Assessing the ground data requirements for regional scale remote sensing of tropical forest biophysical properties

The use of remotely sensed data to estimate terrestrial properties usually involves the acquisition of ground data. Remotely sensed data are being applied to ever larger areas and the acquisition and use of ground data, being so expensive, requires optimization. This paper investigates a sampling strategy that has already been used to acquire ground data in support of National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR) imagery of approximately 18 000 km² of Cameroonian forest and attempts to validate both the strategy and the use of the ground data in regression modelling. Specifically, a geostatistical approach was used to quantify the variability in the scene, the precision of the ground data, the benefits of twostage sampling and the errors associated with regression modelling and prediction.     

Optimal spatial sampling techniques for ground truth data in microwave remote sensing of soil moisture

Microwave remote sensing of soil moisture is currently being explored by a series of both active and passive experiments with the sensor output then related to soil moisture laboratory measurements made on field-collected samples taken at the time of microwave data acquisition. In addition to diurnal variation, soil moisture varies widely with surface location and depth; furthermore, the cost of sample extraction increases markedly with depth. Therefore it is desirable to identify sampling techniques which give acceptable statistical validity while minimizing the effort involved in sample extraction. Data from an extensive soil sample collection program carried out in April, 1976 near Perry-Topeka, Kansas were used as input to five statistical sampling tests based on both simple random sampling and stratified sampling. In addition to the relation between desired sample size and depth, the tests were applied to various field cell sizes corresponding to the resolution cell of the microwave remote sensor; field cells ranging from 2.5 to 40 acres in size were considered, and depths to 45 cm were included. If the total number of samples taken during a ground truth mission can be prespecified, then stratified sampling based on optimal allocation is to be preferred; otherwise, simple random sampling should be used. As an example, in the top 0–1 cm layer of a 20-acre field, 35 soil samples would be required using simple random sampling whereas only 19 samples would be required using optimal allocation stratified sampling. This reduction in the number of samples is a consequence of the higher, weighting assigned to the surface layers which exhibit the greatest soil moisture variability with spatial position.     

Estimation of net primary productivity by integrating remote sensing data with an ecosystem model

This paper describes a method for integrating leaf area index (LAI) derived from remote sensing data with an ecosystem model for accurate estimation of net primary productivity (NPP). The ecosystem model used in this study was Sim-CYCLE, with which LAI retrieved from the data acquired by MODIS sensor (MODIS-LAI) was integrated. Global annual NPP was estimated as 59.6 Gt C year⁻¹ by MOD-Sim-CYCLE (Sim-CYCLE after integration of MODIS-LAI), whereas it was 62.7 Gt C year⁻¹ in case of Sim-CYCLE for the year 2001. Both models predicted highest NPP around the equator with another smaller peak occurring around 60°N. These two regions represented the tropical and boreal forests biomes, respectively. The NPP estimated by MOD-Sim-CYCLE exceeded the NPP estimated by Sim-CYCLE in these two regions. Other than the tropical and boreal forests biomes, NPP values estimated by the MOD-Sim-CYCLE were typically lower than Sim-CYCLE across the latitudes. Validations of results in Australia and USA showed that MOD-Sim-CYCLE estimated NPP more accurately than Sim-CYCLE. Our results demonstrate the utility of combining satellite-observation with an ecosystem process model to achieve improved accuracy in estimates and monitoring global net primary productivity.     

Upscaling of sparse in situ soil moisture observations by integrating auxiliary information from remote sensing

Upscaling of sparse in situ soil moisture (SM) observations is essential for the validation of current and upcoming space-borne SM retrievals, and the successful application of SM observations in hydrological models or data assimilation. In this study, we construct a novel method based on Bayesian data fusion to upscale in situ SM observations to the coarse scale of microwave remote sensing. In the framework of Bayesian theory, the valuable auxiliary information obtained in Moderate Resolution Imaging Spectroradiometer (MODIS) apparent thermal inertia (ATI) is integrated into the upscaling process. The method is validated using SM wireless sensor network data in the Tibetan plateau, which covers an area of approximately 30 × 30 km² with 20 in situ stations. Results confirm that the upscaled SM using the method with randomly selected three stations from the 20 stations is extremely close to the mean of the 20 SMs. The mean root mean square error (RMSE) between the upscaled SM and the mean of the 20 in situ SMs was 0.02 m³ m^?3, and the max RMSE was less than 0.05 m³ m^?3. Furthermore, the sensitivity of the upscaling accuracy to the number of in situ observations is discussed. When the number of in situ observations is greater than nine, the increasing accuracy of the Bayesian method is limited by the uncertainty in the ATI of the remote sensing.     

土壤水分变化量遥感反演算法

为获得作物生长发育期内任意时间段的土壤水分变化量空间分布,基于叶面积指数和生物量的土壤水分变化量遥感反演模型,利用ArcGIS Engine 9.3平台结合GDAL图像读写库设计和实现了相关算法,形成了从遥感影像数据到土壤水分变化量产品的处理流程。经研究区域的Landsat TM数据测试,反演算法运行稳定且计算结果符合实际,可为农业生产提供决策依据,为补充性灌溉提供指导。     

Progress in the remote sensing of land surface temperature and ground emissivity using NOAA-AVHRR data

The extensive requirement of landsurface temperature (LST) for environmental studies and management activities of the Earth's resources has made the remote sensing of LST an important academic topic during the last two decades. Many studies have been devoted to establishing the methodology for the retrieval of LST from channels 4 and 5 of Advanced Very High Resolution Radiometer (AVHRR) data. Various split-window algorithms have been reviewed and compared in the literature to understand their differences. Different algorithms differ in both their forms and the calculation of their coefficients. The most popular form of split-window algorithm is T _s=T ₄+A(T ₄-T ₅)+B , where T _s is land surface temperature, T ₄ and T ₅ are brightness temperatures of AVHRR channels 4 and 5, A and B are coefficients in relation to atmospheric effects, viewing angle and ground emissivity. For the actual determination of the coefficients, no matter the complexity of their calculation formulae in various algorithms, only two ways are practically applicable, due tothe unavailability of many required data on atmospheric conditions and ground emissivities in situ satellite pass. Ground data measurements can be used to calibrate the brightness temperature obtained by remote sensing into the actual LST through regression analysis on a sample representing the studied region. The other way is standard atmospheric profile simulationusing computer software such as LOWTRAN7. Ground emissivity has a considerable effect on the accuracy of retrieving LST from remote sensing data. Generally, it is rational to assume an emissivity of 0.96 for most ground surfaces. However, the difference of ground emissivity between channels 4 and 5 also has a significant impact on the accuracy of LST retrieval. By combining the data of AVHRR channels 3, 4 and 5, the difference can be directly calculated from remote sensing data. Therefore, much more study is required on how to accurately determine the coefficients of split-window algorithms in the application of remote sensing to examine LST change and distribution in the real world.     

Land tessellation effects in mapping agricultural areas by remote sensing at field level

While mapping agricultural areas by remote sensing, it is quite common to operate at cadastral parcel level. Unfortunately, this land tessellation is merely administrative: a single parcel can, in fact, be made of differently managed parts whose spectral properties can be significantly different, being often different their content. In this situation, approaches that aggregate spectral signals of pixels belonging to the same parcel to investigate their average behaviour can generate misleading results. In this work, we evaluated how different field tessellation schemes can condition the interpretation of the spectral behaviour of crops with special concern on time series of NDVI (normalized difference vegetation index) and NDWI (normalized difference water index) spectral indices, which are assumed as proxies of plant vigour and crop/soil water content, respectively. The study relies on Sentinel 2 and Landsat 8 data imaging a rice-cultivated area sited in Piemonte (NW Italy). Two reference land tessellation geometries were taken into account: (a) the local cadastral map (purely administrative land division criterion) and (b) a map obtained by image segmentation of the NDVI time series (purely spectral land division criterion). After signal aggregation, some statistics were therefore computed to test differences both in time (within the same parcel along its temporal profile) and in space (within the same image at different positions at the same time). Results obtained exploring the rice growing season 2016 showed that (a) in 23% (70% at 1 sigma) and 27% (70% at 1 sigma) of segments (respectively for NDVI and NDWI) spectral differences, averagely along the year, are significant, possibly leading to wrong interpretation of occurring dynamics in the area; (b) in rice-cultivated fields, spectral differences suffer from seasonality with a higher incidence in Spring, when rice agronomic phases are more dynamic and, in the meantime, critical for management.     

Estimation of regional terrestrial water cycle using multi-sensor remote sensing observations and data assimilation

An integrated data assimilation system is implemented over the Red-Arkansas river basin to estimate the regional scale terrestrial water cycle driven by multiple satellite remote sensing data. These satellite products include the Tropical Rainfall Measurement Mission (TRMM), TRMM Microwave Imager (TMI), and Moderate Resolution Imaging Spectroradiometer (MODIS). Also, a number of previously developed assimilation techniques, including the ensemble Kalman filter (EnKF), the particle filter (PF), the water balance constrainer, and the copula error model, and as well as physically based models, including the Variable Infiltration Capacity (VIC), the Land Surface Microwave Emission Model (LSMEM), and the Surface Energy Balance System (SEBS), are tested in the water budget estimation experiments. This remote sensing based water budget estimation study is evaluated using ground observations driven model simulations. It is found that the land surface model driven by the bias-corrected TRMM rainfall produces reasonable water cycle states and fluxes, and the estimates are moderately improved by assimilating TMI 10.67 GHz microwave brightness temperature measurements that provides information on the surface soil moisture state, while it remains challenging to improve the results by assimilating evapotranspiration estimated from satellite-based measurements.     

A GIS based analysis of data from Landsat TM,airborne geophysical measurements,and digital maps for geological remote sensing in the Stockholm region,Sweden

This study focuses on GIS-based integration of traditional remote sensing data (Landsat TM), geophysical data from airborne measurements (gamma radiation, magnetic and VLF) and ancillary data for geological studies. The test area, Stockholm region, is complex with a fragmented distribution of different land-cover types. Methods for estimation and correction of the influence of this on the measured gamma radiation are discussed. Image processing methods are tested on the geophysical data with the aim to increase the possibilities to extract useful geological information. An integrated analysis of the different data types is applied to find the mutual relationship as a base for a geological analysis. The ultimate application of these techniques should result in a more detailed and accurate geological interpretation.     

Synthesis of ground and remote sensing data for monitoring ecosystem functions in the Colorado River Delta, Mexico

The delta of the Colorado River in Mexico supports a rich mix of estuarine, wetland and riparian ecosystems that provide habitat for over 350 species of birds as well as fish, marine mammals, and other wildlife. An important part of the delta ecosystem is the riparian corridor, which is supported by agricultural return flows and waste spills of water originating in the U.S. and Mexico. These flows may be curtailed in the future due to climate change and changing land use practices (out-of-basin water transfers, increased agricultural efficiency, and more optimal management of dams) in the U.S. and Mexico, and resource managers need to monitor the effects of their water management practices on these ecosystems. We developed ground-validated, remote sensing methods to monitor the vegetation status, habitat value, and water use of wetland and riparian ecosystems using multi-temporal, multi-resolution images. The integrated methodology allowed us to project species composition, leaf area index, fractional cover, habitat value, and evapotranspiration over seasons and years throughout the delta, in response to variable water flows from the U.S. to Mexico. Waste spills of water from the U.S. have regenerated native cottonwood and willow trees in the riparian corridor and created backwater and marsh areas that support birds and other wildlife. However, the main source of water supporting the riparian vegetation is the regional aquifer recharged by underflow from U.S. and Mexico irrigation districts. Native trees have a short half-life in the riparian zone due to human-set fires and harvesting for timber. Active management, monitoring, and restoration programs are needed to maintain the habitat value of this ecosystem for the future.     

Laser-induced marine bioluminescence measurements and the potential for airborne remote sensing

Bioluminescence in cultures of the dinoflagellate Pyrocystis lunula at various temperatures were stimulated using a pulsed dye laser and Rhodamine 6G dye having an optimum lasing wavelength of $\text{586 ± 30}\text{nm}$. Following an intense “first flash” response, the flash intensity decayed in logarithmic fashion with successive laser shots. Samples pulsed to exhaustion were found to completely recover during the 12 h photophase. The total stimulable light (TSL) was calculated to be between $\text{4.5 × 10}{}^{\mathrm{?10}}$ J cell^?1 and $\text{38.5 × 10}{}^{\mathrm{?10}}$ J cell^?1. The time from stimulation to maximum light emission ($\text{t}{}_{\mathrm{m}}$) was found to vary with temperature logarithmically from approximately 11°C to 28°C. The corresponding regression equation was found to predict temperatures to within ±0.4°C. These results provide the basis for predicting the feasibility of an airborne laser transceiver for mapping the distribution of ocean bioluminescence. The potential exists for determining ocean surface/near surface temperature from measurements of the response pulse time parameters.     

Intercomparison of ozone models derived by remote and in situ sensing techniques with recent local measurements at middle latitudes

In the framework of the European Arctic Stratospheric Ozone Experiment (1991–1992) a scries of balloon ascents for ozone and temperature in situ measurements up to 35 km height have been performed at Athens, Greece (38° N, 24° E). This is the first time that such an intensive sounding campaign has been performed in Athens. The data collected for the vertical distribution of ozone and temperature have been compared with the satellite-derived reference models which provide the monthly latitudinal variations of vertical structure of both ozone and temperature. The comparison shows that at the middle stratosphere there is very good agreement between the Satellite Reference Model and the in situ ozone measurements. There is also very good agreement between the Satellite Reference Model and the in situ temperature measurements, thus confirming the recently published findings by Varotsos and Helmis.     

Water level estimation by remote sensing for the 2008 flooding of the Kosi River

Flood is a natural disaster which worsens when it is triggered by man-made constructions. This paper discusses one such flood event which occurred because of breach of a levee in the upper reach of the Kosi River in 2008, when floodwater spread over a large portion of the low-lying Ganga Plain of North Bihar, India. Here we have analysed a suite of space-based observations from radar altimetry, Moderate Resolution Imaging Spectroradiometer (MODIS) images, and Tropical Rainfall Measuring Mission (TRMM) precipitation data, together with in situ monthly precipitation data, with a main emphasis on the results from altimetry and MODIS data. A methodology to calculate water levels, using MODIS data and Envisat data together, is also discussed. Our analyses suggest a rise in water level of 1.0–1.4 m in the flooded region during the flood event and a maximum extent for the flooded area of around 2900 km². Analyses of TRMM precipitation data do not indicate any influence of high precipitation in the upper catchment of the Kosi Basin on river water feeding into the plain area after breaching of dam. However, heavy and prolonged precipitation was found downstream of the dam over the flooded area during the flood period.     

Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun,Korea

The aim of this study is to evaluate the hazard of landslides at Boun, Korea, using a Geographic Information System (GIS) and remote sensing. Landslide locations were identified in the Boun area from interpretation of aerial photographs and field surveys. The topographic, soil, forest, geologic, lineament and land cover data were collected, processed and constructed into a spatial database using GIS and remote sensing data. The factors that influence landslide occurrence, such as slope, aspect and curvature of the topography, were calculated from the topographic database. Texture, material, drainage and effective soil thickness were extracted from the soil database, and type, age, diameter and density of timber were extracted from the forest database. The lithology was extracted from the geological database and lineaments were detected from Indian Remote Sensing (IRS) satellite images. The land cover was classified based on the Landsat Thematic Mapper (TM) satellite image. Landslide hazard areas were analysed and mapped, using the landslide-occurrence factors, by the probability–likelihood ratio method. The results of the analysis were verified using actual landslide location data. The validation results showed satisfactory agreement between the hazard map and the existing data on landslide locations.     

Compiling soil maps on the basis of remotely-sensed data digital processing: soil interpretation

A method for compiling soil maps from the results of digital processing of remotely-senseddata is presented. The method is based on graphic analysis of mean digital values of clusters in the green-red and (green-red)–(NIR-red) projections in conjunction with visual analysis of the cluster map and spectral curves and their interpretation on the basis of thematic maps and ground data. The proposed technique allows interpretation of the soil types of arable lands with 50 per cent vegetation density and differentiation of the soils with regard to their water regime. The accuracy of the resulting soil map was evaluated as 0·82.     

基于正样本和未标记样本的遥感图像分类方法

传统分类器的构建需要正样本和负样本两类数据。在遥感影像分类中,常出现这样一类情形：感兴趣的地物只有一种。由于标记样本耗时耗力,未标记样本往往容易获取并且包含有用信息,鉴于此,提出了一种基于正样本和未标记样本的遥感图像分类方法（PUL）。首先,根据正样本固有特征并结合支持向量数据描述（SVDD）从未标记集筛选出可信正负样本,再将其从未标记集中剔除;接着将其带入SVM训练,根据未标记集在分类器中的表现设立阈值,再从未标记集中筛选出相对可靠的正负样本;最后是加权SVM（Weighted SVM）过程,初始正样本及提取出的可靠正负样本权重为1,SVM训练筛选出的样本权重范围0~1。为验证PUL的有效性,在遥感影像进行分类实验,并与单类支持向量机（OC-SVM）、高斯数据描述（GDD）、支持向量数据描述（SVDD）、有偏SVM（Biased SVM）以及多类SVM分类对比,实验结果表明PUL提高了分类效果,优于上述单类分类方法及多类SVM方法。     

Intercomparison of ground-based microwave remote sensing measurements of stratospheric ozone over the Mendoza region, Argentina with HALOE data

The Tropospheric Water Vapour and Stratospheric Ozone (TROPWA) project has measured ground-based stratospheric ozone by means of millimetre wave radiometry tuned at 142 GHz from 1993 to 2000 in Mendoza, Argentina. Additionally, tropospheric water vapour was measured using a 92-GHz radiometer. This paper presents the theoretical error analysis used to characterize the ozone instrument, and a comparative study of the retrieved profiles with the coincident measurements taken with different instruments. To evaluate and validate the retrieved stratospheric ozone profiles, we have used a set of ozone profiles measured with the Halogen Occultation Experiment (HALOE); while the water vapour data was calibrated against a set of 3-year-radiosounding-balloon data taken by the Argentine National Weather Service. This study also includes a comparison of individual ozone profiles measured using a second ground-based millimetre wave radiometer-spectrometer tuned at 276 GHz from the Max-Planck-Institut für Aeronomie (MPAE), Germany. During this particular campaign carried out in November 1994, the ground-based measurements were contrasted with two space-born experiments: the Millimetre Wave Atmospheric Sounder (MAS), flown in the NASA-ATLAS 3 mission and the above-mentioned HALOE.From the error analysis and the comparison tests, it follows that between 20 to 40 km the TROPWA instrument is able to retrieve ozone profiles with absolute errors varying from 10% to 20%, relative errors less than 5%, and with a height resolution, calculated as full width at half maximum (FWHM), varying from 5 to 11 km depending on the altitude. The major discrepancies between the different set of profiles are about +8% to −10% (+0.4 to −0.8 ppmv), mainly due to the coarser height resolution of our instrument.     

Improving the spatio-temporal distribution of surface solar radiation data by merging ground and satellite measurements

Appropriate information on solar resources is very important for a variety of technological areas, such as: agriculture, meteorology, forestry engineering, water resources and in particular in the designing and sizing of solar energy systems. However, the availability of observed solar radiation measurements has proven to be spatially and temporally inadequate for many applications. In this paper we propose to merge the global solar radiation measurements from the Royal Meteorological Institute of Belgium solar measurements network with the operationally derived surface incoming global short-wave radiation products from Meteosat Second Generation satellites imageries to improve the spatio-temporal resolution of the surface global solar radiation data over Belgium. We evaluate several merging methods with various degrees of complexity (from mean field bias correction to geostatistical merging techniques) together with interpolated ground measurements and satellite-derived values only. The performance of the different methods is assessed by leave-one-out cross-validation.