Land Cover Classification Products Research Articles

Seasonality and directionality effects on radar backscatter are key to identify mountain forest types with Sentinel-1 data

Systematic Sentinel-1 acquisitions provide an unprecedented stream of SAR data which allows to describe forest temporal dynamics in detail, a powerful tool for phenological studies and forest type classification. Several studies have explored the temporal variation of backscatter intensity in this context, but none considered that scattering directionality of canopies may vary. Said directionality is related to target-sensor geometry (incidence angle), forest height, and optical depth, associated with leaf dynamics. This study explicitly models backscatter dependance on incidence angle by fitting a regression model for each Sentinel-1 image and forest type. Residuals are accumulated across the time series and used to classify pixels into the most likely forest type using the smallest accumulated residual. This modelling and classification strategy has been applied over a North-South transect across the Carpathian Mountains, including forests with different physiognomies, from deciduous broadleaf forest, to mixed broadleaf-needleleaf and pure perennial needleleaf forests. These forests were classified with increasing detail, assessing the results against in-situ forest stand data and satellite-based land cover classification products (Copernicus Forest type layer). The accuracy of our classification was K > 0.8, OA > 90% when separating broadleaf from needleleaf forest types. The accuracy decreased (K > 0.6, OA > 79%) when also separating mixed forest types. Our results suggest that incorporating directional effects into classification models can improve SAR-based forest classification of temperate forest over mountainous terrain. Furthermore, models fitted between backscatter and incidence angle provide an estimate of n, a parameter related to optical depth that has been shown to vary with leaf dynamics. ncould be used to improve image normalization in studies aiming at the estimation of biomass, or to aid the estimation of fast-changing parameters such as leaf area index or leaf moisture content.

A New and Automated Method for Improving Georeferencing in Nighttime Thermal ECOSTRESS Imagery.

Georeferencing accuracy plays a crucial role in providing high-quality ready-to-use remote sensing data. The georeferencing of nighttime thermal satellite imagery conducted by matching to a basemap is challenging due to the complexity of thermal radiation patterns in the diurnal cycle and the coarse resolution of thermal sensors in comparison to sensors used for imaging in the visual spectral range (which is typically used for creating basemaps). The presented paper introduces a novel approach for the improvement of the georeferencing of nighttime thermal ECOSTRESS imagery: an up-to-date reference is created for each to-be-georeferenced image, derived from land cover classification products. In the proposed method, edges of water bodies are used as matching objects, since water bodies exhibit a relatively high contrast with adjacent areas in nighttime thermal infrared imagery. The method was tested on imagery of the East African Rift and validated using manually set ground control check points. The results show that the proposed method improves the existing georeferencing of the tested ECOSTRESS images by 12.0 pixels on average. The strongest source of uncertainty for the proposed method is the accuracy of cloud masks because cloud edges can be mistaken for water body edges and included in fitting transformation parameters. The georeferencing improvement method is based on the physical properties of radiation for land masses and water bodies, which makes it potentially globally applicable, and is feasible to use with nighttime thermal infrared data from different sensors.

ANALYSIS OF QUALITY REVIEW OF LAND COVER CLASSIFICATION PRODUCT IN GEOGRAPHIC NATIONAL CONDITIONS MONITORING

Abstract. To the land cover classification product of geographic national conditions monitoring, how to scientifically and objectively inspect and evaluate the results is a problem that if quality review can play a key role in quality control. Based on the characteristics of the land cover classification product, it was carried out a quality inspection for important quality elements and key contents on the basis of quality acceptance to ensure the consistency and compliance of the national results. This paper put forward the content and method of review. The results over the years since the review showed that the classification accuracy was not only a difficulty but also a short board in quality control. Through summarizing the main problems of the land cover classification product found in the review, the causes were analyzed, and suggestions were put forward for production.

Developments in Land Use and Land Cover Classification Techniques in Remote Sensing: A Review

Studies on land use and land cover changes (LULCC) have been a great concern due to their contribution to the policies formulation and strategic plans in different areas and at different scales. The LULCC when intense and on a global scale can be catastrophic if not detected and monitored affecting the key aspects of the ecosystem’s functions. For decades, technological developments and tools of geographic information systems (GIS), remote sensing (RS) and machine learning (ML) since data acquisition, processing and results in diffusion have been investigated to access landscape conditions and hence, different land use and land cover classification systems have been performed at different levels. Providing coherent guidelines, based on literature review, to examine, evaluate and spread such conditions could be a rich contribution. Therefore, hundreds of relevant studies available in different databases (Science Direct, Scopus, Google Scholar) demonstrating advances achieved in local, regional and global land cover classification products at different spatial, spectral and temporal resolutions over the past decades were selected and investigated. This article aims to show the main tools, data, approaches applied for analysis, assessment, mapping and monitoring of LULCC and to investigate some associated challenges and limitations that may influence the performance of future works, through a progressive perspective. Based on this study, despite the advances archived in recent decades, issues related to multi-source, multi-temporal and multi-level analysis, robustness and quality, scalability need to be further studied as they constitute some of the main challenges for remote sensing.

基于SNIC-CNN-SVM模型的京津风沙源二期工程区土地利用/土地覆盖遥感识别研究

PDF HTML阅读 XML下载 导出引用 引用提醒 基于SNIC-CNN-SVM模型的京津风沙源二期工程区土地利用/土地覆盖遥感识别研究 DOI: 10.5846/stxb202109262707 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 中国林业科学研究院院基金重点项目（CAFYBB2019ZB004）；国家高分重大科技专项（21-Y30B02-9001-19/22-3） Remote sensing recognition of land use/land cover in the Beijing-Tianjin sandstorm source region based on SNIC-CNN-SVM model Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:稀疏植被覆盖（草地、沙地、戈壁）演变能够直接表征区域生态环境和人类活动的动态影响变化。但由于大尺度稀疏植被区一般都具有地理跨度大，景观结构复杂多样，破碎化程度高，现有地表覆盖分类产品针对性不足等问题，使得该区域内林草沙的遥感提取难度较大，精度普遍偏低，直接制约生态效应评价模型的应用效果。因此，以典型大尺度稀疏植被区--京津风沙源治理二期工程区为研究区，研建了SNIC-CNN-SVM （SCS）模型，实现了大尺度稀疏植被区林草沙典型要素的信息自动提取和主要土地利用/土地覆盖类型识别。研究结果表明：1）引入惩罚性机制优化后的SNIC分割算法，有效提升了稀疏植被区与沙地区的边界区分度，有助于分类精度的提升；2）基于改进SNIC-CNN-SVM模型方案的研究区总体分类精度达89.41%，较优化前提高了11.17%，特别是乔、灌、草、沙地和戈壁的分类识别精度显著提升，表明该优化方案在以研究区为代表的稀疏植被区域分类中具有较好的应用效果和推广价值；3）分类结果显示，2020年工程区草地面积最大，占到了一半以上（51.52%），沙地占比11.96%，稀疏植被覆盖（草地、沙地、戈壁）区域占比68.68%，表明工程区处在林地-稀疏植被-沙地的过渡地带，生态环境保护压力与防沙治沙形势依然严峻；4）近20年来，乔灌草等植被增加面积约占工程区20.64%，主要由沙化土地转化，沙化土地减少面积约占工程区的4.58%，表明研究区植被状况不断改善，实施的各项生态工程作用显著，能够更有效地服务于多维度生态系统服务功能评价。该研究以期能够为京津风沙源二期工程区的生态系统演变规律研究及生态工程评价等工作提供重要科学支撑。 Abstract:The evolution of sparse vegetation cover (grassland, sandy land, Gobi) can directly characterize the dynamic changes of the regional ecological environment and human activities. However, large-scale sparse vegetation areas generally have large geographic spans, complex and diverse landscape structures, high fragmentation, and insufficient pertinence of existing land cover classification products. These problems make it difficult to extract the forest, grass, and sand in this area by remote sensing, and the accuracy is generally low, which directly restrict the application results of the ecological effect evaluation model. Therefore, this study took the typical large-scale sparse vegetation area, the Beijing-Tianjin sandstorm source region (BTSSR) as the study area, and the SNIC-CNN-SVM (SCS) model is proposed. The forest, grass, sand element information is automatically extracted and the land use/land cover types are identified in the study area. The results show that:1) the SNIC segmentation algorithm was optimized by the penalty mechanism and can effectively increase the boundary discrimination between the sparse vegetation area and the sandy area, which is helpful to improve the classification accuracy; 2) the overall classification accuracy of the optimized model reached 89.41%, which is 11.17% higher than that before optimization, especially the classification and recognition accuracy of trees, shrubs, grasses, sandy land and Gobi is significantly improved, indicating that the optimization model has good application effect and promotion value in the classification of sparse vegetation areas represented by the study area; 3) in 2020, the grassland area in the study area was the largest, accounting for more than half (51.52%), sandy land accounted for 11.96%, and sparse vegetation coverage (grassland, sandy land, Gobi) accounted for 68.68%, indicating that the project area is in the transition zone of woodland-sparse vegetation-sandy land, and the pressure on ecological environment protection and the situation of desertification prevention and control are still severe; 4) in the past 20 years, the increased area of vegetation such as arbor, shrub and grass accounted for about 20.64% of the project, which is mainly derived from desertified land. The reduced area of desertified land accounted for about 4.58% of the project area, indicating that the vegetation conditions in the study area are continuously improving, and the implemented ecological projects have a significant effect. The method in this paper can be more effectively used for multi-dimensional ecosystem service function evaluation. This study is expected to provide important scientific support for the research on the evolution law of the ecosystem and the evaluation of ecological engineering in the BTSSR. 参考文献 相似文献 引证文献

QUALITY ASSESSMENT OF LAND COVER CLASSIFICATION PRODUCTS BASED ON THE FUZZY–AHP SYNTHESIS MODEL

Abstract. As one of the critical products National Geographic Conditions Monitoring Achievements (NGCMA), land cover dataset has great value for the practical needs of China natural resources investigation and monitoring. For providing This paper proposed a fuzzy-AHP synthesis model, for assessing the quality of land cover classification products, based on the current qualifications of inspection and acceptance as well the characteristics of the inspection process of this type of results. The model is practiced on a data set that is the inspection results from 27 surveying areas in northern China. By comparing the results to the results from minimum method (MinM) and weighted average methods (WAM), this study pay attention on giving the analysing of the reasonability and feasibility of this model on practical uses and to provide a technical reference for quality evaluation of the new-type of fundamental surveying and mapping products.

RESEARCH ON QUALITY CONTROL METHOD OF LAND COVER CLASSIFICATION DATA ORIENTED TO NATIONAL GEOGRAPHIC CONDITION MONITORING

Abstract. Aiming at the quality problems of the land cover classification data, such as missing update of changed spots, overrun of spot collection accuracy, incorrect spot classification, and inconsistencies between the filling of the change types of map spots and technical regulations, had been found during the quality inspection of the national geographic condition monitoring in China. This paper analyzed the key production factors, processes and causes that affect the process quality of the land cover classification data, and proposed the concept of the whole production quality control, and discussed the quality control measures to improve the land cover classification product. The research based on the new requirements of the whole-process quality control system in order to solve the quality problem-oriented production quality control method, could improve the quality of land cover classification product and provided reference for the quality control of the production process of related engineering projects in the future.

Improving the Accuracy of Remote Sensing Land Cover Classification by GEO-ECO Zoning Coupled with Geostatistical Simulation

Land cover products obtained from remote sensing image classification inevitably contain a large number of false classification or uncertain pixels because of spectral confusion, image resolution limitation, and ground object complexity. The confusion matrix used to evaluate the classification accuracy cannot reflect the spatial variation. The information provided to users of land cover products is incomplete and uncertain. In this study, a method is presented to evaluate and improve the accuracy of land cover classification products by coupling Geo-Eco zoning and Markov chain geoscience statistical simulation. Validation points collected from various sources are used in the model calculation and accuracy verification of results. The pre-classified image that needs to be improved and Geo-Eco zoning attribute data are used as auxiliary data for co-simulation. Results show that the accuracy of Globeland30 data can be improved by more than 10% by coupling Geo-Eco zoning and Markov chain geostatistical simulation.

Developing Long Time Series 1-km Land Cover Maps From 5-km AVHRR Data Using a Super-Resolution Method

Dynamic land cover (LC) information is an essential part of environmental and ecological research. Therefore, acquiring dynamic LC data with high spatial resolution has attracted a great deal of attention in the remote sensing community. Nevertheless, the high-temporal resolution satellite data tend to have a coarse spatial resolution, and satellite data with high temporal resolution are often relatively low. Obtaining LC with high spatiotemporal resolution is extremely challenging. The super-resolution method can help researchers achieve this goal, and the recently developed neural-network-based deep learning algorithms have great potential for use as an alternative solution. This study proposes a focal loss temporal convolutional long short-term memory (FL-T-ConvLSTM) model for super-resolution LC classification research. It first trains the deep FL-T-ConvLSTM network to establish a transformation between low-resolution quantitative remote sensing parameters and high-resolution quantitative remote sensing parameters and then engages in nonlinear mapping with a high-resolution LC map. A long-term series 1-km super-resolution LC classification model based on deep learning was established and applied to the Beijing-Tianjin-Hebei region. Based on this method, a long-term series of 1-km LC maps from 1982 to 2019 can be obtained. The test accuracy and field validation accuracy of the model reached 90.1% and 86.8% when using reliable test samples and field test samples, respectively. This study provides a method for obtaining high-resolution LC classification products from low-resolution quantitative remote-sensing products.

Analysis and application of interactive supervised classification in Broome County, NY

Land cover classification is essential in urban sprawl. It helps the urban planners and the policy makers to understand their urban communities. Interactive supervised classification (ISC) function in ArcMap can produce land cover classification map. Based on the analysis and comparison of the accuracy of ISC map and land cover classification products, GLC, GLOBC, and MCD, ISC map has the highest user’s accuracy in identifying developed urban area, forest, water body and crop/pasture, and highest overall accuracy, which provides efficient technical support for urban planning and decision-making.

Long Time Series Land Cover Classification in China from 1982 to 2015 Based on Bi-LSTM Deep Learning

Land cover classification data have a very important practical application value, and long time series land cover classification datasets are of great significance studying environmental changes, urban changes, land resource surveys, hydrology and ecology. At present, the starting point of continuous land cover classification products for many years is mostly after the year 2000, and there is a lack of long-term continuously annual land cover classification products before 2000. In this study, a long time series classification data extraction model is established using a bidirectional long-term and short-term memory network (Bi-LSTM). In the model, quantitative remote sensing products combined with DEM, nighttime lighting data, and latitude and longitude elevation data were used. We applied this model in China and obtained China’s 1982–2017 0.05° land cover classification product. The accuracy assessment results of the test data show that the overall accuracy is 84.2% and that the accuracies of wetland, water, glacier, tundra, city and bare soil reach 92.1%, 92.0%, 94.3%, 94.6% and 92.4%, respectively. For the first time, this study used a variety of long time series data, especially quantitative remote sensing products, for the classification of features. At the same time, it also acquired long time series land cover classification products, including those from the year 2000. This study provides new ideas for the establishment of higher-resolution long time series land cover classification products.

Urban Heat Island in Kathmandu, Nepal: Evaluating Relationship between NDVI and LST from 2000 to 2018

The term “urban heat island” (UHI) describes increased surface and atmospheric temperatures in an urban core relative to surrounding non-urbanized areas. Although the phenomenon has been studied to a great extent throughout the world, it is less understood for Kathmandu, Nepal. This study used the Moderate Resolution Imaging Spectro-radiometer (MODIS) 8-day product (MOD11A2) to evaluate land surface temperatures (LSTs), the MODIS-derived Normalized Difference Vegetation Index (NDVI) 16-day product (MOD13Q1) to quantify land surface characteristics, and the MODIS annual land cover classification product (MCD12Q1) to identify major land cover classes. We evaluated the spatial correlation between significant changes in LSTs and NDVI between 2000–2018. Overall, urban (permanently developed areas) LSTs were consistently greater than non-urban (forests and dynamic agriculture lands) LSTs; however, the rate of increase in temperature was higher outside the central Kathmandu developed urban area. Furthermore, significant changes in NDVI values over time were more widespread and not always spatially coincident with significant changes in LST values, particularly for forested land areas. These results provide insight into systematic planning of open and green areas, construction of new infrastructure in peripheral areas, and highlight the challenges in applying traditional UHI conceptual models to rapidly developing urban areas such as Kathmandu, Nepal.

Incorporating Endmember Variability into Linear Unmixing of Coarse Resolution Imagery: Mapping Large-Scale Impervious Surface Abundance Using a Hierarchically Object-Based Spectral Mixture Analysis

As an important indicator of anthropogenic impacts on the Earth’s surface, it is of great necessity to accurately map large-scale urbanized areas for various science and policy applications. Although spectral mixture analysis (SMA) can provide spatial distribution and quantitative fractions for better representations of urban areas, this technique is rarely explored with 1-km resolution imagery. This is due mainly to the absence of image endmembers associated with the mixed pixel problem. Consequently, as the most profound source of error in SMA, endmember variability has rarely been considered with coarse resolution imagery. These issues can be acute for fractional land cover mapping due to the significant spectral variations of numerous land covers across a large study area. To solve these two problems, a hierarchically object-based SMA (HOBSMA) was developed (1) to extrapolate local endmembers for regional spectral library construction; and (2) to incorporate endmember variability into linear spectral unmixing of MODIS 1-km imagery for large-scale impervious surface abundance mapping. Results show that by integrating spatial constraints from object-based image segments and endmember extrapolation techniques into multiple endmember SMA (MESMA) of coarse resolution imagery, HOBSMA improves the discriminations between urban impervious surfaces and other land covers with well-known spectral confusions (e.g., bare soil and water), and particularly provides satisfactory representations of urban fringe areas and small settlements. HOBSMA yields promising abundance results at the km-level scale with relatively high precision and small bias, which considerably outperforms the traditional simple mixing model and the aggregated MODIS land cover classification product.

A Novel Land Cover Classification Map Based on a MODIS Time-Series in Xinjiang, China

Accurate mapping of land cover on a regional scale is useful for climate and environmental modeling. In this study, we present a novel land cover classification product based on spectral and phenological information for the Xinjiang Uygur Autonomous Region (XUAR) in China. The product is derived at a 500 m spatial resolution using an innovative approach employing moderate resolution imaging spectroradiometer (MODIS) surface reflectance and the enhanced vegetation index (EVI) time series. The classification results capture regional scale land cover patterns and small-scale phenomena. By applying a regionally specified classification scheme, an extensive collection of training data, and regionally tuned data processing, the quality and consistency of the phenological maps are significantly improved. With the ability to provide an updated land cover product considering the heterogenic environmental and climatic conditions, the novel land cover map is valuable for research related to environmental change in this region.

Geospatial method for computing supplemental multi-decadal US coastal land use and land cover classification products, using Landsat data and C-CAP products

This paper discusses the development and implementation of a method that can be used with multi-decadal Landsat data for computing general coastal US land use and land cover (LULC) maps consisting of seven classes. With Mobile Bay, Alabama as the study region, the method that was applied to derive LULC products for nine dates across a 34-year time span. Classifications were computed and refined using decision rules in conjunction with unsupervised classification of Landsat data and Coastal Change and Analysis Program value-added products. Each classification’s overall accuracy was assessed by comparing stratified random locations to available high spatial resolution satellite and aerial imagery, field survey data and raw Landsat RGBs. Overall classification accuracies ranged from 83 to 91% with overall κ statistics ranging from 0.78 to 0.89. Accurate classifications were computed for all nine dates, yielding effective results regardless of season and Landsat sensor. This classification method provided useful map inputs for computing LULC change products.

Effective UV surface albedo of seasonally snow-covered lands

Abstract. At ultraviolet wavelengths the albedo of most natural surfaces is small with the striking exception of snow and ice. Therefore, snow cover is a major challenge for various applications based on radiative transfer modelling. The aim of this work was to determine the characteristic effective UV range surface albedo of various land cover types when covered by snow. First we selected 1 by 1 degree sample regions that met three criteria: the sample region contained dominantly subpixels of only one land cover type according to the 8 km global land cover classification product from the University of Maryland; the average slope of the sample region was less than 2 degrees according to the USGS's HYDRO1K slope data; the sample region had snow cover in March according to the NSIDC Northern Hemisphere weekly snow cover data. Next we generated 1 by 1 degree gridded 360 nm surface albedo data from the Nimbus-7 TOMS Lambertian equivalent reflectivity data, and used them to construct characteristic effective surface albedo distributions for each land cover type. The resulting distributions showed that each land cover type experiences a characteristic range of surface albedo values when covered by snow. The result is explained by the vegetation that extends upward beyond the snow cover and masks the bright snow covered surface.

How well do we know northern land cover? Comparison of four global vegetation and wetland products with a new ground‐truth database for West Siberia

An unprecedented collection of 2161 geolocated, irregularly spaced field observations of land cover spanning ∼106 km2 throughout West Siberia suggests that currently available land cover classification products are remarkably poor indicators of vegetation type and water body extent in this northern wetland environment. The ground‐truth data are compared with (1) the Global Land Cover Characteristics database derived from Advanced Very High Resolution Radiometer data (GLCC.AVHRR), (2) the Global Land Cover Classification derived from Moderate Resolution Imaging Spectroradiometer data (GLCC.MODIS), (3) the Global Lakes and Wetlands Database (GLWD), and (4) the West Siberian Lowland Peatland Database (WSLPD) using: (1) all land cover categories and (2) permanent wetland and water body categories only. Overall agreement with ground observations of land cover is only 21% for the GLCC.AVHRR database and 11% for the GLCC.MODIS database. However, at a much broader scale (one degree of latitude) there is improved qualitative agreement between vegetation classes, with some notable exceptions: (1) at low latitudes (∼54°N–58°N), both the GLCC.AVHRR and GLCC.MODIS databases underestimate woody savannas in favor of croplands; (2) at midlatitudes (∼58°N–64°N), the GLCC.AVHRR database underestimates evergreen needleleaf forest in favor of mixed forest; and (3) at high latitudes (∼64°N–68°N), both the GLCC.AVHRR and GLCC.MODIS databases underestimate deciduous needleleaf forest in favor of woody savannas or open shrublands, respectively. It is clear that all four data databases significantly underestimate permanent wetlands and water bodies, although those specifically developed for this purpose (GLWD and WSPLD) perform better than the more widely used, multiclass land cover products. For permanent wetlands, agreement with our ground data is only 2% for GLCC.MODIS, 23% for GLCC.AVHRR, 45% for GLWD and 56% for WSLPD. Agreement with open water bodies is even poorer (0–5%). These results raise into question the efficacy of incorporating currently available land cover products into terrestrial ecosystem models in northern wetland environments.

Land cover assessment with MODIS imagery in southern African Miombo ecosystems

Global land use and land cover products in highly dynamic tropical ecosystems lack the detail needed for natural resource management and monitoring at the national and provincial level. The MODIS sensor provides improved opportunities to combine multispectral and multitemporal data for land use and land cover mapping. In this paper we compare the MODIS Global Land Cover Classification Product with recent land use and land cover maps at the national level over a characteristic location of Miombo woodlands in the province of Zambezia, Mozambique. The performances of three land cover-mapping approaches were assessed: single-date supervised classification, principal component analysis of band-pair difference images, and multitemporal NDVI analysis. Extensive recent field data were used for the definition of the test sites and accuracy assessment. Encouraging results were achieved with the three approaches. The classification results were refined with the help of a digital elevation model. The most consistent results were achieved using principal component analysis of band-pair difference images. This method provided the most accurate classifications for agriculture, wetlands, grasslands, thicket and open forest. The overall classification accuracy reached 90%. The multitemporal NDVI provided a more accurate classification for the dense forest cover class. The selection of the right image dates proved to be critical for all the cases evaluated. The flexibility of these alternatives makes them promising options for rapid and inexpensive land cover mapping in regions of high environmental variability such as tropical developing countries.

The effects of aggregated land cover data on estimating NPP in northern Wisconsin

Ecosystem models are routinely used to estimate net primary production (NPP) from the stand to global scales. Complex ecosystem models, implemented at small scales (< 10 km 2), are impractical at global scales and, therefore, require simplifying logic based on key ecological first principles and model drivers derived from remotely sensed data. There is a need for an improved understanding of the factors that influence the variability of NPP model estimates at different scales so we can improve the accuracy of NPP estimates at the global scale. The objective of this study was to examine the effects of using leaf area index (LAI) and three different aggregated land cover classification products–two factors derived from remotely sensed data and strongly affect NPP estimates–in a light use efficiency (LUE) model to estimate NPP in a heterogeneous temperate forest landscape in northern Wisconsin, USA. Three separate land cover classifications were derived from three different remote sensors with spatial resolutions of 15, 30, and 1000 m. Average modeled net primary production (NPP) ranged from 402 gC m − 2 year − 1 (15 m data) to 431 gC m − 2 year − 1 (1000 m data), for a maximum difference of 7%. Almost 50% of the difference was attributed each to LAI estimates and land cover classifications between the fine and coarse scale NPP estimate. Results from this study suggest that ecosystem models that use biome-level land cover classifications with associated LUE coefficients may be used to model NPP in heterogeneous land cover areas dominated by cover types with similar NPP. However, more research is needed to examine scaling errors in other heterogeneous areas and NPP errors associated with deriving LAI estimates.

Spatially complete global spectral surface albedos: value-added datasets derived from Terra MODIS land products

Recent production of land surface anisotropy, diffuse bihemispherical (white-sky) albedo, and direct-beam directional hemispherical (black-sky) albedo from observations acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the National Aeronautics and Space Administration's Terra and Aqua satellite platforms have provided researchers with unprecedented spatial, spectral, and temporal information on the land surface's radiative characteristics. Cloud cover, which curtails retrievals, and the presence of ephemeral and seasonal snow limit the snow-free data to approximately half the global land surfaces on an annual equal-angle basis. This precludes the MOD43B3 albedo products from being used in some remote sensing and ground-based applications, climate models, and global change research projects. An ecosystem-dependent temporal interpolation technique is described that has been developed to fill missing or seasonally snow-covered data in the official MOD43B3 albedo product. The method imposes pixel-level and local regional ecosystem-dependent phenological behavior onto retrieved pixel temporal data in such a way as to maintain pixel-level spatial and spectral detail and integrity. The phenological curves are derived from statistics based on the MODIS MOD12Q1 IGBP land cover classification product geolocated with the MOD43B3 data. The resulting snow-free value-added products provide the scientific community with spatially and temporally complete global white- and black-sky surface albedo maps and statistics. These products are stored on 1-min and coarser resolution equal-angle grids and are computed for the first seven MODIS wavelengths, ranging from 0.47-2.1 /spl mu/m and for three broadband wavelengths 0.3-0.7, 0.3-5.0, and 0.7-5.0 /spl mu/m.