Seasonal variations of leaf area index of agricultural fields retrieved from Landsat data

The derivation of leaf area index (LAI) from satellite optical data has been the subject of a large amount of work. In contrast, few papers have addressed the effective model inversion of high resolution satellite images for a complete series of data for the various crop species in a given region. The present study is focused on the assessment of a LAI model inversion approach applied to multitemporal optical data, over an agricultural region having various crop types with different crop calendars. Both the inversion approach and data sources are chosen because of their wide use. Crops in the study region (Barrax, Castilla-La Mancha, Spain) include: cereal, corn, alfalfa, sugar beet, onion, garlic, papaver. Some of the crop types (onion, garlic, papaver) have not been addressed in previous studies. We use in-situ measurement sets and literature values as a priori data in the PROSPECT + SAIL models to produce Look Up Tables (LUTs). Those LUTs are subsequently used to invert Landsat-TM and Landsat-ETM+ image series (12 dates from March to September 2003). The Look Up Tables are adapted to different crop types, identified on the images by ground survey and by Landsat classification. The retrieved LAI values are compared to in-situ measurements available from the campaign conducted in mid July-2003. Very good agreement (a high linear correlation) is obtained for LAI values from 0.1 to 6.0. LAI maps are then produced for each of the 12 dates. The LAI temporal variation shows consistency with the crop phenological stages. The inversion method is favourably compared to a method relying on the empirical relationship between LAI and NDVI from Landsat data. This offers perspectives for future optical satellite data that will ensure high resolution and high temporal frequency.     

Spatially and temporally continuous LAI data sets based on an integrated filtering method: Examples from North America

Leaf Area Index (LAI) is an important biophysical variable for characterizing the land surface vegetation. Global LAI product has been routinely produced from the MODerate resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellite platforms. However, the MODIS standard LAI product is not continuous both spatially and temporally. To fill the gaps and improve the quality, we have developed a data filtering algorithm. This filter, called the temporal spatial filter (TSF), integrates both spatial and temporal characteristics for different plant functional types. The spatial gaps are first filled with the multi-year averages of the same day. If the values are missing over all years, the pixel is filled with a new estimate using the vegetation continuous field-ecosystem curve fitting method. The TSF integrates both the multi-seasonal average trend (background) and the seasonal observation. We implement this algorithm using the MODIS Collection 4 LAI product over North America. Comparison of the TSF results with the Savitzky-Golay filter indicates that the TSF performs much better in restoring the spatial and temporal distribution of seasonal LAI trends. The new LAI product has been validated by comparing with field measurements and the derived LAI maps from ETM+ data at a broadleaf forest site and an agricultural site. The validation results indicate that the new LAI product agrees better with both the field measurements and LAI values obtained from the ETM+ than does the MODIS LAI standard product, which usually shows higher LAI values.     

Integration of MODIS data into a simple model for the spatial distributed simulation of soil water content and evapotranspiration

A precise simulation of soil water content (SWC) and actual evapotranspiration (ET_a) in a region or a catchment depends on the accuracy of the spatial data inputs. In this study, we developed a simple grid-based soil water balance model. In this model, remotely sensed vegetation data are used to estimate spatial distributions of daily SWC and ET_a rates. The model was validated by comparing simulated SWC with the measured by gravimetric method and time domain reflectometry (TDR) at an experimental test site located in Northeastern Germany in the time period 1993-1998. The index of agreement IA and the root-mean-square error obtained from the comparison of the TDR measurements to the simulated values ranged from 0.45 to 0.80 and from 0.029 to 0.061 cm³/cm³, respectively. The comparison of simulated ET_a rates to those measured by four large-scale lysimeters at another test site showed IA values above 0.87 and R² values higher than 0.59. For the regional application of the model, a method was developed to integrate the Moderate Resolution Imaging Spectrometer (MODIS) vegetation data into the model. The MODIS data used in our study consist of 16-day normalized difference vegetation index and 8-day leaf area index products. Regarding the spatial application of the model, our approach was tested in a catchment located in Northeastern Germany in 2001-2003. A sufficient correlation between daily discharge rates measured at two observation gauges in the catchment and the corresponding simulated discharge rates and also good correlations between the simulated ET_a rates and the MODIS-leaf area index values indicate that the model is an appropriate simulation tool at regional scale if the corresponding additional spatial databases regarding surface and soil properties are available.     

大豆叶面积的高光谱模型

以ASD FieldSpec-Vnir光谱仪实测不同生长季大豆的冠层反射率,同期采集对应大豆LAI,然后逐波段分析冠层光谱反射率、导数光谱与大豆LAI的相关关系;并采用单变量线性回归逐波段分析了冠层光谱反射率、导数光谱与大豆LAI确定性系数随波长的变化趋势,建立了以近红外与可见光波段冠层光谱反射率的比值植被指数RVI与大豆LAI的高光谱遥感估算模型。结果表明,冠层光谱反射率在350￣680nm、760￣1050nm波谱区与大豆LAI相关性较大,而在红边区680￣760nm的相关性变化较大;导数光谱在红边区与大豆LAI相关程度高。通RVI方式建立的遥感估算模型能较为准确估算大豆LAI,通过对红外与蓝波段建立的RVI指数与大豆LAI的回归模型,表明其预测大豆LAI的能力较好,有进一步研究的必要;通过对比发现,神经网络模型可以大大提升高光谱反演大豆LAI的水平,模型的确定系数R2为0.9661,而总均方根误差RMSE仅为0.446m2.m-2。     

Satellite passive microwave remote sensing for monitoring global land surface phenology

Vegetation phenology characterizes seasonal life-cycle events that influence the carbon cycle and land-atmosphere water and energy exchange. We analyzed global phenology cycles over a six year record (2003-2008) using satellite passive microwave remote sensing based Vegetation Optical Depth (VOD) retrievals derived from daily time series brightness temperature (T_b) measurements from the Advanced Microwave Scanning Radiometer on EOS (AMSR-E) and other ancillary data inputs. The VOD parameter derives vegetation canopy attenuation at a given microwave frequency (18.7 GHz) and varies with canopy height, density, structure and water content. An error sensitivity analysis indicates that the retrieval algorithm can resolve the VOD seasonal cycle over a majority of global vegetated land areas. The VOD results corresponded favorably (p < 0.01) with vegetation indices (VIs) and leaf area index (LAI) information from satellite optical-infrared (MODIS) remote sensing, and phenology cycles determined from a simple bioclimatic growing season index (GSI) for over 82% of the global domain. Lower biomass land cover classes (e.g. savannas) show the highest correlations (R = 0.66), with reduced correspondence at higher biomass levels (0.03 < R < 0.51) and higher correlations for homogeneous land cover areas (0.41 < R < 0.83). The VOD results display a unique end-of-season signal relative to VI and LAI series, and may reflect microwave sensitivity to the timing of vegetation biomass depletion (e.g. leaf abscission) and associated changes in canopy water content (e.g. dormancy preparation). The VOD parameter is independent of and synergistic with optical-infrared remote sensing based vegetation metrics, and contributes to a more comprehensive view of land surface phenology.     

基于无人机高光谱数据的玉米叶面积指数估算

无人机高光谱遥感是低成本、高精度获取精细尺度农作物生物物理参数和生物化学参数的新型手段,以此快速反演叶面积指数（Leaf Area Index, LAI）对作物长势评价、产量预测具有重要意义。以山东禹城市玉米为研究对象,利用PROSAIL辐射传输模型模拟玉米冠层反射率获取LAI特征响应波段结合相关性定量分析获取对LAI变化最为敏感的波段,并以此计算6种植被指数（Vegetation Index,VI）,利用6种回归模型分别对单一特征波段和VI进行反演建模,以实测LAI评定模型精度。研究表明,光谱反射率中516、636、702、760和867 nm等波段对LAI变化最为敏感,以此建立的单一特征波段反演模型预测LAI精度R2为0.44~0.58;RMSE为0.16~0.18,其中636 nm建立的模型(LAI=21.86exp(-29.47R₆₃₆))相比其他反演模型预测精度较高（R2=0.58,RMSE=0.16）;6种植被指数与LAI高度相关,相关性系数R ²为0.85~0.86,以此建立的反演模型相比单一特征波段反演模型精度有所提高,R2为0.66~0.72,RMSE为0.12~0.14;其中mNDVI构建的LAI估算模型（LAI=exp(2.76~1.77/mNDVI))精度最高（R2=0.72,RMSE=0.13）。无人机高光谱遥感是快速、无损监测农作物生长信息的有效手段,为指导精细化尺度作物管理提供依据。     

Leaf Area Index Inversion of Summer Maize at Multiple Growth Stages based on BP Neural Network

Leaf Area Index (LAI) is an important vegetation structure parameter in biogeochemical cycling. In view of the lack of LAI inversion in the multiple growth period of summer maize based on GF-1 WFV satellite images in China, this study constructs a BP neural network model (BP1 model and BP2 model) based on different hidden layers, and compares and analyzes the accuracy of the inversion between the BP1 model, BP2 model and 6 statistical models (NDVI、RVI、DVI、EVI、SAVI、ARVI). Based on the measured data, BP1 model and BP2 model are used to map the LAI dynamic changes of summer maize. The results show that LAI has good correlation with 6 common statistical models, and the fitting degree of the NDVI exponential equation regression model is the best. The overallR ² of BP neural network model is slightly smaller than the statistical model, while RMSE is less than the statistical model, and the errors with the measured value is smaller than the statistical model. So both the statistical model and the BP neural network model have advantages and disadvantages. The BP2 model is superior to the BP1 model inR ² and RMSE, and can obtain more accurate inversion values, and the overall prediction accuracy of BP2 is higher. Based on the BP neural network simulation of summer maize growth period inversion, the LAI value presents a slow increase to the gradual decrease of S type change process, which is basically in line with the crop growth rules. The study combines with the BP neural network model established by different hidden layers to provide a method for the application of GF-1 satellite in the application of crop leaf area index multiple growth period inversion.     

An interpolation procedure for generalizing a look-up table inversion method

The inversion of physically based reflectance models is increasingly efficient for extracting vegetation variables from remote sensing images. It requires a vegetation reflectance model and an inversion method that are accurate and efficient. Usually, the complexity of reflectance models implies to use specific inversion methods (e.g., look-up table and neural network). Unfortunately, these methods are valid only for the view-sun directions for which they are designed. A developed look-up table based inversion method avoids this limitation: it generalizes any look-up table for any view-sun direction, and more generally for any input parameter value. It uses a look-up table made of c_i coefficients of any analytical expression h that fits a set of reflectance values simulated by the Discrete Anisotropic Radiative Transfer (DART) model. Interpolation on coefficients c_i allows h to give reflectance values for any input parameter value. We settled some options of the inversion method with sensitivity studies: tree covers are simulated with 4-tree scenes, expression h has six coefficients c_i and the interpolation is the continuous first derivative interpolation method. Moreover, the robustness of the inversion method was validated. The ability to generalize a look-up table for any view-sun direction was successfully tested with the inversion of SPOT images of Fontainebleau (France) forest. LAI maps proved to be as accurate (i.e., RMSE≈1.3) as those obtained with classical relationships that are calibrated with in situ LAI measurements. Here, the advantage of our inversion method was to avoid this calibration.     

NSST框架下结合局部自适应亮度与DCAPCNN的双波段图像融合

针对现有红外与可见光图像融合后,易出现边缘平滑严重、纹理细节恢复不足、对比度低、显著目标不突出、部分信息缺失等问题,提出一种基于非下采样剪切波变换(non-subsampled shearlet transform,NSST)的红外与可见光双波段图像融合算法。首先,采用基于自适应引导滤波(adaptive guided filter,AGF)的方法对源红外、可见光图像增强。其次,利用NSST正变换分别对源红外与可见光图像分解,得到红外、可见光图像的低、高频子带分量。然后,分别通过基于局部自适应亮度(local adaptive intensity,LAI)与双通道自适应脉冲耦合神经网络(dual channel adaptive pulse coupled neural network,DCAPCNN)规则融合低、高频子带分量。最后,通过NSST逆变换得到最终融合图像。实验结果表明,本文算法整体对比度更适宜,对红外热目标及可见光背景的边缘与纹理的细节恢复性更好,融合图像信噪比高,有效结合了红外及可见光图像的各自优势,与现有传统图像融合与深度学习融合算法相比,本文算法达到了更好的实验效果,在主观视觉感知和客观指标评价中均具有更好的融合性能。     

利用可见/近红外光谱测定小麦叶面积指数的改进研究

改进了小麦叶面积指数的可见/近红外光谱测定模型。以不同方法实现了小麦冠层反射光谱的预处理,并采用偏最小二乘回归算法（PLS）建立小麦叶面积指数估测模型对其进行比较分析,发现小波除噪结合一阶导数能最有效地消除原始光谱的噪声与背景信息,此时PLS模型校正集与预测集R2分别为0.849与0.835。为进一步优化模型,对经一阶导数结合小波除噪后的光谱采用主成分分析法（PCA）降维,以前4个主成分（含原始光谱84.867%特征信息）为输入变量,采用小二乘支撑向量机回归算法（LS-SVR）建立了小麦叶面积指数估测模型,其校正集与预测集R2分别达0.905与0.883,具有比PLS算法更高的精度。结果表明:以小波除噪结合一阶导数去除小麦冠层反射光谱中的土壤背景信息以提高模型精度是可行的,且LS-SVR是建模的优选方法。