基于小波理论的干旱区内陆湖泊叶绿素a的TM影像遥感反演

叶绿素a(Chl-a)是衡量湖泊富营养化的重要指标,利用遥感技术动态监测面积较大的湖区水体中Chl-a浓度对了解湖区水质具有重要意义。以内蒙古乌梁素海为例,提出利用TM影像中的水体实测光谱进行小波去噪和光谱信号重构,并结合水质采样实测数据进行神经网络拟合,建立光谱反射率比值与Chl-a浓度的反演模型的方法。结果显示:小波理论和神经网络相结合的模型可以适用于估算乌梁素海Chl-a浓度,去噪后Chl-a浓度与光谱信号的相关系数(-0.575)较去噪前(-0.417)明显增强,去噪后的采样点光谱信号与Chl-a浓度之间表现出比原始信号更强的负相关性,证明了去噪后的观测值可进一步减弱随机误差的干扰和去除噪声,使观测数据更加逼近Chl-a浓度的真实情况,图像去噪重构结果显示重构后的光谱范围较之前有所缩窄,部分信号点得到了增强,但基本剖面结构并没有产生较大变化,反演模型的平均相对误差为0.142,与其他研究相比差别不大。反演得出的乌梁素海Chl-a浓度分布反映了污染源的分布,同时说明了乌梁素海Chl-a浓度在时空分布上呈现一定的差异,表现为丰水期呈现浅水区Chl-a浓度值高于湖心区,来水区高于其他湖区的分布趋势,枯水期乌梁素海中部呈现由西向东Chl-a浓度逐步降低的分布规律,西部呈均一化分布。反演模型基本可以满足实际预测的需要。但模型在具体应用中在影像数据采集、数据量及算法方面还有很大的改进空间,该方法的提出为干旱区大型内陆水体富营养化的实时定量遥感监测提供了新的解决方案。     

Internet of things-enabled photomultiplier tube- and smartphone-based electrochemiluminescence platform to detect choline and dopamine using 3D-printed closed bipolar electrodes

There is a growing demand to realize low-cost miniaturized point-of-care testing diagnostic devices capable of performing many analytical assays. To fabricate such devices, three-dimensional printing (3DP)-based fabrication techniques provide a turnkey approach with marked precision and accuracy. Here, a 3DP fabrication technique was successfully utilized to fabricate closed bipolar electrode-based electrochemiluminescence (ECL) devices using conductive graphene filament. Furthermore, using these ECL devices, Ru(bpy)₃²⁺/TPrA- and luminol/H₂O₂-based electrochemistry was leveraged to sense dopamine and choline respectively. For ECL signal capture, two distinct approaches were used, first a smartphone-based miniaturized platform and the second with a photomultiplier tube embedded with the internet of things technology. Choline sensing led to a linear range 5–700 μM and 30–700 μM with a limit of detection (LOD) of 1.25 μM (R² = 0.98, N = 3) and 3.27 μM (R² = 0.97, N = 3). Furthermore, dopamine sensing was achieved in a linear range 0.5–100 μM with an LOD = 2 μM (R² = 0.99, N = 3) and LOD = 0.33 μM (R² = 0.98, N = 3). Overall, the fabricated devices have the potential to be utilized effectively in real-time applications such as point-of-care testing.     

青藏高原草地地上生物量估算

及时准确评估草地产草量对草地资源的科学管理和可持续发展具有重要意义。青藏高原自然环境特殊,气候差异显著,地形复杂,仅依靠遥感信息准确监测草地地上生物量(Aboveground Biomass,AGB)变化有较大限制。基于青藏高原草地AGB野外实测数据与Landsat遥感影像,探索了植被指数表征草地AGB信息的有效性,评估了气象和地形信息对准确估算草地AGB的影响,综合利用气象、地形和遥感信息,在新一代地球科学数据和分析应用平台(Google Earth Engine)上构建了梯度增强回归树草地AGB估算模型,绘制了青藏高原多年草地AGB空间分布图。结果表明:(1)基于单因素遥感因子的线性回归模型仅能解释8%-40%的草地AGB变化情况,其中绿色归一化植被指数(Green Normalized Difference Vegetation Index, GNDVI)对草地AGB解释能力较强(40%)。(2)基于遥感因子构建的梯度增强回归树模型测试集R²为0.57。分别添加气象、地形信息,模型对草地AGB的估测准确性有所提升,测试R²为0.62和0.63。(3)基于气象、地形和遥感因子的多因素估测模型能够提高草地AGB估测精度,经递归特征消除法优选后,基于13个特征变量的梯度增强回归树模型拟合效果最好(训练数据集R²=0.79,RMSE=43.42 g/m²,P<0.01;测试数据集R²=0.66,RMSE=53.64 g/m²,P<0.01),可以解释66%草地AGB变化情况。(4)2010年青藏高原平均AGB为94.58 g/m²,2015年93.63 g/m²,2020年100.78 g/m²。青藏高原西北部草地AGB较低,东南部草地AGB较高,整体呈现自西北向东南逐渐增加的分布格局。研究结果为准确估算青藏高原草地产草量和碳储量等研究提供重要参考。     

Remote sensing of chlorophyll-a concentration for drinking water source using genetic algorithms (GA)-partial least square (PLS) modeling

Accurate estimation of phytoplankton chlorophyll-a (Chl-a) concentration in turbid waters through remote sensing is a challenge due to the optical complexity of water constituents. Reflectance spectra and concurrent water quality parameters of 225 samples across the Shitoukoumen Reservoir, the drinking water resource for Changchun City, were used to retrieve Chl-a concentration with high total suspended matter (TSM) during 2006–2008. A combination of genetic algorithms and partial least square (GA-PLS) model was established for Chl-a retrieval through GA to select sensitive spectral variables and PLS for regression. To compare GA-PLS performances, the widely accepted three-band algorithm was implemented for Chl-a concentration estimation. Both GA-PLS and the three-band algorithm have stable performance for the aggregated dataset (R² = 0.85 and 0.81; RPD = 3.95 and 3.61; relative RMSE = 31.7% and 34.2%), with the GA-PLS model performing marginally better. The temporal transferability of the models was validated with the dataset collected in 2006 and 2007 respectively as independent dataset, showing that GA-PLS outperformed the three-band algorithm. Our result also indicated that relative error [(Chl-a_predicted − Chl-a_measured) / Chl-a_measured] showed good linear relation to TSM: Chl-a ratio (R² = 0.84), which implied that TSM concentration exerted significant impact on the accuracy of Chl-a estimation in this case study. As the results were derived from a large number of samples representing a wide range of spatiotemporal variations of pigment under TSM (3.7–472.8 mg/L) concentration influence, the GA-PLS model has great potential for Chl-a estimation for inland waters with similar backgrounds. Nevertheless, the three-band algorithm also has its own merit considering its simplicity for implementation.     

花期苹果树冠氮素营养状况的卫星遥感反演

以山东栖霞为研究区,基于TM和ALOS影像获取花期苹果树的冠层反演反射率,结合实测反射率,构建并筛选氮素敏感光谱指数,以敏感光谱指数为自变量,建立氮素反演模型,利用精度最高模型进行空间反演.结果表明： 光谱指数与氮素营养相关性为：冠层＞叶＞花,敏感指数构成以绿、红、近红外波段为主;反演模型精度为：支持向量机回归＞逐步回归＞单变量回归;基于不同影像的反演结果近似,叶N含量均以3～4等(27～33 g·kg^-1)为主,冠N指标均以2～4等(TM: 38～47 g·kg^-1; ALOS: 32～41 g·kg^-1)为主;基于不同影像的空间布局亦类似,研究区北部和南部的营养水平高于中部,叶N和冠N高等级区域位于西北部的苏家店镇和松山街道、东北部的臧家庄镇和亭口镇、南部的蛇窝泊镇等,与苹果生产重点镇布局一致.此研究为果树营养状况的宏观数据获取提供了可行方法,也可为其他类似遥感反演提供借鉴.     

Monitoring landscape fragmentation and aboveground biomass estimation in Can Gio Mangrove Biosphere Reserve over the past 20 years

Over the past 20 years, the mangrove landscape of Can Gio Mangrove Biosphere Reserve (MBR) has undergone drastic changes in space and time. However, we know very little about changes in mangrove landscape model characteristics from analysis of different aspects based on landscape fragmentation. In the present study, the temporal and spatial changes of landscape pattern of land use/land cover (LULC) over the past 20 years in Can Gio Mangrove Biosphere Reserve (MBR), southern Vietnam were analyzed based on remote sensing data, with high classification accuracy (overall accuracy >85%, Kappa >0.8). The present study selected representative landscape indexes and built an integrated landscape index to examine the spatial-temporal changes of landscape patterns. Overall, over the past 20 years, the degree of fragmentation has gradually increased, mainly occurring in the transition zone of MBR. These changes are intended to reflect the significant temporal variation of the MBR, where the ecosystem is strongly disturbed by the intensity of human activities. We then investigate the effectiveness of principal component analysis (PCA)-based machine learning techniques in estimating the mangrove AGB, and applying landscape indices to assess impacts in Can Gio MBR. It reveals that the ANN model obtained the highest prediction accuracy (R²_train = 0.785), followed by GPR (R²_train = 0.703), and SVM (R²_train = 0.671). As a result of applying the ANN model, the predicted mangrove AGB in 2000 and 2020 in the study site ranged from 6.531 to 368.163 Mg ha⁻¹, and 13.749 to 320.295 Mg ha⁻¹, respectively. These results support the application of the model as a tool to support LULC management and protection in the study site, and to contribute insights into the future mangrove research in other regions of the world.     

Discriminating and mapping the C3 and C4 composition of grasslands in the northern Great Plains,USA

There is uncertainty about the extent and distribution of grasslands following the C3 and C4 photosynthetic pathways. Since these grasses have an asynchronous seasonal profile it should be possible to estimate and map the C3–C4 composition of grasslands from multi-temporal remote sensing imagery. This potential was evaluated using 30 weekly composite MERIS MTCI images for South Dakota, USA. Derived relationships between the remotely sensed response and composition of grasslands were significant, with R² ∼ 0.6. It also appears possible to map broad classes of grassland composition, with a three class (high, medium and low C3 cover) classification having an accuracy of 77.8%.     

Comparative regional-scale soil salinity assessment with near-ground apparent electrical conductivity and remote sensing canopy reflectance

Soil salinity is recognized worldwide as a major threat to agriculture, particularly in arid and semi-arid regions. Producers and decision makers need updated and accurate maps of salinity in agronomically and environmentally relevant ranges (i.e., <20 dS m⁻¹, when salinity is measured as electrical conductivity of the saturation extract, EC_e). State-of-the-art approaches for creating accurate EC_e maps beyond field scale (i.e., 1 km²) include: (i) Analysis Of Covariance (ANOCOVA) of near-ground measurements of apparent soil electrical conductivity (EC_a) and (ii) regression modeling of multi-year remote sensing canopy reflectance and other co-variates (e.g., crop type, annual rainfall). This study presents a comparison of the two approaches to establish their viability and utility. The approaches were tested using 22 fields (total 542 ha) located in California’s western San Joaquin Valley. In 2013 EC_a-directed soil sampling resulted in the collection of 267 soil samples across the 22 fields, which were analyzed for EC_e, ranging from 0 to 38.6 dS m⁻¹. The ANOCOVA EC_a-EC_e model returned a coefficient of determination (R²) of 0.87 and root mean square prediction error (RMSPE) of 3.05 dS m⁻¹. For the remote sensing approach seven years (2007–2013) of Landsat 7 reflectance were considered. The remote sensing salinity model had R² = 0.73 and RMSPE = 3.63 dS m⁻¹. The robustness of the models was tested with a leave-one-field-out (lofo) cross-validation to assure maximum independence between training and validation datasets. For the ANOCOVA model, lofo cross-validation provided a range of scenarios in terms of RMSPE. The worst, median, and best fit scenarios provided global cross-validation R² of 0.52, 0.80, and 0.81, respectively. The lofo cross-validation for the remote sensing approach returned a R² of 0.65. The ANOCOVA approach performs particularly well at EC_e values <10 dS m⁻¹, but requires extensive field work. Field work is reduced considerably with the remote sensing approach, but due to the larger errors at low EC_e values, the methodology is less suitable for crop selection, and other practices that require accurate knowledge of salinity variation within a field, making it more useful for assessing trends in salinity across a regional scale. The two models proved to be viable solutions at large spatial scales, with the ANOCOVA approach more appropriate for multiple-field to landscape scales (1–10 km²) and the remote sensing approach best for landscape to regional scales (>10 km²).     

Improving and evaluating the soil cover indicator for agricultural land in Canada

A soil cover days (SCD) model has been developed by Agriculture and Agri-Food Canada for use as an agri-environmental indicator to monitor the relationship between agricultural production activities and agri-environmental quality. The SCD indicator integrates information on crops, soils, climate, and field activities to estimate the total equivalent number of days that agricultural soils are covered by crop canopy, crop residue and snow in a given year. Daily cover fractions of plant and residue for a given crop in an ecoregion are simulated using typical crop calendar and field management practices, and the equivalent number of days that soil is covered by snow in winter is derived from long term climate normals. The equivalent SCD for a spatial unit is then derived as the area-weighted sum of different crops and different management practices within the unit. This paper presents the SCD framework, details an assessment of the accuracy of the model and outlines future improvements. Annual snow days derived from 30-year climate normals as used in the model was strongly correlated (excluding mountain areas) with that derived from satellite data (R² = 0.45, n = 48), even though the remote sensing product showed significant temporal and spatial variability. Crop residue fraction estimated by the model was strongly correlated with field data collected over major crop areas and crop types (R² = 0.74, n = 55), and modelled plant cover fraction was well correlated with that derived from remote sensing data (R² = 0.57, n = 57). Large discrepancies were observed for some samples due to deviation of the actual crop calendar from that estimated using climate normals. National map showing the change in the indicator from 1981 to 2011 reveals changes in crop and residue management practices.     

Modeling Soil Organic Carbon at Regional Scale by Combining Multi-Spectral Images with Laboratory Spectra

There is a great challenge in combining soil proximal spectra and remote sensing spectra to improve the accuracy of soil organic carbon (SOC) models. This is primarily because mixing of spectral data from different sources and technologies to improve soil models is still in its infancy. The first objective of this study was to integrate information of SOC derived from visible near-infrared reflectance (Vis-NIR) spectra in the laboratory with remote sensing (RS) images to improve predictions of topsoil SOC in the Skjern river catchment, Denmark. The second objective was to improve SOC prediction results by separately modeling uplands and wetlands. A total of 328 topsoil samples were collected and analyzed for SOC. Satellite Pour l’Observation de la Terre (SPOT5), Landsat Data Continuity Mission (Landsat 8) images, laboratory Vis-NIR and other ancillary environmental data including terrain parameters and soil maps were compiled to predict topsoil SOC using Cubist regression and Bayesian kriging. The results showed that the model developed from RS data, ancillary environmental data and laboratory spectral data yielded a lower root mean square error (RMSE) (2.8%) and higher R² (0.59) than the model developed from only RS data and ancillary environmental data (RMSE: 3.6%, R²: 0.46). Plant-available water (PAW) was the most important predictor for all the models because of its close relationship with soil organic matter content. Moreover, vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), were very important predictors in SOC spatial models. Furthermore, the ‘upland model’ was able to more accurately predict SOC compared with the ‘upland & wetland model’. However, the separately calibrated ‘upland and wetland model’ did not improve the prediction accuracy for wetland sites, since it was not possible to adequately discriminate the vegetation in the RS summer images. We conclude that laboratory Vis-NIR spectroscopy adds critical information that significantly improves the prediction accuracy of SOC compared to using RS data alone. We recommend the incorporation of laboratory spectra with RS data and other environmental data to improve soil spatial modeling and digital soil mapping (DSM).     

Assessing the impact of ecological security and forest fire susceptibility on carbon stocks in Bo Trach district,Quang Binh province,Vietnam

An assessment index of landscape ecological security (LES), different from other ecological models namely Pressure-State-Response (PSR), will be more accurate to capture temporal and geographic changes in landscape as well as ecosystem resilience and resistance to interference. In the present study, the impact of ecological security and forest fires on the carbon stock of forests in Bo Trach district, Quang Binh province, central Vietnam was evaluated and analyzed based on remote sensing data and a hybrid model of ant colony optimization (ACO) and neuro-fuzzy system (NFS). The present study indicated that forest fires are generally high throughout the study area, concentrating on areas exposed to and affected by human reclamation and production activities. The artificial neural network (ANN) model based on principal component analysis (PCA) combined Sentinel-1A data performed a higher prediction accuracy (R² = 0.74), being much greater than biomass estimation using optical data. It reveals that there is a reliability in estimating the aboveground carbon stocks (AGCs) from the aboveground biomass (AGB). The calculated data suggest the AGCs in the study area is high, but these parameters will loss severely in the coming years due to the nature and humans impacts. These results show that utilizing remote sensing combined with PCA-ANN model would have increased the accuracy of forest fire susceptibility, and detected assessment of the LES and forest fires on the AGCs. The above obtainings supply helpful information for managers and forest rangers to guard the forests in the study area better, and to limit human encroachment, thereby offering actions that contribute to sustainable development.     

联合GF-6和Sentinel-2红边波段的森林地上生物量反演

光谱反射率能反映地物差异,是森林地上生物量（Aboveground Biomass,AGB）遥感反演的理论基础。红边波段处于近红外与红光波段交界处快速变化的区域,能对植被冠层结构和叶绿素含量的微小变化做出快速反应,对植被生长状况较敏感。研究以GF-6和Sentinel-2多光谱影像作为数据源,结合野外调查AGB数据,构建落叶松和樟子松AGB线性和非线性估测模型,通过比较模型精度选择最优模型进行森林AGB反演和空间分布制图。结果表明：GF-6和Sentinel-2影像红边波段反射率与落叶松、樟子松AGB均呈显著相关（P<0.05）,红边波段对AGB估测较敏感。多变量估测模型整体估测效果优于单变量模型,所有模型中多元线性回归模型取得了最优的决定系数（落叶松R²=0.66,樟子松R²=0.65）和最低的均方根误差（落叶松RMSE=31.45 t/hm²,樟子松RMSE=54.77 t/hm²）。相比单个数据源,联合GF-6和Sentinel-2影像构建的多元线性回归模型估测效果得到了显著提升,模型RMSE对于落叶松和樟子松AGB估测分别最大降低了22.9%和11.2%。增加红边波段进行AGB估测能显著提高模型估测精度,三组数据源分别加入红边波段信息后进行建模,模型RMSE得到了显著降低。GF-6拥有800 km观测幅宽和高效的重访周期,可以快速地提供大尺度时间序列数据,在森林地上生物量反演和动态监测方面有着很大潜力。     

Machine learning approach to predict terrestrial gross primary productivity using topographical and remote sensing data

Gross Primary Productivity (GPP) is the amount of sequestered CO₂ during plant photosynthesis. GPP is an important indicator of ecosystem health in various ecologies and to assess climate change. The objective of the present work is to propose a machine learning based GPP estimation model using remote sensing (RS) data in combination with meteorological data (MET) and topographical data (TOPO) for prediction of GPP, which can be upscaled in temporal and spatial resolution. Random Forest Regression (RFR) is proposed for this using the Fluxnet2015 GPP dataset for the Australian region. This model has attained a very high accuracy with an R² value of 0.82, as estimated by 10-fold cross-validation. The model has been compared with state-of-the-art machine learning models and found to be performing better than others. Different feature sets like MET-features and TOPO-features were evaluated in combination with RS-features. The results exhibited that the RFR model performed better when MET and TOPO features are combined with RS-features. GPP prediction for the year 2014, in 8 days temporal and 500m  spatial resolution for the Australian region for different plant function types is demonstrated using the proposed model and produced very high value of R² (0.84), when compared to ground truth. Thus, the proposed approach of the RFR model for GPP estimation showed significant improvement in regional carbon cycle studies and can also be employed for simulating GPP for the future under different climate scenarios.     

Improved retrieval of Secchi depth for optically-complex waters using remote sensing data

Water transparency is one of the ecological indicators for describing water quality and the underwater light field which determines its productivity. In the European Water Framework Directive (WFD) as well as in the European Marine Strategy Framework Directive (MSFD) water transparency is used for ecological status classification of inland, coastal and open sea waters and it is regarded as an indicator for eutrophication in Baltic Sea management (HELCOM, 2007). We developed and compared different empirical and semi-analytical algorithms for lakes and coastal Nordic waters to retrieve Secchi depth (Z_SD) from remote sensing data (MERIS, 300 m resolution). The algorithms were developed in water bodies with high coloured dissolved organic matter absorption (a_CDOM(442) ranging 1.7–4.0 m⁻¹), Chl a concentration (0.5–73 mg m⁻³) and total suspended matter (0.7–37.5 g m⁻³) and validated against an independent data set over inland and coastal waters (0.6 m < Z_SD < 14.8 m). The results indicate that for empirical algorithms, using longer wavelengths in the visible spectrum as a reference band decreases the RMSE and increases the coefficient of determination (R²). The accuracy increased (R² = 0.75, RMSE = 1.33 m, n = 134) when Z_SD was retrieved via an empirical relationship between Z_SD and K_d(490). The best agreement with in situ data was attained when Z_SD was calculated via both the diffuse and the beam attenuation coefficient (R² = 0.89, RMSE = 0.77 m, n = 89). The results demonstrate that transparency can be retrieved with high accuracy over various optical water types by the means of ocean color remote sensing, improving both the spatial and temporal coverage. The satellite derived Z_SD product could be therefore used as an additional source of information for WFD and MSFD reporting purposes.     

遥感GPP模型在中亚干旱区4个典型生态系统的适用性评价

总初级生产力(GPP)是全球生态系统碳循环的重要组成部分,对全球气候变化有重要影响。目前有多种遥感模型可以模拟总初级生产力,比较不同遥感模型在中亚干旱区上的适用性对推进全球干旱区碳收支估算具有重要意义。基于涡度协相关技术观测的四个地面站数据验证MOD17、VODCA2、VPM、TG、SANIRv五种模型的模拟精度。结果表明：(1)基于光能利用率理论的MOD17、VPM模型模拟咸海荒漠植被和阜康荒漠植被GPP的精度最高(R²分别为0.52和0.80),但在模拟草地、农田生态系统生产力时存在较明显的低估(RE>20%);基于植被指数的遥感模型TG模型、SANIRv模型模拟巴尔喀什湖草地生态系统和乌兰乌苏农田生态系统GPP的精度最高(R²分别为0.91和0.81),同时模拟值与实测值的相对误差也较低;基于微波的VODCA2模型模拟各生态系统生产力的效果最差。(2)水分亏缺是限制植被GPP的主要因素,因此是否合理考虑水分胁迫是影响GPP模型在中亚干旱区适用性的重要因素。研究揭示了遥感GPP模型在中亚干旱区的应用潜力,为推进全球植被碳通量的准确估...     

Optimization of Cd2+ removal by the cyanobacterium Synechocystis pevalekii using the response surface methodology

Response surface methodology (RSM) has been used to optimize the critical parameters responsible for higher Cd²⁺ removal by a unicellular cyanobacterium Synechocystis pevalekii. A three-level Box–Behnken factorial design was used to optimize pH, biomass and metal concentration for Cd²⁺ removal. A coefficient of determination (R²) value (0.99), model F-value (86.40) and its low p-value (F < 0.0001) along with lower value of coefficient of variation (5.61%) indicated the fitness of response surface quadratic model during the present study. At optimum pH (6.48), biomass concentration (0.25 mg protein ml^?1) and metal concentration (5 μg ml^?1) the model predicted 4.29 μg ml^?1 Cd²⁺ removal and experimentally, 4.27 μg ml^?1 Cd²⁺ removal was obtained.     

Estimating the spatial pattern of soil respiration in Tibetan alpine grasslands using Landsat TM images and MODIS data

Monitoring soil respiration (R_s) at regional scales using images from operational satellites remains a challenge because of the problem in scaling local R_s to the regional scales. In this study, we estimated the spatial distribution of R_s in the Tibetan alpine grasslands as a product of vegetation index (VI). Three kinds of vegetation indices (VIs), that is, normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and modified soil adjusted vegetation index (MSAVI), derived from Landsat Thematic Mapper (TM) and Moderate-resolution Imaging Spectroradiometer (MODIS) surface reflectance product were selected to test our method. Different statistical models were used to analyze the relationships among the three VIs and R_s. The results showed that, based on the remote sensing data from either MODIS or Landsat TM, exponential function was the optimal fit function for describing the relationships among VIs and R_s during the peak growing season of alpine grasslands. Additionally, NDVI consistently showed higher explanation capacity for the spatial variation in R_s than EVI and MSAVI. Thus, we used the exponential function of TM-based NDVI as the R_s predictor model. Since it is difficult to achieve full spatial coverage of the entire study area with Landsat TM images only, we used the MODIS 8-day composite images to obtain the spatial extrapolation of plot-level R_s after converting the NDVI_MODIS into its corresponding NDVI_TM. The performance of the R_s predictor model was validated by comparing it with the field measured R_s using an independent dataset. The TM-calibrated MODIS-estimated R_s was within an accuracy of field measured R_s with R² of 0.78 and root mean square error of 1.45 gC m⁻² d⁻¹. At the peak growing season of alpine grasslands, R_s was generally much higher in the southeastern part of the Tibetan Plateau and gradually decreased toward the northwestern part. Satellite remote sensing demonstrated the potential for the large scale mapping of R_s in this study.     

High resolution retrieval of leaf chlorophyll content over Himalayan pine forest using Visible/IR sensors mounted on UAV and radiative transfer model

Forests play an essential role towards net primary productivity, biological cycles and provide habitat to flora & fauna. To monitor key physiological activities in forest canopies such as photosynthesis, respiration, transpiration, spatially-explicit and precise information of the biochemical (biological) variables such as Leaf Chlorophyll Content (LCC) is required. While lookup-table (LUT)-based Radiative Transfer Model (RTM) inversion against optical remote sensing imagery is regarded as a physically sound and robust approach for retrieving biochemical and biophysical variables, regularization procedures are required to offset the problem of ill-posedness. To optimize the RTM inversion of LCC over a sub-tropical pine forest plantation, in the Western Himalaya, we investigated the role of: (1) cost functions (CFs), (2) added noise, and (3) multiple finest solutions in LUT inversion. Principal CFs were evaluated belonging to three categories: information measures, M-estimates, and minimal contrast approaches. The inversion approaches were applied to a LUT produced by the coupled leaf-canopy model known as PROSAIL RTM and tested in contrast field spectral data obtained from reflectance data derived from UAV (Unmanned Aerial Vehicle) images taken over the canopies of covered pine forests. The Bhattacharyya divergence, an information measure, outperformed all other CFs in LCC inversion, with R² of 0.94, RMSE of 6.20 μg/cm² and NRMSE of 12.27% during the validation. The optimized inversion strategy was subsequently applied to a UAV-acquired multispectral image at an 8.2 cm pixel resolution for detailed landscape forest LCC mapping. The associated residuals as provided by the LUT-based inversion provided insights in the spatial consistency of the LCC map.     

基于多源遥感数据的大豆叶面积指数估测精度对比

近年来遥感技术的革新促使遥感源越来越丰富.为分析多源遥感数据的叶面积指数(LAI)估测精度,本文以大豆为研究对象,利用比值植被指数(RVI)、归一化植被指数(NDVI)、土壤调整植被指数(SAVI)、差值植被指数(DVI)、三角植被指数(TVI)5种植被指数,结合地面实测LAI构建经验回归模型,比较3类遥感数据(地面高光谱数据、无人机多光谱影像以及高分一号WFV影像)对大豆LAI的估测能力,并从传感器几何位置和光谱响应特性以及像元空间分辨率三方面分析讨论了3类遥感数据的LAI反演差异.结果表明： 地面高光谱数据模型和无人机多光谱数据模型都可以准确预测大豆LAI(在α=0.01显著水平下,R²均>0.69,RMSE均<0.40);地面高光谱RVI对数模型的LAI预测能力优于无人机多光谱NDVI线性模型,但两者差异不大(E_A相差0.3%,R²相差0.04,RMSE相差0.006);高分一号WFV数据模型对研究区内大豆LAI的预测效果不理想(R²<0.30,RMSE>0.70).针对星、机、地三类遥感信息源,地面高光谱数据在反演LAI方面较传统多光谱数据有优势但不突出;16 m空间分辨率的高分一号WFV影像无法满足田块尺度作物长势监测的需求;在保证获得高精度大豆LAI预测值和高工作效率的前提条件下,基于无人机遥感的农情信息获取技术不失为一种最佳试验方案.在当今可用遥感信息源越来越多的情况下,农业无人机遥感信息可成为指导田块精细尺度作物管理的重要依据,为精准农业研究提供更科学准确的信息.