,干旱由表层向深层传递,当气象干旱持续时间达到50～60 d时,土壤由深层向上补充水分。

[Wang Wen, Duan Ying.Study on the soil moisture change during continuous drought in winter of 2010 and spring of 2011 in the middle and lower reaches of Yangtze River. Journal of Arid Meteorology, 2012, 30(3): 305-314.]

[40]
Wang X, Xie H, Guan H, et al.Different responses of MODIS-derived NDVI to root-zone soil moisture in semi-arid and humid regions. Journal of Hydrology, 2007, 340(1): 12-24.SummarySurface representation of the root-zone soil moisture is investigated so that feasibility of using optical remote sensing techniques to indirectly map root-zone soil moisture is assessed. Specifically, covariation of root-zone soil moisture with the normalized difference of vegetation index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) is studied at three sites (New Mexico, Arizona, and Texas) selected from the Soil Climate Analysis Network (SCAN). The three sites represent two types of vegetation (shrub and grass) and two types of climate conditions: semi-arid (New Mexico and Arizona) and humid (Texas). Collocated deseasonalized time series of soil moistures at five depths (5 cm, 10 cm, 20 cm, 50 cm, and 100 cm) and NDVI (8-day composite in 250 m resolution) during the period of February 2000 through April 2004 were used for correlation analysis. Similar analysis was also conducted for the raw time series for comparison purposes. The linear regression of both the deseasonalized time series and the raw time series was used to estimate root-zone soil moisture. Results show that (1) the deseasonalized time series results in consistent and significant correlation (0.46-0.55) between NDVI and root-zone soil moisture at the three sites; (2) vegetation (NDVI) at the humid site needs longer time (10 days) to respond to soil moisture change than that at the semi-arid sites (5 days or less); (3) the time-series of root-zone soil moisture estimated by a linear regression model based on deseasonalized time series accounts for 42-71% of the observed soil moisture variations for the three sites; and (4) in the semi-arid region, root-zone soil moisture of shrub-vegetated area can be better estimated using NDVI than that of grass-vegetated area.

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