基于森林模型参数先验知识估算高分辨率叶面积指数

目前,估算高分辨率叶面积指数LAI（Leaf Area Index）的常用方法是采用大量地面测量数据和遥感数据建立统计模型,再用统计模型估算LAI。然而,与农田地面测量实验相比,森林地面测量实验获取的观测数据更加有限,这使得基于统计模型的森林高分辨率LAI的估算精度低,难以满足应用需求。为此,本文提出一种基于森林模型参数先验知识、使用森林研究区少量的LAI地面测量数据和归一化植被指数NDVI数据估算森林高分辨率LAI的方法。首先,获取全球20个森林实验区的LAI地面测量数据和NDVI数据,建立LAI-NDVI统计模型并提取森林模型参数的先验知识。然后,以一个新的森林站点Concepción作为研究区,将该研究区的数据分为建模数据和验证数据两个部分。使用研究区有限的建模数据对森林模型参数先验知识进行本地化校正得到优化模型,优化模型用于估算森林高分辨率LAI,使用验证数据评价LAI的估算精度。同时,选取了Camerons站点、Gnangara站点、Hirsikangas站点评价本文方法的LAI估算精度。使用地面测量LAI验证基于森林模型参数先验知识估算高分辨率LAI的结果精度,经验证4个森林站点的均方根误差分别为0.6680,0.4449,0.2863,0.5755。研究结果表明：在仅有少量观测数据时,采用本方法能有效地提高森林高分辨率LAI的估算精度。因此,本方法可为森林高分辨率LAI的遥感估算提供参考。     

中国区域MuSyQ叶面积指数产品验证与分析

对多源遥感数据协同生产的2010年—2015年中国区域1 km空间分辨率5天合成的MuSyQ（Multi-source data Synergized Quantitative remote sensing production system）叶面积指数LAI产品进行验证。参考现有的LAI产品（MODIS c5,GLASS LAI）和中国生态系统研究网络部分农田和森林站点可用的LAI地面测量数据,从时空连续性、时空一致性、精度和准确性等方面对中国区域的MuSyQ LAI产品进行定性和定量分析与评价。结果表明：（1） MuSyQ LAI产品在保证精度优于MODIS产品的情况下,时间分辨率和时空连续性均有提高。MuSyQ LAI与其他LAI产品（MODIS c5,GLASS LAI）在整体上有很好的一致性（RMSE=1.0,RMSE=0.81）,但对常绿阔叶林高值处的描述不稳定;（2） 与LAI地面测量数据相比,MuSyQ LAI产品与地面参考图对比结果较好（最高相关性（R²=0.54）和较低总体误差（RMSE=0.96））,其在阔叶作物生长季高值处有些许低估且在某些阔叶林站点有些高估。整体上,MuSyQ LAI产品呈现出较高的精度,可靠的空间分布和连续稳定的时间分布,且对森林LAI的描述具有更可靠的动态范围。     

AMSR-E微波极化指数与MODIS植被指数关系研究

首先，利用辐射传输方程对微波极化指数（MPI，Microwave Polarization Index)进行推导，以AMSR-E像元经纬度为控制条件，采集与之对应的MODIS植被指数（ LAI/NDVI），并将其平均值作为AMSR-E对应像元的值； 然后，对AMSR-E微波极化指数与LAI/NDVI进行相关分析。结果表明，MPI与LAI/NDVI之间存在着指数关系，而且频率越低，相关性越好。     

树冠叶面积体密度和叶面积指数的间接估值方法研究

本文论述了根据计算机断层成像的原理，对树冠的叶面积体密度和叶面积指数等构造参数进行间接估值的方法。说明了对树冠多角度底视数据的获取，对树冠外形的反投影重构，和获得冠层内叶面积体密度的空间分布的原理和方法。总结了测量工作经验，并用测量数据对估值方法进行了检验。     

水稻叶面积指数的高光谱遥感估算模型

通过不同氮素营养水平的水稻田间试验 ,采用单变量线性与非线性拟合模型和逐步回归分析 ,用1 999年试验数据为训练样本 ,建立水稻LAI的高光谱遥感估算模型 ,用 2 0 0 0年试验数据作为测试样本数据 ,对其精度进行评价和验证。结果表明 ,高光谱变量与LAI之间的拟合分析中 ,蓝边内一阶微分的总和与红边内一阶微分的总和的比值和归一化差植被指数是最佳的变量     

Canopy cover effects on local soil water dynamics in a tropical agroforestry system: Evaporation drives soil water isotopic enrichment

Despite the widely held assumption that trees negatively affect the local water budget in densely planted tree plantations, we still lack a clear understanding of the underlying processes by which canopy cover influences local soil water dynamics in more open, humid tropical ecosystems. In this study, we propose a new conceptual model that uses a combination of stable isotope and soil moisture measurements throughout the soil profile to assess potential mechanisms by which evaporation (of surface soil water and of canopy‐intercepted rainfall) affects the relationship between surface soil water isotopic enrichment (lc‐excess) and soil water content. Our conceptual model was derived from soil water data collected under deciduous and evergreen plants in a shade grown coffee agroforestry system in Costa Rica. Reduced soil moisture under shade trees during the “drier” season, coinciding when these trees were defoliated, was largely the result of increase soil water evaporation as indicated by the positive relationship between soil water content and lc‐excess of surface soil water. In contrast, the evergreen coffee shrubs had a higher leaf area index during the “drier” season, leading to enhanced rainfall interception and a negative relationship between lc‐excess and soil water content. During the wet season, there was no clear relationship between soil water content and between lc‐excess of surface soil water. Greater surface soil water under coffee during the dry season may, in part, explain greater preferential flow under coffee compared with under trees in conditions of low rainfall intensities. However, with increasing rainfall intensities during the wet season, there was no obvious difference in preferential flow between the two canopy covers. Results from this study indicate that our new conceptual model can be used to help disentangling the relative influence of canopy cover on local soil water isotopic composition and dynamics, yet also stresses the need for additional measurements to better resolve the underlying processes by which canopy structure influences local water dynamics.     

Estimation of vegetation LAI from hyperspectral reflectance data: Effects of soil type and plant architecture

The retrieval of canopy biophysical variables is known to be affected by confounding factors such as plant type and background reflectance. The effects of soil type and plant architecture on the retrieval of vegetation leaf area index (LAI) from hyperspectral data were assessed in this study. In situ measurements of LAI were related to reflectances in the red and near-infrared and also to five widely used spectral vegetation indices (VIs). The study confirmed that the spectral contrast between leaves and soil background determines the strength of the LAI–reflectance relationship. It was shown that within a given vegetation species, the optimum spectral regions for LAI estimation were similar across the investigated VIs, indicating that the various VIs are basically summarizing the same spectral information for a given vegetation species. Cross-validated results revealed that, narrow-band PVI was less influenced by soil background effects (0.15 ≤ RMSE_cv ≤ 0.56). The results suggest that, when using remote sensing VIs for LAI estimation, not only is the choice of VI of importance but also prior knowledge of plant architecture and soil background. Hence, some kind of landscape stratification is required before using hyperspectral imagery for large-scale mapping of vegetation biophysical variables.     

黄淮海平原冬小麦最大可能蒸散的估算

作物最大可能蒸散考虑了作物及当地地表状况,为当地地表实际覆盖情况下实际蒸散的理论上限值,能客观分析作物对水分的需求程度和农业干旱状况。基于遥感（叶面积指数和地表反照率）数据和逐日气象数据,利用Penman-Monteith公式,计算黄淮海平原小麦种植区27个气象站冬小麦生育期2000-2015年逐日蒸散,提取得到冬小麦生育期逐日最大可能蒸散数据集,并分析其时空变化特征及成因。结果表明:与联合国粮农组织（FAO）单作物系数法计算的最大可能蒸散E_k对比,区域平均最大可能蒸散E_c的时间变化趋势与E_k一致,空间分布上E_c符合客观实际。黄淮海平原冬小麦全生育期、越冬期和返青-拔节期E_c均呈北低南高的分布特征,日平均值分别为1.99 mm,0.44 mm和2.75 mm;其余3个生育期（越冬前、抽穗期、乳熟-成熟期）在空间分布上差异不大,日平均值分别为1.23 mm,4.71 mm和3.74 mm。冬小麦不同生育期（含全生育期）E_c的空间分布主要受叶面积指数分布特征的影响,二者呈显著正相关关系。     

Quantification winter wheat LAI with HJ-1CCD image features over multiple growing seasons

Remote sensing images are widely used to map leaf area index (LAI) continuously over landscape. The objective of this study is to explore the ideal image features from Chinese HJ-1 A/B CCD images for estimating winter wheat LAI in Beijing. Image features were extracted from such images over four seasons of winter wheat growth, including five vegetation indices (VIs), principal components (PC), tasseled cap transformations (TCT) and texture parameters. The LAI was significantly correlated with the near-infrared reflectance band, five VIs [normalized difference vegetation index, enhanced vegetation index (EVI), modified nonlinear vegetation index (MNLI), optimization of soil-adjusted vegetation index, and ratio vegetation index], the first principal component (PC1) and the second TCT component (TCT2). However, these image features cannot significantly improve the estimation accuracy of winter wheat LAI in conjunction with eight texture measures. To determine the few ideal features with the best estimation accuracy, partial least squares regression (PLSR) and variable importance in projection (VIP) were applied to predict LAI values. Four remote sensing features (TCT2, PC1, MNLI and EVI) were chosen based on VIP values. The result of leave-one-out cross-validation demonstrated that the PLSR model based on these four features produced better result than the ten features’ model, throughout the whole growing season. The results of this study suggest that selecting a few ideal image features is sufficient for LAI estimation.     

Evaluation of SPOT imagery for the estimation of grassland biomass

In many regions, a decrease in grasslands and change in their management, which are associated with agricultural intensification, have been observed in the last half-century. Such changes in agricultural practices have caused negative environmental effects that include water pollution, soil degradation and biodiversity loss. Moreover, climate-driven changes in grassland productivity could have serious consequences for the profitability of agriculture. The aim of this study was to assess the ability of remotely sensed data with high spatial resolution to estimate grassland biomass in agricultural areas. A vegetation index, namely the Normalized Difference Vegetation Index (NDVI), and two biophysical variables, the Leaf Area Index (LAI) and the fraction of Vegetation Cover (fCOVER) were computed using five SPOT images acquired during the growing season. In parallel, ground-based information on grassland growth was collected to calculate biomass values. The analysis of the relationship between the variables derived from the remotely sensed data and the biomass observed in the field shows that LAI outperforms NDVI and fCOVER to estimate biomass (R² values of 0.68 against 0.30 and 0.50, respectively). The squared Pearson correlation coefficient between observed and estimated biomass using LAI derived from SPOT images reached 0.73. Biomass maps generated from remotely sensed data were then used to estimate grass reserves at the farm scale in the perspective of operational monitoring and forecasting.