@Article{Yaqian2016,
author="Yaqian, He
and Yanchen, Bo
and Leilei, Chai
and Xiaolong, Liu
and Aihua, Li",
title="Linking in situ LAI and fine resolution remote sensing data to map reference LAI over cropland and grassland using geostatistical regression method",
journal="International Journal of Applied Earth Observations and Geoinformation",
year="2016",
volume="50",
keywords="Leaf area index;Up-scaling;Geostatistical regression;Reduced major axis;Vegetation index",
abstract="Highlights{\textbullet}A geostatistical regression (GR) method for leaf area index up-scaling is proposed.{\textbullet}Performance of GR method is better than reduced major axis method.{\textbullet}Performance of GR method varies among vegetation indices and land cover types.;AbstractLeaf Area Index (LAI) is an important parameter of vegetation structure. A number of moderate resolution LAI products have been produced in urgent need of large scale vegetation monitoring. High resolution LAI reference maps are necessary to validate these LAI products. This study used a geostatistical regression (GR) method to estimate LAI reference maps by linking in situ LAI and Landsat TM/ETM+ and SPOT-HRV data over two cropland and two grassland sites. To explore the discrepancies of employing different vegetation indices (VIs) on estimating LAI reference maps, this study established the GR models for different VIs, including difference vegetation index (DVI), normalized difference vegetation index (NDVI), and ratio vegetation index (RVI). To further assess th...",
issn="0303-2434"
}

@Article{Wang2012,
author="Wang, Ying
and Xue, Yong
and Li, Yingjie
and Guang, Jie
and Mei, Linlu
and Xu, Hui
and Ai, Jianwen",
title="Prior knowledge-supported aerosol optical depth retrieval over land surfaces at 500 m spatial resolution with MODIS data",
journal="International Journal of Remote Sensing",
year="2012",
volume="33",
number="3",
abstract="Aerosol optical depth (AOD) values at a spatial resolution of 500 m were retrieved over terrain areas by applying a time series of Moderate resolution Imaging Spectroradiometer (MODIS) 500 m resolution data in the Heihe region (36--42{\textdegree} N, 97--104{\textdegree} E) of Gansu Province, China; in the Pearl River Delta (18--30{\textdegree} N, 108--122{\textdegree} E), China; and in Beijing (39--41{\textdegree} N, 115--118{\textdegree} E), China. A novel prior knowledge scheme was used in the algorithm that performs cloud screening, simultaneous AOD and surface reflectance retrieval from the MODIS 500 m Level 1B data. This prior knowledge scheme produced a new {\AA}ngstr{\"o}m exponent $\alpha$, utilizing a Terra pass time $\alpha$ and an Aqua pass time $\alpha$ to better satisfy the invariant $\alpha$ assumption. The retrieved AOD data were compared with AOD data observed with the ground-based, automatic Sun-tracking photometer CE318 at corresponding bands in the Heihe region and with Aerosol Robotic Network (AERONET) data in the Pearl River Delta and in Beijing. Validation experiments demonstrated the potential of ...",
issn="0143-1161"
}

@Article{刘艳2010,
author="{刘艳}
and {王锦地}
and {周红敏}
and {薛华柱}",
title="黑河中游试验区不同分辨率LAI数据处理、分析和尺度转换",
journal="遥感技术与应用",
year="2010",
volume="25",
number="06",
pages="805--813",
keywords="叶面积指数(LAI);泰勒级数展开模型;尺度转换",
abstract="2008年开展的黑河综合遥感联合试验获取了大量野外实测叶面积指数(LAI)数据以及遥感LAI产品。在利用LAI地面点观测数据对遥感影像进行验证或者不同分辨率遥感产品相互比较的过程中存在由于地表异质性引起的尺度效应,导致无法直接进行验证、比较,需要进行尺度转换。以基于泰勒级数展开的尺度转换模型为基础,研究不同源LAI之间的尺度转换方法。包括两部分内容:①以高分辨率影像为辅助数据将地面实测点尺度的LAI转换到中、低分辨率遥感像元尺度;②利用高分辨率影像作为亚像元信息对低分辨率LAI产品进行尺度纠正。结果表明,利用泰勒级数展开模型进行尺度转换是一种简单可行的方法,经尺度转换的地面实测点尺度LAI可用作像元尺度数据比较验证的参考。",
issn="1004-0323"
}

@Article{陈玲2009,
author="{陈玲}
and {阎广建}
and {李静}
and {余莹洁}",
title="行播作物地面方向性测量的视场不确定性分析",
journal="地球科学进展",
year="2009",
volume="24",
number="07",
pages="793--802",
keywords="行结构;视场不确定性分析模型;冠层BRF;垄周期;误差",
abstract="行播作物以其独特的几何结构介于离散与连续植被之间。地面测量此类地物的双向反射系数(Bidirectional Reflectance Factor,BRF)特征,不可回避视场变化所引起的不确定性问题。在Kimes垄行结构模型中加入等效视场的概念,对视场进行分解,从而建立了一个行结构多角度地面测量的视场不确定性分析模型,为定量分析视场变化所引起的BRF测量误差提供了可能。利用该模型较为全面地模拟分析了视场变化对视场内四组分比例及冠层BRF的影响。结果表明:①BRF误差基本独立于植被---土壤光谱对比度。②误差与观测天顶角之间的关系复杂,前向观测表现得尤为明显。③垂直观测视场满1个垄周期后,四组分比例及冠层BRF的误差可保持较小且稳定的状态;满2个垄周期,误差达到局部最小值(局部指垂直视场含2.5个垄周期以下,不排除视场更大,误差更小的可能性)。④垂直视场若仅含0.5个垄周期,BRF误差最大值一般可高达67.8{\%},最小值亦可达38.7{\%};满1个垄周期后,BRF误差极大值降至20{\%}以下,极小值可控制在6{\%}以内。其中视场为1个整周期,误差范围为6{\%}{\~}12{\%};2个整周期,误差范围为0.6{\%}{\~}3.9{\%}。⑤垂直视场大小为1{\~}2个垄周期之间的非整周期,四组分比例及冠层BRF误差总体上均稍高于1个整周期,故建议在实际测量过程中,测量高度若无法满足垂直视场为2个垄周期,可优先考虑1个整周期的情况。还通过非线性最优化函数将2个模型分别与黑河实验玉米地方向性观测实测数据进行拟合,得出的结果与模拟分析的结论一致,即在垂直视场内包含2个垄周期以上的生长初期,方向性测量无需考虑视场效应;若垂直视场内不足一个垄周期(生长中期),则有必要考虑视场的不确定性。",
issn="1001-8166"
}

@Article{康国婷2009,
author="{康国婷}
and {阎广建}
and {任华忠}
and {王颢星}
and {钱永刚}",
title="田块尺度作物辐射温度获取方法对比研究",
journal="地球科学进展",
year="2009",
volume="24",
number="07",
pages="784--792",
keywords="田块尺度;辐射温度;热像仪;手持式辐射计;采样方式",
abstract="热像仪的优势在于可以获得组分辐射温度,常用于地面温度同步测量实验中,其数据与植被覆盖度联合还可以得到作物田块尺度的平均辐射温度。以黑河流域进行的星---机---地遥感综合观测试验加密观测------盈科绿洲玉米地的热像仪和手持式红外辐射计温度测量数据为基础对不同采样方式获得的地面辐射温度进行对比。对于热像仪数据:①采用阈值法对热像仪影像中的玉米和土壤背景两组分进行分离,获得了各自辐射温度的平均值;②利用LAB彩色变换法处理同步拍摄的真彩色照片,获得每块玉米样地的植被覆盖度;③最终结合组分温度和植被覆盖度求得地块平均的辐射温度。经过实验对比发现,由热红外图像计算获得的地面平均辐射温度与手持式红外辐射计垂直垄或顺垄条带采样获得的地面平均辐射温度差值较小,基本在{\textpm}1{\textcelcius}以内,而3种测量方式的最大值、最小值相差较多。还模拟了几种常见尺度下利用手持式红外辐射计进行随机采样时,其采样平均值以不同的置信度处于真实温度{\textpm}0.5{\textcelcius}之间所对应的采样次数。分析表明,基于点测量的采样方案难以仅利用1{\~}2台手持式红外辐射计实现对田块或更大尺度平均辐射温度的准确测量,高时空采样频率是保障地面辐射温度测量精度的前提,与遥感像元尺度相匹配的地面真实性检验需要进行测量方法和设备的革新。",
issn="1001-8166"
}