条件植被温度指数的多尺度特性分析与应用

基于2008-2013年关中平原冬小麦单产数据和条件植被温度指数（vegetation temperature condition index,VTCI）的干旱监测结果,分别采用Morlet、Mexican Hat和Paul（m=4）3种非正交小波的功率谱分析冬小麦单产和主要生育期VTCI和单产的多时间尺度特征,借助小波互相关度进一步确定两个时间序列在时频域局部相关的密切程度,并以此构建主要生育期加权VTCI与冬小麦单产间的线性回归模型。结果表明,基于同一小波函数确定的主要生育期VTCI的振荡能量不同,而基于不同小波函数确定的同一生育期VTCI的主振荡周期及其与单产对应的小波互相关系数也存在差异,但各生育时期VTCI均存在着6 a左右的主振荡周期。基于Paul（m=4）小波的各生育时期VTCI与单产时间序列的多尺度相关性分析的效果最佳（R2=0.521）,且Paul（m=4）对应的模型的单产估测结果与实测单产的平均相对误差较之于Morlet和Mexican Hat小波函数获得的相对误差分别降低了0.78%和0.30%,表明Paul（m=4）小波函数能更好地用于干旱对冬小麦单产的影响评估研究,也可用于多尺度的干旱影响评估研究。     

利用双变量同化与PCA-Copula法的冬小麦单产估测

为了进一步提高冬小麦产量估测的精度,基于集合卡尔曼滤波算法和粒子滤波（particle filter, PF）算法,对CERES–Wheat模型模拟的冬小麦主要生育期条件植被温度指数（vegetation temperature condition index,VTCI）、叶面积指数（leaf area index, LAI）和中分辨率成像光谱仪（moderate-resolution imaging spectroradiometer, MODIS）数据反演的VTCI、LAI进行同化,利用主成分分析与Copula函数结合的方法构建单变量和双变量的综合长势监测指标,建立冬小麦单产估测模型,并通过对比分析选择最优模型,对2017—2020年关中平原的冬小麦单产进行估测。结果表明,单点尺度的同化VTCI、同化LAI均能综合反映MODIS观测值和模型模拟值的变化特征,且PF算法具有更好的同化效果;区域尺度下利用PF算法得到的同化VTCI和LAI所构建的双变量估产模型精度最高,与未同化VTCI和LAI构建的估产模型精度相比,研究区各县（区）的冬小麦估测单产与实际单产的均方根误差降低了56.25 kg/hm2,平均相对误差降低了1.51%,表明该模型能有效提高产量估测的精度,应用该模型进行大范围的冬小麦产量估测具有较好的适用性。     

冬小麦叶面积指数的高光谱估算模型研究

本文以山东禹城为研究区,利用地面实测光谱数据,探讨不同植被指数和红边参数建立高光谱模型反演冬小麦叶面积指数的精度。通过逐波段分析计算了4种植被指数(NDVI、RVI、SAVI、EVI),结合同步观测LAI数据,确定反演叶面积指数的最优波段;计算了5种常用的高光谱植被指数MCARI、MCARI2、OSAVI、MTVI2、MSAVI2,同时利用4种常用方法计算红边位置和红谷,与实测LAI进行回归分析,比较植被指数和红边参数模型对冬小麦LAI的估测精度。结果表明各因子与LAI均具有较高的相关性,整个研究区归一化植被指数具有最高的反演精度,确定了估算冬小麦LAI的最优模型,并使用独立的LAI观测数据对模型进行了验证。     

非等间距WGM-AR模型在基坑周边建筑物沉降预测中的应用

非等间距GM（1,1）模型在不等时间间隔序列的趋势分析和预测方面具有重要作用,在此基础上,提出一种基于非等间距加权GM（1,1）模型和自回归AR（p）模型相结合的非等间距加权灰色自回归模型（非等间距WGM-AR模型）.将基坑周边建筑物沉降监测数据视为具有确定趋势的非等时间序列,对序列进行平滑处理,利用非等间距加权GM（1,1）模型提取该时序中的确定性趋势项,用自回归AR（p）模型分析生成的等间距序列中的随机项,并采用内插法得到沉降监测序列的随机项.将组合模型与非等间距GM（1,1）模型计算结果对比分析,结果表明,组合模型具有更高的预测精度,在基坑周边建筑物沉降预测中具有较高的应用价值.     

基于环境星CCD数据的冬小麦叶面积指数遥感监测模型研究

以山东禹城为研究区,利用我国自主研发的环境星数据,计算了4种植被指数,即归一化植被指数(NDVI)、比值植被指数(RVI)、土壤调节植被指数(SAVI)及增强型植被指数(EVI);结合同步观测数据,将植被指数与实测叶面积指数(LAI)进行回归分析,比较各种植被指数模型对冬小麦LAI的估测精度。结果表明,4种植被指数与LAI均具有较高的相关性,其中,比值植被指数(RVI)对LAI反演精度最高,即LAI=2.967 lnRVI-1.201是估算冬小麦LAI的最优模型。使用2009年5月冬小麦LAI观测数据对模型进行验证,平均相对误差为19%。     

关键时相长势—环境和景观特征对河北省县级尺度冬小麦单产估算精度影响分析

区域尺度上精准、快速的作物单产估算可以有效地为国家粮食安全相关政策的制定提供数据支撑。本文针对县级估产时相和特征类型选择问题,基于遥感、气象和统计等多源数据,通过不同时相和特征要素之间的组合分析来探索其对于县级尺度冬小麦单产估算的影响。特征要素主要考虑作物长势、环境（水分和光温条件）和农田景观3个类型;时相主要考虑由冬小麦生长过程NDVI（Normalized Difference Vegetation Index）曲线特征提取的5个关键时段（P1—P5）。利用不同时相与类型特征的组合与统计单产构建随机森林回归模型,根据精度评价结果分析各组合的优劣。2014年—2017年的数据用来建模,2018年数据用来验证。对于单时相,P2、P3、P4的表现明显好于P1和P5;多时相的准确度明显优于单时相,其中P2、P4的组合效果最佳。对于不同类型的特征要素,作物长势特征参量对估产精度的影响最大,而水分影响和光温条件等环境因子的加入对估产准确性并没有明显提升,农田景观参数的加入能够有效提升估产的准确性。在最优组合的基础上,剔除冗余变量优选出5个重要的指标因子（PROP、NDVI_P2、B2_P2、ED、B1_P4）,并建立单产估算模型获取2018年河北省冬小麦县级尺度单产。结果表明,平均相对误差（MRE）仅为2.85%,决定系数（R ²）为0.83,均方根误差（RMSE）为253.25 kg/ha,归一化均方根误差（NRMSE）为4.09%。研究结果为全国县级冬小麦单产估算提供了新的思路和方法参考。     

无人机激光雷达人工林林分高估测模型分析EI北大核心CSCD

中国人工林面积居世界第一,精确地对人工林结构进行监测具有重要意义。本研究以内蒙古自治区赤峰市旺业甸林场内的落叶松和油松人工林为研究对象,利用无人机激光雷达LiDAR(Light Detection And Ranging)离散点云数据和地面样地调查数据对人工林林分高进行建模,通过点云特征变量与地面测量的6种林分高(包括:Lorey’s高、算术平均高、最大高、优势树高、中位数高和树冠面积加权高)间的Pearson’s相关性筛选自变量,然后利用全子集回归构建不同林分高估测模型,并采用交叉检验法进行精度评价。结果表明:激光雷达点云高度百分位数与不同林分高相关性均较高,通过一元线性回归构建的不同林分高结果最优,且估测模型的自变量均为高度特征变量。Lorey’s高(R^(2)=0.91—0.97,rRMSE=2.75%—3.96%)、优势树高(R^(2)=0.86—0.97,rRMSE=3.72%—3.83%)和树冠面积加权高(R^(2)=0.86—0.96,rRMSE=3.81%—4.73%)估测精度最高,算术平均高(R^(2)=0.85—0.94,rRMSE=4.52%—6.07%)和中位数高(R^(2)=0.80—0.95,rRMSE=5.37%—7.34%)次之,最大高(R^(2)=0.69—0.87,rRMSE=6.19%—8.09%)最低。针对不同森林类型,落叶松人工林林分高估测精度最优,优于不区分森林类型模型的估测精度(ΔR^(2)=0—0.05,ΔrRMSE=-0.69%—1.97%),优于油松林林分高模型的估测精度(ΔR^(2)=0.06—0.18,ΔrRMSE=-1.90%—1.13%)。无人机激光雷达可以用于估测北方温带针叶林的林分高,能够满足人工林资源调查快速、精确的要求。     

基于地统计学空间插值法的作物单产估算

针对第三次全国农业普查农产量抽样调查的样本调查方法改革后,现行的传统农作物单产抽样调查估算方法无法有效利用调查样本所包含的空间差异性来估算抽样设计中子总体的作物单产等问题,提出了一种基于地统计学的空间插值估算方法,对调查队取得的样本数据进行深度挖掘。以河南省辉县为研究区,以冬小麦单产为研究对象,进行实验和结果分析。结果表明,利用地统计学克里格插值法取得的村级冬小麦平均单产估算精度在90%以上的村达到研究区村总量的91%,且其中83%的村估算精度优于95%;估算精度在90%以下的所有村的冬小麦种植总面积仅占全县的2.26%,对全县产量的影响微乎其微。基于地统计学的空间插值法很好地分析和利用了样本属性中的冬小麦单产信息表现出的空间相关性和异质性,不仅能较高精度地估算出现行的传统农作物单产推算方法无法给出的抽样设计中子总体(全县各村)的单产信息,而且能较好地给出总体(全县)的单产信息。利用该方法得到的全县冬小麦平均单产估算精度达到97.75%,高于现行的传统农作物单产抽样调查估算精度,估算效果良好,方法可行性高,相对传统方法还可起到费省效宏的作用。     

改进不等时距权重的灰色残差组合修正模型及其应用

为提高传统不等时距灰色模型（TUTGM）的预测精度，提出了一种改进不等时距权重的灰色残差组合修正模型（IUTWGM-RCC）。首先在传统不等时距灰色模型中引入时距权重分配系数，按照累加生成和累减还原过程的生成序列不同，构建了4种不同的预测模型，并依据相似度准则确定最优拟合序列和预测值；然后采用正弦函数和谐波变化生成的周期序列函数修正残差序列，进一步提高模型的预测精度；最后对建筑物3个观测点的沉降量进行预测。结果表明，累减还原过程引入不等时距权重的灰色模型预测精度最高，经残差组合修正后，预测结果的后验差比分别为0.04、0.11和0.05，精度等级为1级。     

利用光谱反射率估算叶片生化组分和籽粒品质指标研究

对可见光至短波红外波段(350—2500nm)冬小麦田间冠层光谱反射率与叶片含氮量间的关系进行了相关分析。结果表明，820—1100nm波段的光谱反射率与叶片含氮量极显著正相关；1150—1300hm波段的光谱反射率与叶片含氮量显著正相关，以上两波段为叶片全氮的敏感波段。对各生育时期叶片全氮与其他生化组分的关系进行了回归分析，并建立了相关的回归方程，显著性检验结果表明，方程具有较高的可靠性。小麦的叶片含氮量可以估算其它生化组分及干物质指标含量，开花期叶片含氮量可用来估测籽粒蛋白质和干面筋等品质指标含量。     

On agricultural drought monitoring in Australia using Himawari-8 geostationary thermal infrared observations

Monitoring agricultural drought effectively and timely is important to support drought management and food security. Effective drought monitoring requires a suite of drought indices to capture the evolution process of drought. Thermal infrared signals respond rapidly to vegetation water stress, thus being regarded useful for drought monitoring at the early stage. Several temperature-based drought indices have been developed considering the role of land surface temperature (LST) in surface energy and water balance. Here, we compared the recently proposed Temperature Rise Index (TRI) with several agricultural drought indices that also use thermal infrared observations, including Temperature Condition Index (TCI), Vegetation Health Index (VHI) and satellite-derived evapotranspiration ratio anomaly (Δf_RET) for a better understanding of these thermal infrared drought indices. To do so, we developed a new method for calculating TRI directly from the top-of-atmosphere brightness temperatures in the two split-window channels (centered around ∼11 and 12 μm) rather than from LST. TRI calculated using the Himawari-8 brightness temperatures (TRI_BT) and LST retrievals (TRI_LST), along with the other LST-based indices, were calculated for the growing season (July–October) of 2015−2019 over the Australian wheatbelt. An evaluation was conducted by spatiotemporally comparing the indices with the drought indices used by the Australian Bureau of Meteorology in the official drought reports: the Precipitation Condition Index (PCI) and the Soil Moisture Condition Index (SMCI). All the LST-based drought indices captured the wet conditions in 2016 and dry conditions in 2019 clearly. Ranking of Pearson correlations of the LST-based indices with regards to PCI and SMCI produced very similar results. TRI_BT and TRI_LST showed the best agreement with PCI and SMCI (r > 0.4). TCI and VHI presented lower consistency with PCI and SMCI compared with TRI_BT and TRI_LST. Δf_RET had weaker correlations than the other LST-based indices in this case study, possibly because of outliers affecting the scaling procedure. The capability of drought early warning for TRI was demonstrated by comparing with the monthly time series of the greenness index Vegetation Condition Index (VCI) in a case study of 2018 considering the relatively slow response of the greenness index to drought. TRI_BT and TRI_LST had a lead of one month in showing the changing dryness conditions compared with VCI. In addition, the LST-based indices were correlated with annual wheat yield. Compared to wheat yields, all LST-based indices had a peak correlation in September. TRI_BT and TRI_LST had strong peak and average correlations with wheat yield (r ≥ 0.8). We conclude that TRI has promise for agricultural drought early warning, and TRI_BT appears to be a good candidate for efficient operational drought early warning given the readily accessible inputs and simple calculation approach.     

Monitoring winter wheat growth in North China by combining a crop model and remote sensing data

Both of crop growth simulation models and remote sensing method have a high potential in crop growth monitoring and yield prediction. However, crop models have limitations in regional application and remote sensing in describing the growth process. Therefore, many researchers try to combine those two approaches for estimating the regional crop yields. In this paper, the WOFOST model was adjusted and regionalized for winter wheat in North China and coupled through the LAI to the SAIL–PROSPECT model in order to simulate soil adjusted vegetation index (SAVI). Using the optimization software (FSEOPT), the crop model was then re-initialized by minimizing the differences between simulated and synthesized SAVI from remote sensing data to monitor winter wheat growth at the potential production level. Initial conditions, which strongly impact phenological development and growth, and which are hardly known at the regional scale (such as emergence date or biomass at turn-green stage), were chosen to be re-initialized. It was shown that re-initializing emergence date by using remote sensing data brought simulated anthesis and maturity date closer to measured values than without remote sensing data. Also the re-initialization of regional biomass weight at turn-green stage led that the spatial distribution of simulated weight of storage organ was more consistent to official yields. This approach has some potential to aid in scaling local simulation of crop phenological development and growth to the regional scale but requires further validation.     

基于温度供水干旱指数的京津冀春旱监测及时空分布分析

为提高农业干旱监测效果和精度,在对传统干旱监测模型对比分析基础上,本文提出将温度植被干旱指数(TVDI)和植被供水指数(VSWI)加权联合构建温度供水干旱指数(TSWDI)的研究思路。以京津冀2006—2012年5月份数据作为实验统计数据,以京津冀2006—2016年3—5月份春旱监测为例进行了模型实验。实验结果证实,TSWDI指数相对其他两个指数与10、20和50 cm深处的土壤水分相关性更高,能够更精准地反映农业干旱状况。TSWDI计算结果显示,京津冀干旱分布具有如下特征:从时间角度看,2006—2016年整体干旱状况逐渐缓解,特别是自2010年至今,研究区域干旱程度逐步减轻;从空间角度看,京津冀区域整体干旱面积逐步减少。     

Investigation of automatic feature weighting methods (Fisher,Chi-square and Relief-F) for landslide susceptibility mapping

In landslide susceptibility mapping, factor weights have been usually determined by expert judgements. A novel methodology for weighting landslide causative factors by integrating statistical feature weighting algorithms was proposed. The primary focus of this study is to investigate the effectiveness of automatic feature weighting algorithms, namely Fisher, Chi-square and Relief-F algorithms. Analytic hierarchy process (AHP) method was used as a benchmark method to compare the performances of the weighting algorithms. All weighted factors were tested using factor-weighted overlay method, and quality of these maps was assessed using overall accuracy, area under the ROC curve (AUC) and success rate curve. In addition, Wilcoxon’s signed-rank test was applied to evaluate statistical differences between both estimated overall accuracies and AUCs, respectively. Results showed that the weights determined by feature weighting methods outperformed the conventional AHP method by about 6% and this level of differences was found to be statistically significant.     

Simple DEM-Based Methods to Delineate Channel Networks for Hydrogeomorphological Mapping

To delineate channel networks from DEMs regardless of landform type, this article proposes a new method using slope-weighted flow accumulation. To validate the method, SRTM-3, a global DEM dataset with a resolution of approximately 90 m, was used for analysis of the Loess Plateau, China. Channel networks delineated with and without slope-weighted flow accumulation were derived in both uplands and hilly lands for comparison. In the weighted flow accumulation method, the thresholds for delineating the channels were defined by detecting a turning point in the frequency distribution of the weighted flow accumulation function or by visual similarity with drainage channels extracted from topographic maps. The channel networks delineated with weighting showed closer correlation with a topographic map than the channel networks without weighting, despite the differences in thresholds. Moreover, the channel networks delineated with weighting represented the differences between landform types, while the channel networks without weighting did not. Weighting on the basis of the slope angle shows promise as a general channel delineation method which reflects the actual topography due to its hydrogeomorphological functions.     

Large-area rice yield forecasting using satellite imageries

Ability to make large-area yield prediction before harvest is important in many aspects of agricultural decision-making. In this study, canopy reflectance band ratios (NIR/RED, NIR/GRN) of paddy rice (Oryza sativa L.) at booting stage, from field measurements conducted from 1999 to 2005, were correlated with the corresponding yield data to derive regression-type yield prediction models for the first and second season crop, respectively. These yield models were then validated with ground truth measurements conducted in 2007 and 2008 at eight sites, of different soil properties, climatic conditions, and various treatments in cultivars planted and N application rates, using surface reflectance retrieved from atmospherically corrected SPOT imageries. These validation tests indicated that root mean square error of predicting grain yields per unit area by the proposed models were less than 0.7 T ha⁻¹ for both cropping seasons. Since village is the basic unit for national rice yield census statistics in Taiwan, the yield models were further used to forecast average regional yields for 14 selected villages and compared with officially reported data. Results indicate that the average yield per unit area at village scale can be forecasted with a root mean square error of 1.1 T ha⁻¹ provided no damaging weather occurred during the final month before actual harvest. The methodology can be applied to other optical sensors with similar spectral bands in the visible/near-infrared and to different geographical regions provided that the relation between yield and spectral index is established.     

植被指数与水稻长势及产量结构要素关系的研究

本文介绍水稻产量与其反射波谱数据的相关性试验结果,研究表明,水稻齐穗期以前,植被指数RVI与水稻的生长状况(叶面积指数及干物重)间相关系数很高,它们之间有较好的对应关系;齐穗期以,RVI与水稻于物产量之间的相关性也很显著;同时还发现垂直植被指数PVI与水稻产量结构各要素(每亩穗数,每穗实粒数,千粒重)以及理论产量之间也具有较好的相关性。     

Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield

ABSTRACT

Agricultural drought threatens food security. Numerous remote-sensing drought indices have been developed, but their different principles, assumptions and physical quantities make it necessary to compare their suitability for drought monitoring over large areas. Here, we analyzed the performance of three typical remote sensing-based drought indices for monitoring agricultural drought in two major agricultural production regions in Shaanxi and Henan provinces, northern China (predominantly rain-fed and irrigated agriculture, respectively): vegetation health index (VHI), temperature vegetation dryness index (TVDI) and drought severity index (DSI). We compared the agreement between these indices and the standardized precipitation index (SPI), soil moisture, winter wheat yield and National Meteorological Drought Monitoring (NMDM) maps. On average, DSI outperformed the other indices, with stronger correlations with SPI and soil moisture. DSI also corresponded better with soil moisture and NMDM maps. The jointing and grain-filling stages of winter wheat are more sensitive to water stress, indicating that winter wheat required more water during these stages. Moreover, the correlations between the drought indices and SPI, soil moisture, and winter wheat yield were generally stronger in Shaanxi province than in Henan province, suggesting that remote-sensing drought indices provide more accurate predictions of the impacts of drought in predominantly rain-fed agricultural areas.     

PM_(2.5)土地利用回归模型地理要素的选取

针对PM_(2.5)土地利用回归(Land Use Regression,简称LUR)模型地理要素选取不规范、代表性不明确的问题,本文从地理要素的精度、易获取程度、广泛应用程度及地理要素与PM_(2.5)的经验相关性4个评价指标出发,结合层次分析法(Analytic Hierarchy Process简称AHP)和熵值法,对京津冀地区PM_(2.5)LUR模型构建时各备选地理要素的权重进行综合度量。结果显示,京津冀地区污染企业、交通网络、地表覆盖等优选地理要素的综合权重分别为20%、19%、18%,地理要素与PM_(2.5)的经验相关性和数据精度等优先评价指标的综合权重占××的比例分别次为49%、26%。该方法得出的评价结果符合客观实际,能达到科学选取地理要素的目的,对评估地理要素的代表性和分析LUR的异同性及地方主要污染要素具有重要的参考价值。