A technique for improving MODIS standard snow products for snow cover monitoring over Eastern Turkey

Daily snow cover maps at 500 m resolution are available from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on NASA's Terra and Aqua satellites. However, information of the ground can only be obtained during clear sky conditions. In addition, the spectral similarity between clouds and snow in the visible part of the spectrum causes some ambiguities in separation of them. The dynamic behavior of clouds with their tendency to move faster often enables discrimination between cloud and snow, since snow remains relatively stationary. In this study, a daily merging methodology to combine Terra and Aqua based snow maps are proposed, and the benefits of this combination are evaluated. The methodology was tested for Eastern Turkey covering the period from December 2002 to March 2003. Merged maps derived from the standard daily snow maps from Terra (MOD10A1) and Aqua (MYD10A1) reduce the cloud cover present in any one image alone, provide better representation of the surface snow cover, and indicate better agreement with ground snow measurements than when either one is used alone. For “on the ground data date” analyses yielded 31% match ratios for MOD10A1 and MYD10A1, whereas combined images enabled 38%. One- to 2-day temporal window shifting enabled further 7% and 5% improvements in match ratios, respectively. The maximum improvement of 25% was achieved in January of 2003. The proposed methodology provides an easy and effective way to improve snow cover maps with little effort and provides fewer cloud-contaminated images for snow monitoring, for hydrology, and water resource management.     

Accuracy verification of MODIS daily cloud-free snow cover products over the Tibetan Plateau北大核心CSCD

Snow cover is of great hydrological,ecological,and climatic significance in the Tibetan Plateau. MODIS snow products are widely used at present but are seriously affected by clouds. Scholars at home and abroad have developed a variety of cloud removal products for raw MODIS daily snow products,but the accuracy of these products in the Tibetan Plateau has not been evaluated comprehensively. Therefore,this paper uses Landsat-8 data with high resolution as the reference value to conduct systematic verification of three datasets of cloud-free snow products released on a daily basis. The results show that compared with the two sets of products （M*D10A1GL06 and MODIS_Dysno_Cloudfree）,which are produced based on raw MODIS daily snow cover product realized by NSIDC（National Snow and Ice Data Center）,the MODIS CGF SCE product produced based on MODIS surface reflectance data,has a great advantage in snow identification accuracy. The MODIS CGF SCE product optimized the NDSI threshold for different land cover types. Although the accuracy of snow identification was significantly improved,the problem of large snow identification error in forest areas was still not effectively resolved,and there was a high underestimate error. © 2022 Science Press (China).     

基于MODIS数据的东北地区积雪覆盖率估算

东北地区是我国三大积雪区之一,森林覆盖面积占总面积的40%左右。受森林冠层的影响,当前的MODIS雪盖产品（V6）提供的积雪覆盖率标准模型对东北地区积雪覆盖率估算结果存在明显的低估现象。基于此,采用分区建模的方式：在森林地区,计算归一化差值林地积雪指数（NDFSI）,建立像元积雪覆盖率（FSC）与NDFSI及NDVI之间的线性关系;在非森林地区,采用MOD10A1 V6提供的归一化差值积雪指数（NDSI）,建立像元积雪覆盖率（FSC）与NDSI及NDVI之间的线性关系。采用Landsat 8 OLI数据提取的积雪覆盖率（FSC）对分区建模的估算结果与标准模型的估算结果进行对比,发现进行估算的过程中均方根误差和平均绝对误差这两项指标的数值相对于标准模型有了大幅下降,这一结果在林区有更显著的表现。计算得到的决定系数R²,在本文模型也有提高。以T1林区影像为例,本文模型的均方根误差和平均绝对误差分别为0.246、0.055,而标准模型的两项指标则分别为0.420、0.348。本文模型和标准模型的决定系数分别为0.675、0.641。     

Monitoring rangeland ground cover vegetation using multitemporal MODIS data

The aim of the present research is to monitor changes in herbage production during the grazing season in the Semirom and Brojen regions, Iran, using multitemporal Moderate Resolution Imaging Spectroradiometer (MODIS) data. At first, various preprocessing steps were applied to a topography map. The atmospheric and topographic corrections were applied using subtraction of the dark object method and the Lambert method. Image processing, including false-color composite, principal component analysis, and vegetation indices were employed to produce land use and pasture production maps. Vegetation sampling was carried out over a period of 4 months during June–September 2008, using a stratified random sampling method. Twenty random sampling points were selected, and herbage production was estimated and verified with the double-checking method. Four MODIS data sets were used in this study. The models for image processing and integrating ground data with satellite images were processed, and the resulting images were categorized into seven classes. Finally, the land covers were verified for accuracy. A postclassification analysis was carried out to verify the seven class change detections. The results confirmed that Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) maps had a close relationship with the field data. The indices produced with shortwave infrared bands had a close relationship with field data where the ground cover and yields were high. The R ² value observed was 0.85. The changes in the pasture vegetation were high during the growing season in more than 90 % of the pastures. During the growing season, most changes in the pastures belonged to class 5 and 2 in the NDVI and SAVI index maps, respectively.     

基于MODIS数据的新疆叶尔羌河流域山区积雪特征分析

科学监测新疆叶尔羌河流域山区积雪面积及其变化特征对该区域的气候研究、雪水资源开发利用、环境灾害预报和生态环境保护等方面有重要意义. 利用2000-2012年近13 a的MOD10A2积雪产品提取研究区域内积雪,结合DEM数据分析研究区内积雪面积的动态变化特征. 结果显示：新疆叶尔羌河流域山区的积雪面积的年际变化幅度较大,其中,2005年和2009年积雪面积较大,2007年则为典型少雪年;年内变化差异显著,总体上呈现“M”型的特点,12月和3月处于高位,2月和8月处于低谷. 叶尔羌河流域山区积雪覆盖率随着海拔的上升逐渐增大,稳定积雪主要分布在海拔5 000 m以上的地区;不同坡向的积雪覆盖率差异比较明显,西北坡、东坡、东北坡的积雪覆盖率比北坡、东南坡、西坡、南坡的积雪覆盖率高,西北坡高达52.8%,南坡仅为20%. 叶尔羌河流域山区的积雪面积与气温呈负相关,与降水量呈正相关,积雪面积变化对气温因素更为敏感.     

Prediction of minesite-drainage chemistry through closure using operational monitoring data

Post-mining drainage chemistry depends strongly on geochemical and hydrologic processes that occur during the operational years. Where routine operational monitoring data are abundant, the dominant processes can often be evaluated with no additional, special studies. Thus, post-mining drainage chemistry can be predicted with confidence based on the operational data. This is referred to here as ‘empirical drainage-chemistry modelling’ (EDCM).This paper presents examples of EDCM using routine monitoring databases from several minesites, each containing thousands of analyses and spanning up to 30 years of mine operation and closure. Simple statistical interpretations in the EDCM can reveal annual cycles and trends that aqueous concentrations display during operation and into closure.A new compilation of 12 EDCM equations for copper versus pH is presented to illustrate similar trends with pH among the sites. However, this compilation also shows that average-annual copper concentrations at a particular pH can vary by three orders of magnitude, reflecting site-specific factors. Also, a new case study comparing actual closure concentrations to those predicted with EDCM in 1991 demonstrates that predictive accuracy is within a factor of two.     

基于MODIS积雪产品的天山年积雪日数空间分布特征研究

山区积雪是干旱区气候变化的重要指标因子,积雪日数与积雪分布之间有着密切关系。为了研究天山山区积雪日数空间分布特征,以MODIS8d积雪产品MOD10A2（Terra）和MYD10A2（Aqua）为数据源,首先对数据进行最大化合成,获取新疆天山500m×500m分辨率的年积雪日数,然后分析了2002-2014年13a积雪日的年际变化,并结合DEM数据分析了13a天山多年平均积雪日随高程和坡度的变化特征。结果表明：天山积雪日数分布极为不均,最大年平均积雪日数为193d,13a内天山绝大部分地区年积雪日变化趋势较为稳定,稳定区约占天山总面积的83.92%;在研究时段内天山总积雪日数主要集中在30d以内,其比例约为天山总面积的48%;各个高程带积雪日面积分布差异明显,但总体上积雪日数随着高程的增加而增加;从积雪日数随坡向分布来看,北坡、东北坡、东坡、西坡、西北坡所占面积比例（>30d）相对高于其他坡向。该研究结果对干旱区水资源估算具有参考意义。     

基于MODIS的青藏高原季节性积雪去云方法可行性比较研究

青藏高原地处中纬度地区,季节性积雪分布破碎,地面观测站点稀少,中分辨率成像光谱仪（MODIS）可为该地区提供每日积雪监测数据,然而云是光学遥感最大的影响因素,为研究MODIS每日积雪产品去云方法在青藏高原的适用性,根据原理将去云方法归纳总结为五大类,并对每种方法的“潜在假设”开展分析讨论。结果显示：基于时间连续性的方法适用性强,去云效果明显,上下午积雪连续的平均概率为72.5%,而2~5d的连续积雪的概率为5.6%~43%不等,可靠性差;临近像元法可去除零散分布的云,平均正确率达到95.5%,但去除云量较少;基于高程的去云算法在山区适用性好,而在高原腹地由于坡度较小而错判概率较大;采用被动微波遥感数据进行去云则依赖于微波对云的识别率,往往误差较大;采用数学方法拟合积雪边界在积雪破碎、降雪融雪较快的青藏高原地区,物理意义较弱。通过分析研究表明,青藏高原地区MODIS日积雪产品的去云,需综合多种算法的区域适用性,充分考虑青藏高原地形及积雪本身的特征,逐步完善每日积雪去云工作。     

FY-3 A/MERS I积雪制图中NDS I指标建立及积雪判识模型研究 ——以祁连山区为例

我国新型自主的极轨气象卫星风云3号A星（简称FY-3A）上搭载的中分辨率光谱成像仪（MERSI）为大面积雪监测提供了新的遥感数据源。以中国西北祁连山区为例,分析FY-3A/MERSI传感器积雪与其它地物的图谱特征差异,建立了适用于FY-3A/MERSI的归一化差分积雪指数（NDSI）,以此为基础,构建了综合利用多光谱判别指标及土地覆盖类型（LULC）定类辅助的积雪判识模型,生成250 m分辨率的日积雪制图产品。模型通过逐步逼近的树状判别结构,去除了易和积雪混淆的部分乔木林、云、云阴影、水体、湖冰、沙（盐）地等地物,并提出应考虑积雪下覆地表特性的影响,调整设定不同LULC类型的积雪判别阈值约束,实时结合区域LULC影像进行积雪的最终判定与优化。对祁连山区2010-2011年积雪季FY-3A/MERSI影像的积雪制图应用结果表明,该资料能够客观精细地反映积雪的空间分布与动态发展过程。同时利用气象台站积雪观测记录及Terra/MODIS积雪判识结果进行对比验证,结果表明基于FY-3A/MERSI建立的积雪判识模型具有较高的精度和稳定性,特别是提高了云雪区分的效能。     

一种基于MODIS积雪产品的雪线高度提取方法

冰川雪线高度的遥感提取对冰川物质平衡研究具有重要意义。提出一种基于晴空环境下积雪覆盖频率的雪线高度提取方法。使用MOD10A1积雪产品中的像元积雪面积比例数据,提取了2000/2001-2014/2015年间高亚洲地区冰川消融期末雪线高度。使用实测的冰川年物质平衡资料和气象格网数据对提取的雪线高度变化的可信度进行分析。研究表明：近15 a高亚洲雪线高度变化及趋势具有明显的东西差异,雪线高度变化幅度自青藏高原内部地区向四周呈增加趋势,西部大于东部。提取的冰川雪线高度变化与观测的年物质平衡序列具有很高的相关性,对物质平衡波动的平均解释率可高达75%;与气象要素（气温、降水）的年际变化的相关性也较高,约61.58%的格网冰川雪线高度变化可以由夏季气温和季节降水解释。而高亚洲各分区冰川雪线高度的波动规律也与大气环流背景分布一致。因此提取的雪线高度变化具有冰川学意义,可以进一步应用于冰川物质平衡估算及模拟研究中。     

基于MODIS的青藏高原逐日无云积雪产品算法

青藏高原积雪对高亚洲地区水和能量循环起着重要的反馈和调节作用,其变化影响着融雪性河流流量,对下游水资源和经济活动具有重要影响。中分辨率成像光谱仪（MODIS）具有较高的时空分辨率,被广泛应用于积雪遥感动态监测,然而光学遥感积雪受云层影响严重,且青藏高原地区水汽分布不均,局地对流活跃,积雪的赋存时间变化快,这给高原地区逐日积雪监测及其气候学制图带来挑战。在考虑青藏高原地形和积雪分布特征情况下,结合现有的云覆盖下积雪判别算法,采用8个不同方法的组合,逐步实现MODIS逐日无云积雪算法。选取2009年10月1日-2011年4月30日两个积雪季为研究期,并采用145个地面台站观测雪深数据对去云算法各步骤过程开展精度验证,结果表明：当积雪深度>3 cm时,逐日无云积雪产品总分类精度达到96.6%,积雪分类精度达83%,积雪判对概率（召回率）达到89.0%,算法可实现青藏高原地区逐日无云积雪动态监测和积雪覆盖气候学数据重建,对高亚洲地区的水、生态和灾害等全球环境变化影响研究具有重要的意义。     

基于光谱-环境随机森林回归模型的MODIS积雪面积比例反演研究

积雪面积比例（Fractional Snow Cover, FSC）数据能在亚像元尺度上定量的描述像元内积雪覆盖的程度,相比二值积雪面积数据可以更加精确地估计积雪覆盖的面积。基于机器学习的随机森林回归模型可以表示高维的非线性关系,可显著提高MODIS FSC的反演精度。采用随机森林回归模型结合光谱、环境信息构建了一个新的回归模型——光谱-环境随机森林回归（Spectral Environment Random Forest Regressor, SE-RFR）模型,用于MODIS数据反演中国区域的FSC。利用中国典型积雪区内由Landsat 8地表反射率数据获取的FSC数据作为参考值,对SE-RFR模型的反演精度进行评估。研究表明,利用“SE-RFR”获取的FSC数据RMSE、MAE分别为0.160、0.104,精度较高。此外,根据SE-RFR模型与未加入环境信息的随机森林回归（S-RFR）模型比较结果可知,加入环境信息的随机森林回归模型提高了FSC反演的精度,特别是在受环境信息影响较大的青藏高原地区,RMSE从0.200降低到0.181。最后,将SE-RFR模型与目前使用广泛的MODIS FSC反演模型FSC_NDSI、MODSCAG和SSEmod进行了比较,结果表明SE-RFR模型的RMSE与FSC_NDSI、MODSCAG和SSEmod模型的RMSE相比,平均RMSE分别提高了12.0%、8.3%和5.5%。总体来说,SE-RFR模型可以准确地提取MODIS FSC,对于区域乃至全球FSC产品制备具有广泛的应用前景。     

Estimation of snow cover distribution in Beas basin,Indian Himalaya using satellite data and ground measurements

In the present paper, a methodology has been developed for the mapping of snow cover in Beas basin, Indian Himalaya using AWiFS (IRS-P6) satellite data. The complexities in the mapping of snow cover in the study area are snow under vegetation, contaminated snow and patchy snow. To overcome these problems, field measurements using spectroradiometer were carried out and reflectance/snow indices trend were studied. By evaluation and validation of different topographic correction models, it was observed that, the normalized difference snow index (NDSI) values remain constant with the variations in slope and aspect and thus NDSI can take care of topography effects. Different snow cover mapping methods using snow indices are compared to find the suitable mapping technique. The proposed methodology for snow cover mapping uses the NDSI (estimated using planetary reflectance), NIR band reflectance and forest/vegetation cover information. The satellite estimated snow or non-snow pixel information using proposed methodology was validated with the snow cover information collected at three observatory locations and it was found that the algorithm classify all the sample points correctly, once that pixel is cloud free. The snow cover distribution was estimated using one year (2004–05) cloud free satellite data and good correlation was observed between increase/decrease areal extent of seasonal snow cover and ground observed fresh snowfall and standing snow data.     

NOAA IMS雪冰产品在青藏高原积雪监测中的适用性分析

在系统评估青藏高原积雪观测典型气象站历史定位坐标精度基础上,利用站点雪深资料对NOAA IMS 4 km和1 km分辨率雪冰产品在青藏高原的精度和适用性进行了验证和评估,定量分析了IMS 4 km到1 km空间分辨率提高和气象站历史定位与GPS定位坐标之间的差异对青藏高原IMS积雪监测精度的影响。结果表明：青藏高原个别气象站历史坐标与当前GPS接收机定位之间存在较大的差异,如安多气象站经度偏小0.6°,纬度偏大0.08°。IMS 4 km雪冰产品在青藏高原的总精度介于76.4%~83.2%,平均为80.1%,积雪分类精度介于35.8%~60.7%,平均为47.2%,平均误判率为17.1%,平均漏判率为45.5%,总体上呈现地面观测的积雪日数越多、平均雪深越大,其总体监测精度越低,而积雪分类精度越高的特点。IMS分辨率从4 km到1 km总体精度平均提高了2.9%,积雪分类精度平均提高了0.9%,主要是由于个别站点的精度提升较大引起的,对高原多数台站积雪监测精度的改进和提升很小。除个别台站外,目前气象站历史坐标和GPS定位坐标之间的差异,对IMS 4 km积雪监测精度验证结果没有影响。然而,今后随着卫星遥感技术的发展,更高时空分辨率的遥感积雪产品将用于积雪监测和研究,精确的地面观测站坐标信息是对这些遥感数据开展精度验证与实际应用的前提。     

Dynamic monitoring of drought using HJ-1 and MODIS time series data in northern China

From early November 2008 to February 2009, lack of rainfall led to severe drought in northern China. More than 9.3 million ha of wheat in six major crop production provinces, including Henan, Anhui, Shandong, Shanxi, Gansu, and Shaanxi, were hit by drought. Supported by Chinese HJ-1 satellite images together with NASA Moderate Resolution Imaging Spectroradiometer (MODIS) data, dynamic monitoring of the drought was conducted. HJ-1 CCD data with 30-m resolution were used to identify cropland information. Spatial–temporal variation of drought was detected using Vegetation Index and Water Index time series data derived from MODIS visible, infrared, and short-wave infrared bands. The influences of drought were classified into five levels based on MODIS-derived 8-day composite Anomaly Water Index (AWI) and field survey data. The results indicated that the drought deteriorated beginning in November 2008 and became most serious in late January 2009. HJ-1 data together with MODIS data proved to be valuable data sources for monitoring soil moisture and drought at a both regional and national scale.     

基于MODIS数据的西藏干旱自动监测系统实现的关键技术研究

干旱是一种危害较大的自然灾害, 影响范围大、持续时间长. 随着经济发展和人口的增加, 水资源短缺现象日益严重, 这也导致了干旱范围的扩大和干旱化程度加重. 为降低业务人员负担, 提高业务工作效率, 探讨了基于MODIS遥感数据的西藏干旱监测系统实现的关键技术. 首先, 阐述了MODIS 轨道数据快速投影处理的正向投影处理技术原理, 然后分析了温度植被干旱指数算法(TVDI)及其实现, 最后, 将干旱监测方法集成到业务系统软件, 最终实现西藏全区的干旱遥感监测业务工作. 通过与传统手工干旱监测方法进行对比, 发现干旱自动监测系统具有速度快、自动化等优点, 具有十分重要的实践意义.     

基于ETM+的积雪提取方法研究——以新疆玛纳斯河流域为例

为了高精度地提取积雪信息, 消除森林覆盖区以及结冰河流水体对于积雪信息提取的影响, 以Landsat ETM+为数据源, 分析了玛纳斯河流域积雪信息提取的归一化差值积雪指数阈值, 并建立积雪信息提取方法. 对研究区进行了积雪信息提取, 并与国际数据服务平台所得的积雪信息提取结果进行了比较. 结果表明: 基于Landsat数据利用归一化差值积雪指数提取积雪信息时, 其合理阈值应为0.37; 通过总体精度以及Kappa相关系数在结冰水体区域以及森林覆盖区域的提取结果进行对比, 认定所使用的提取方法更加准确可靠.     

Monitoring and evaluation of seasonal snow cover in Kashmir valley using remote sensing,GIS and ancillary data

Seasonal snow cover is a vital natural resource in the Himalaya. Monitoring of the areal extent of seasonal snow cover is important for both climatological studies as well as hydrological applications. In the present paper, snow cover monitoring was carried out to evaluate the region-wise accumulation and ablation pattern of snow cover in Pir Panjal and Shamshawari ranges of Kashmir valley. The study was carried out for the winter period between November and April of 2004–05, 2005–06 and 2006–07, using multi-temporal WiFS sensor data of IRS-1C/1D satellites. The study shows reduction in the areal extent of seasonal snow cover and rising trend of maximum temperature in three winters for the entire Kashmir valley. This has been validated with 20 years (1988–89 to 2007–08) climatic conditions prevailed in both ranges of Kashmir valley. Region-wise study shows the spatial and temporal variability in seasonal snow cover within Kashmir valley. Advance melting was observed in Banihal and Naugam/Tangdhar regions than Gurez and Machhal regions. Different geographical parameters of these regions were studied to evaluate the influence on snow cover and it was observed that altitude and position of region with respect to mountain range are the deciding factors for retaining the seasonal snow cover for longer duration. Such region-wise study of snow cover monitoring, can provide vital inputs for planning the hydropower projects, development in habitat areas, recreational and strategic planning in the region.     

基于MODIS数据的北疆积雪黑碳和雪粒径反演及时空变化分析

积雪是地球上反射率较高的自然表面,对于中高纬度地区的水文和能量收支平衡发挥着重要作用。表层积雪中的黑碳和雪粒径变化可以显著影响积雪反照率,造成积雪对太阳辐射吸收的变化,进而对区域气候变化和水文循环产生反馈作用。利用遥感技术对季节性积雪表层黑碳和雪粒径进行定量评估,可以获取时空上连续系统的雪表黑碳浓度和雪粒径变化情况,这也是许多气候和水文模型的输入因子。以中国主要季节性积雪区北疆为研究区,基于MODIS（Moderate Resolution Imaging Spectroradiometer）数据的3（0.47 μm）、2（0.86 μm）和5（1.24 μm）波段,采用SGSP（Snow Grain Size and Pollution Amount）算法反演2000-2018年积雪期的雪表黑碳浓度和雪粒径,并结合地面观测数据对于反演结果进行了精度验证,综合分析北疆雪表黑碳浓度和雪粒径时空变化趋势。结果显示,SGSP算法能够同时反演雪表黑碳浓度和雪粒径,并且验证结果表明纯雪像元上反演结果具有较好的精度;2000-2018年北疆雪表年均黑碳浓度和年均雪粒径都随时间变化呈现微弱下降趋势;受地理位置和局部污染源的影响,北疆积雪黑碳浓度空间分布复杂,天山北坡经济带平均黑碳浓度最高,伊犁地区平均黑碳浓度最低,雪粒径的空间分布显示塔城地区平均雪粒径最大,伊犁地区最小。