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中国农业干旱的多源遥感监测

Agricultural Drought Monitoring Using Multi-source Remote Sensing Data in China

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【Author in Chinese】 Tehseen Javed

【Supervisor】 李毅

【Author's Information】 西北农林科技大学, 水利工程, 2020, 博士

【Abstract in Chinese】 干旱是一种复杂且不为人所知的潜在自然灾害。一直以来,中国频繁遭受干旱事件,对可持续作物的生产带来潜在危害。为了监测干旱,需要大量的历史气象、土壤和农业数据等。研究农业干旱监测的时空变化特征对抗旱和农业种植规划有着重要意义。因此,为了研究干旱,需要一种自动化且高效的方法在庞大的数据集中提取出有价值的信息。本研究采用多个基于遥感产品的数据集和实测气象站点数据,包括中国气象局1961-2017年552个气象站的日降水量和温度数据集,卫星网格化月降水数据、土地覆盖数据、归一化差异植被指数(NDVI)数据和土壤湿度数据等,通过计算和分析标准化降水(SPI)、标准化降水蒸散发指数(SPEI)、降水距平、植被状况指数(VCI)、NDVI距平、增强型植被指数(EVI)、标准化土壤水分指数(SSI)、多变量标准化干旱指数(MSDI)和植被健康指数(VHI),基于皮尔逊相关系数(R)、线性回归、决定系数(R~2)、均方根误差(RMSE)和改进的Mann-Kendall检验(MMK检验)等方法评估了中国不同地区干旱事件的发生规律。本论文包括三个部分。第一部分首先用MMK趋势检验方法分析了中国四种不同土地覆盖类型(即耕地、林地、草地和沙漠)中干旱的时空分布特征,并基于皮尔逊相关系数和决定系数确定NDVI距平、VCI、降水和SPI的关系。第二部分主要研究了干旱或湿润条件对中国不同分区(西北地区NW、华北地区NC、青藏地区QTA和华南地区SC)植被物候期和生产力的影响。逐日降水数据主要用来分析降水的变化趋势,并计算SPEI,并利用中分辨率成像光谱辐射计(MODIS)的遥感数据EVI对植被物候其进行评价。第三部分主要研究了干旱指数(SPI、SSI、MSDI和VHI)对农业干旱预测评估的性能。论文得出的主要结论如下:(1)平均月降水量和年降水量依不同土壤覆盖类型的排序为林地>草地≈农田>荒漠。不同覆盖类型土地下干旱指数(NDVI距平、VCI和SPI)都与降水量呈正相关。NDVI距平和VCI与耕地3个月时间尺度SPI的相关性较好,与林地6个月时间尺度SPI的相关性较好。VCI与SPI的相关性较NDVI距平与SPI的相关性好,计算得出的沙漠和草地地区下最干旱年份(2011年)和最湿润年份(2016年)VCI与降水的R~2的R~2值为0.70~0.90,耕地和林地的R~2值为0.54~0.69。降水量、SPI和VCI有显著的增长趋势。另外,降水、NDVI和VCI的空间分布模式随海拔的降低而增加总体上,沙漠和草地经常受到中度或极端干旱条件的影响,而且沙漠地区和划地对短期的干旱更敏感。(2)不同地区气温都有显著升高的趋势(p<0.05)。中国西北地区降水呈现不显著增加趋势,华北、青海、西藏地区和华南地区降水量呈不显著下降趋势。综合增强植被指数(IEVI)和SPEI的变化表明,2011年2016年是2000年到2017年间的极端干、湿年。2011年2016年期间,植被物候和生产力发生了快速的变化。2011年,植被物候随生长季节天数(ΔLGS)变化为-14±36天。在2016年,在生长季总净效应发生了变化,ΔLGS值为34±71天。气候敏感性从干旱到半干旱地区变化率为0.16,从半湿润区到湿润区的变化率为-0.04。与i EVI和降水相比,i EVI和SPEI具有较高的相关性。(3)相对土壤含水量和VHI随降水量变化较小。与SPI和SSI相比,MSDI与VHI关系稍好。西北地区1个月尺度SPI、SSI和MSDI与月VHI的相关系数r值分别是0.15、0.17和0.21。华北地区3个月尺度SPI、SSI和MSDI与月VHI的相关性最高,其次是华南地区、青藏高原地区和西北地区,r值分别为0.72、0.68、0.63和0.57。然而6个月时间尺度SPI、SSI和MSDI与月VHI相关性中,华南地区r值最大为0.58,其次是青藏地区、华北地区和西北地区,r值分别为0.54、0.45和0.41。西北地区、青藏地区和华南地区的VHI呈显著增长趋势,MMK检验的统计量Z值分别为2.26、4.09和4.70。华北地区增长趋势不明显。(4)总的来说,a SPI的三个时间尺度(1个月、3个月和6个月)表明,极端干旱事件发生在近10年期间,而极端干旱事件更频繁地发生在冬小麦生长期。在我国西北、华北小麦生长季,4月份极端干旱事件频发,有3个时间尺度a SPI。而在青藏地区和华南地区,最常见的极端干旱事件分别出现在12月和5月。另一方面,在我国西北和华南玉米生长季,极端干旱事件发生频率最高的是7月份,而华北和青藏地区则分别是8月份和9月份。在中国四个亚区,3个月的a SPI与VISW或作物产量异常(YAI)有较高的相关性。总体来看,夏玉米产量呈显著增长趋势,而华北东北部小麦产量呈显著增长趋势。

【Abstract】 Drought is an insidious hazard of nature,which is considered by many to be the most complex but least,understood of all-natural hazards.For monitoring of drought,large historical datasets are required,which involves complex inter-relationship between the climatological and meteorological data.The extraction of valuable information from such extensive data archives demands an automated and efficient way.Data mining is the answer to the above problem as it has the potential to search for hidden patterns and identify the relationship between the data.China has also been suffered from frequent droughts events,which has brought potential hazards to sustainable crop production.Therefore,the study of spatial-temporal variation characteristics of agricultural drought monitoring plays a vital role in drought relief and agricultural planning.The study analyzes the relationship between the metrological and agricultural drought,the impact of drought on vegetation phenology,and productivity and investigates the drought indices performances for prediction of agriculture drought.Multiple datasets were used in this study,daily precipitation and temperature datasets from 763 stations,between1961-2017 china meteorological bureau,satellite gridded monthly precipitation,land cover,thermal bands,normalized difference vegetation index(NDVI),and soil moisture.For assessment of drought events and there correlation the following drought indices were calculated,standardized precipitation index(SPI),standardized precipitation-evapotranspiration index(SPEI),precipitation anomaly,vegetation condition index(VCI),NDVI anomaly,enhanced vegetation index(EVI),standardized soil moisture index(SSI),multivariate standardized drought index(MSDI),and vegetation health index(VHI).To find the correction and trends of these drought indices,the following statistical analysis was performed;Pearson correlation coefficient(r),linear regression,coefficient of determination(R2),and root mean square error(RMSE)and modified Mann-Kendall(MMK).The study was divided into four phases.In the first phase of the study,drought was assessed under four different land cover types,cropland,forestland,grassland,and desert-land in China.The modified Mann-Kendall test was used to detect the significance of a trend.The Pearson correlation and coefficient of determination methods were used to find the relationship between NDVI anomaly,VCI,precipitation,and SPI.In the second phase,investigate the impacts of drought or wet conditions on the vegetation phenology and productivity across the different sub-regions(northwest(NW),north China(NC),QinghaiTibet area(QTA),and south China(SC).Daily rain gauge datasets were used to predict the air temperature,precipitation trend,and to compute the standardized precipitationevapotranspiration index(SPEI).Remote sensing-based EVI data from moderate resolution imaging spectroradiometer(MODIS)were used to evaluate the vegetation phenology.In the third phase,investigate the drought indices(SPI,SSI,MSDI,and VHI)performances for prediction of agriculture drought.In fourth phase using globally remote sensing(RS)based gridded monthly precipitation,Climate Hazards Group Infra-Red Precipitation and Station(CHIRPS),normalized difference vegetation index(NDVI)and land surface temperature(LST)datasets over 1982-2018 were utilized to derive agricultural Standardized Precipitation Index(a SPI),and Vegetation Supply Water Index(VSWI).The main findings of the study are the following:(1)The mean monthly and yearly precipitation had a general land cover type rank of forestland > grassland ≈ cropland > desert-land.A positive correlation was found between drought indices(NDVI anomaly,VCI,SPI)and precipitation for different land cover types.The NDVI anomaly and VCI were well correlated with 3-month SPI for cropland and were well correlated with 6-month SPI for forestland.VCI performed better than NDVI anomaly when correlating with SPI.The coefficient of determination(R2),were obtained for precipitation and VCI in the driest(2011)and wettest(2016)years.The R2 values for desert and grassland ranged from(0.70-0.90),and cropland and forestland were lower(0.54-0.69).Only precipitation,SPI,and VCI of cropland had significantly increasing trends.The spatial distribution patterns of precipitation,NDVI,and VCI increased with decreased elevation.The study revealed that desert and grassland had been regularly exposed to moderate or extreme droughts conditions and confirmed that desert and grassland are more sensitive to short-term drought.(2)The air temperature had significant increasing trends(p < 0.05)in all subregions.Precipitation showed a non-significant increasing trend in northwest China and insignificant decreasing trends in north China,Qinghai Tibet area,and south China.Integrated enhanced vegetation index(i EVI)and SPEI variations depicted that 2011 and 2016 were the extremely dry and wet years over 2000-2017.Rapid changes were observed in the vegetation phenology and productivity between 2011 and 2016.In 2011,changes in the vegetation phenology with the length of the growing season(ΔLGS)= was-14 ±36 days.In 2016,the overall net effect changed at the onset and end of the growing season with ΔLGS of 34±71days.The climatic sensitivity had a changing rate of 0.16 from arid to semi-arid regions and declined from semi-humid to humid regions with a decreasing rate of-0.04.A higher association was observed between i EVI and SPEI as compared to i EVI and precipitation.North China is more sensitive to climatic variation.(3)The relative soil moisture and VHI depicted similar patterns,while slight variations with precipitation.The MSDI performed well against VHI,compared to SPI and SSI.The correlation among the 1-month SPI,SSI,and MSDI with monthly VHI the maximum r-value(0.21)were obtained in northwest China with the r-values of 0.15,0.17,and 0.21,respectively.The correlation among the 3-month SPI,SSI,and MSDI with monthly VHI,the highest value was obtained in north China,followed by South China,Qinghai-Tibet area,and northwest China,with the r-values of 0.72,0.68,0.63,and 0.57 respectively.While correlation among the 6-month SPI,SSI,and MSDI with monthly VHI,maximum r(0.58)value obtained in south China,followed by Qinghai-Tibet area,north China,and northwest China(r = 0.54,0.45,0.41 respectively).The VHI shows a significant increasing trend for northwest,Qinghai-Tibet area,and south China with Mann-Kendall Z values of 2.26,4.09,and 4.70,respectively,and the insignificant increasing trend in north China.(4)Overall the three timescales(1-,3,and 6-months)of a SPI show that extreme drought events occurred in the 21 st century,and the more frequent extreme drought events occurred in the winter wheat growing season.In the wheat-growing season northwest and north China,the much frequency of extreme drought events occurred in April for three timescales of a SPI.While in Qinghai-Tibet area and south China the most frequently extreme drought events found in December and May,respectively.On the other hand,during the corn growing season in the northwest and south China,the most frequency of extreme drought events occurred in July,while in north China and Qinghai-Tibet area,August and September respectively.A higher correlation was obtained for the pair of 3-month a SPI and VISW or crop yield anomaly(YAI)in four sub-regions of China.Overall the summer corn yield shows the significant increasing trends,while wheat yield in the northeast of north China illustrations significantly increasing trends.

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