Long-term soil moisture content estimation using satellite and climate data in agricultural area of Mongolia

The purpose of this study is to estimate long-term SMC and find its relation with soil moisture (SM) of climate station in different depths and NDVI for the growing season. The study area is located in agricultural regions in the North of Mongolia. The Pearson’s correlation methodology was used in this study. We used MODIS and SPOT satellite data and 14 years data for precipitation, temperature and SMC of 38 climate stations. The estimated SMC from this methodology were compared with SM from climate data and NDVI. The estimated SMC was compared with SM of climate stations at a 10-cm depth (r² = 0.58) and at a 50-cm depth (r² = 0.38), respectively. From the analysis, it can be seen that the previous month’s SMC affects vegetation growth of the following month, especially from May to August. The methodology can be an advantageous indicator for taking further environmental analysis in the region.     

基于影像多种特征的决策树分类方法

阐述了决策树分类CART算法原理,将纹理信息、NDVI指数引入决策树方法对影像进行分类,并将分类结果与最大似然分类结果进行比较,研究表明决策树分类方法相对传统分类方法总体精度提高了8.9148%,Kappa系数提高了0.1074。     

A novel dynamic stretching solution to eliminate saturation effect in NDVI and its application in drought monitoring

The normalized difference vegetation index(NDVI) is one of the key input variables for developing drought indices.However,the NDVI quickly saturates in high vegetation surfaces,and thus,the generalization of a drought index over different ecosystems becomes a challenge.This paper presents a novel,dynamic stretching algorithm to overcome the saturation effect in NDVI.A scaling transformation function to eliminate saturation effects when the vegetation fraction(VF) is large is proposed.Dynamic range adjustment is conducted using three coefficients,namely,the normalization factor(a),the stretching range controlling factor(m),and the stretching size controlling factor(e).The results show that the stretched NDVI(S-NDVI) is more sensitive to vegetation fraction than NDVI when the VF is large,ranging from 0.75 to 1.00.Moreover,the saturation effect in NDVI is effectively removed by using the S-NDVI.Further analysis suggests that there is a good linear correlation between the S-NDVI and the leaf area index(LAI).At the same time,the proposed S-NDVI significantly reduces or even eliminates the saturation effect over high biomass.A comparative analysis is performed between drought indices derived from NDVI and S-NDVI,respectively.In the experiment,reflectance data from the moderate resolution imaging spectroradiometer(MODIS) products and in-situ observation data from the meteorological sites at a regional scale are used.In this study,the coefficient of determination(R2) of the stretched drought index(S-DI) is above 0.5,indicating the reliability of the proposed algorithm with surface soil moisture content.Thus,the S-DI is suggested to be used as a drought index in extended regions,thus regional heterogeneity should be taken into account when applying stretching method.     

多源遥感数据的降水空间降尺度研究——以川渝地区为例

大量研究表明,通过传统地面气象站点实测的单点数据,不能有效地反映降水的空间变化特征。目前,以遥感数据获取的降水产品已得到了广泛的应用,但在地形地势复杂区域,遥感降水产品的空间分辨率与数据精度等方面仍然存在着极大的不足。因此,本文以四川重庆(川渝)地区为例,通过建立降水产品降尺度算法,以实现降水产品的降尺度估算,提高降水数据的空间分辨率。依据在不同尺度下(0.25°、0.50°、0.75°和1.00°),TRMM 3B43、地理因子,以及MOD13A3(NDVI)之间存在的相关关系,构建了多元回归模型。通过对比这4种尺度下的回归模型,选择其中精度最高的作为最终的降尺度算法,然后再把这种降尺度算法应用到1 km分辨率下进行降水估算。进一步,以区域差异分析(GDA)和区域比率分析法(GRA)对降尺度估算的降水数据进行校正,并结合部分地面气象站点实测的降水数据进行验证。验证结果表明:降尺度算法是可靠的,能有效提升降水产品的空间分辨率,同时GDA和GRA校正方法能减小误差,进一步提升降水估算的精度,满足区域地表过程应用的需求。     

通用劈窗算法的NOAA-18(N)AVHRR/3数据地表温度遥感反演与验证

本文以NOAA-18(N)AVHRR/3数据,运用通用劈窗技术获得地表温度。首先,利用MODTRAN 4模拟不同地表和大气状况下热红外通道(Ch4,10.3~11.3μm和Ch5,11.5~12.5μm)的星上亮温,并建立模拟数据库。其次,按照地表温度、大气可降水汽含量、地表比辐射率和观测天顶角,对模拟数据库分组,确定出各分组的通用劈窗算法系数。然后,将构建的地表温度反演模型应用到NOAA-18(N)AVHRR/3数据,模型所需的地表比辐射率由NDVI阈值法确定,大气可降水汽含量是利用Li等(2003)提出的一种劈窗的协方差与方差比的方法来估算。反演结果表明:在观测天顶角小于30°或者大气可降水汽含量小于3.5 g/cm2时,地表温度反演的均方根误差小于1.0K;在观测天顶角小于45°并且大气可降水汽含量小于5.5g/cm2情况下,均方根误差小于1.5K。最后,利用美国通量站的实测数据对地表温度反演结果进行了验证,结果表明均方根误差小于1.8K。     

Vegetation cover variation in the Qilian Mountains and its response to climate change in 2000–2011

An understanding 0f variati0ns in vegetati0n c0ver in resp0nse t0 climate change is critical f0r predicting and managing future terrestrial ec0system dynamics. Because scientists anticipate that m0untain ec0systems will be m0re sensitive t0 future climate change c0mpared t0 0thers, 0ur 0bjectives were t0 investigate the impacts 0f climate change 0n variati0n in vegetati0n c0ver in the Qilian M0untains （QLM）, China, between 2000 and 2011. T0 acc0mplish this, we used linear regressi0n techniques 0n 250-m MODIS N0rmalized Difference Vegetati0n Index （NDVI） datasets and mete0r0l0gical rec0rds t0 determine spati0temp0ral variability in vegetati0n c0ver and climatic fact0rs （i.e. temperature and precipitati0n）. Our results sh0wed that temperatures and precipitati0n have increased in this regi0n during 0ur study peri0d. In additi0n, we f0und that gr0wing seas0n mean NDVI was mainly distributed in the vertical z0ne fr0m 2,700 m t0 3,600 m in elevati0n. In the study regi0n, we 0bserved significant p0sitive and negative trends in vegetati0n c0ver in 26.71% and 2.27% 0f the vegetated areas. C0rrelati0n analyses indicated that rising precipitati0n fr0m May t0 August was resp0nsible f0r increased vegetati0n c0ver in areas with p0sitive trends in gr0wing seas0n mean NDVI. H0wever, there was n0 similar significant c0rrelati0n between gr0wing seas0n mean NDVI and precipitati0n in regi0ns where vegetati0n c0ver declined thr0ugh0ut 0ur study peri0d. Using spatial statistics, we f0und that veeetati0n c0ver freauentlvdeclined in areas within the 2,500-3,100 m vertical z0ne, where it has steep sl0pe, and is 0n the sunny side 0f m0untains. Here, the p0sitive influences 0f increasing precipitati0n c0uld n0t 0ffset the drier c0nditi0ns that 0ccurred thr0ugh warming trends. In c0ntrast, in higher elevati0n z0nes （3,900-4,500 m） 0n the shaded side 0f the m0untains, rising temperatures and increasing precipitati0n impr0ved c0nditi0ns f0r vegetati0n gr0wth. Increased precipitati0n als0 facilitated vegetati0n gr0wth in areas experiencing warming trends at l0wer elevati0ns （2,000-2,400 m） and 0n l0wer sl0pes where water was m0re easily c0nserved. We suggest that spatial differences in variati0n in vegetati0n as the result 0f climate change depend 0n l0cal m0isture and thermal c0nditi0ns, which are mainly c0ntr0lled by t0p0graphy （e.g. elevati0n, aspect, and sl0pe）, and 0ther fact0rs, such as l0cal hydr0l0gy.     

基于Google Earth Engine和NDVI时序差异指数的作物种植区提取

为提高农作物种植信息遥感监测的效率,扩展数据适用范围,本文提出了一种基于时间序列NDVI差异指数的作物种植区提取方法.随着海量遥感与云计算的发展,Google Earth Engine作为一个全球尺度地理空间分析云平台,弥补了单机计算耗时长的不足,为快速遥感分类带来了新机遇.基于Google Earth Engine平...     

青藏高原月NDVI时空动态变化及其对气候变化的 响应

青藏高原脆弱的高寒植被对外界干扰十分敏感,使其成为研究植被对气候变化响应的理想区域之一。青藏高原气候变化剧烈,在较短的合成时间研究气候变化对植被的影响十分必要。因此,本文利用GIMMS NDVI时间序列数据集,研究了1982-2012年青藏高原生长季月尺度植被生长的时空动态变化,探讨了其与气温、降水量和日照时数等气候因子的响应关系。结果表明：在区域尺度上,除8月外,其他各月份植被均呈增加趋势,显著增加多发生在4-7月和9月;大部分月份的NDVI增加速率随着时段的延长显著减小,表明NDVI增加趋势放缓;在像元尺度上,月NDVI显著变化的区域多呈增加趋势,但显著减少范围的扩张多快于显著增加。4月和7月植被生长主要是受气温和日照时数共同作用,6月和9月受气温的控制,而8月则主要受降水量的影响。长时间序列NDVI数据集的出现为采用嵌套时段研究植被生长变化趋势奠定了前提,而植被活动变化趋势的持续性则有助于形象表征植被活动变化过程、深入理解植被对气候变化的响应和预测植被未来生长变化趋势。由此推测,青藏高原月NDVI未来增加趋势总体上趋于缓和,但在像元尺度显著变化的区域趋于增加。     

京津冀地区植被时空动态及定量归因

作为气候变化的敏感指示器,植被的物候、生长、空间分布格局等特征及其动态变化主要取决于气候环境中的水热条件,因此在气候变化背景下,气候-植被关系成为了全球变化研究的前沿和热点问题。本文综合平均温度、降水、水汽压、湿度、日照时数、SPEI等气候因子,坡度、坡向海拔等地形因子及人为活动因子,应用地理探测器方法针对2006-2015年京津冀地区不同季节NDVI、不同地貌类型区、不同植被类型区生长季NDVI的定量归因研究,揭示了过去10年间植被时空分布格局,及植被对气候、非气候因素响应的季节差异与区域差异,以期为生态工程的建设与修复提供参考意义。趋势分析表明：①2006-2015年京津冀地区NDVI呈现增加趋势,但存在显著的空间差异,如山地生长季NDVI的增长速率大于平原、台地、丘陵等地;②基于地理探测器的定量归因结果表明,降水是年尺度上NDVI空间分布的主导因子（解释力39.4%）,土地利用与降水的交互作用对NDVI的影响最为明显（q=58.2%）;③NDVI对气候因子的响应存在季节性及区域性差异,水汽压是春季NDVI空间分布的主导因子,湿度是夏、秋两季的主导因子,土地利用是冬季的主导因子;④影响因子对生长季NDVI的解释力因不同地貌类型区、不同植被类型区而差异显著。     

不同土地覆盖类型上植被生长的最适温度

生态过程模型已成为探测陆地植被对气候变化响应的重要手段之一,最适温度作为模型模拟过程中的一个重要参数,其准确性对模型模拟结果有重要影响。本研究以2001-2010年MODIS-NDVI、2001-2010年气象台站温度数据,以及2000年土地覆盖数据,结合前人研究的成果,将最适温度定义为适宜植物生长温度上下限之间所有月均温度的均值,并从植被类型角度出发,探讨不同植物生长的最适温度,以期为生态过程模型的改进提供参数优化方案。研究表明,不同植被类型植被生长的最适温度存在较大差异。常绿针叶林、常绿阔叶林、落叶针叶林、落叶阔叶林、混交林、灌丛、草地、农田和建设用地的最适温度,依次为22.4℃、23.4℃、14.1℃、19.5℃、20.7℃、22.6℃、15.4℃、24.8℃和25.6℃。