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1.
房世波  韩威  裴志方 《遥感学报》2020,24(3):326-332
2020年初非洲东北和印巴边境沙漠蝗群席卷多个国家,大面积农田及自然植被被啃食,是什么气候条件促成了此次沙漠蝗灾?距离中国最近的印巴边境蝗群成为研究以及社会关注的热点,蝗灾对当地植被的影响如何?其发展趋势如何?从气候学上分析,蝗灾历史上是否曾经或者未来是否向印度东边迁飞而进入中国呢?本研究利用长时间序列的卫星遥感数据和气象气候观测数据,对沙漠蝗群可能扩展趋势进行了分析。研究结果表明:(1)由于沙漠蝗群的啃食,2020年1月和2月,在蝗群分布区大面积植被区的归一化植被指数较常年大幅度下降,2月(2月3日数据)的啃食面积较1月明显扩大;(2)发生在2018年5月和10月两次印度洋飓风和2019年12月强热带风暴等几个罕见气旋给非洲和阿拉伯半岛带来的强降水,是本次非洲-西亚蝗灾的形成重要原因;(3)从影响沙漠蝗群起飞的气温和沙漠蝗虫适合的降水条件来看,历史上或未来沙漠蝗群迁徙到印度东边的机会很少,进入中国的可能性非常小。  相似文献   

2.
In this study, we assessed land cover land use (LCLU) changes and their potential environmental drivers (i.e., precipitation, temperature) in five countries in Eastern & Southern (E&S) Africa (Rwanda, Botswana, Tanzania, Malawi and Namibia) between 2000 and 2010. Landsat-derived LCLU products developed by the Regional Centre for Mapping of Resources for Development (RCMRD) through the SERVIR (Spanish for “to serve”) program, a joint initiative of NASA and USAID, and NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to evaluate and quantify the LCLU changes in these five countries. Given that the original development of the MODIS land cover type standard products included limited training sites in Africa, we performed a two-level verification/validation of the MODIS land cover product in these five countries. Precipitation data from CHIRPS dataset were used to evaluate and quantify the precipitation changes in these countries and see if it was a significant driver behind some of these LCLU changes. MODIS Land Surface Temperature (LST) data were also used to see if temperature was a main driver too.Our validation analysis revealed that the overall accuracies of the regional MODIS LCLU product for this African region alone were lower than that of the global MODIS LCLU product overall accuracy (63–66% vs. 75%). However, for countries with uniform or homogenous land cover, the overall accuracy was much higher than the global accuracy and as high as 87% and 78% for Botswana and Namibia, respectively. In addition, the wetland and grassland classes had the highest user’s accuracies in most of the countries (89%–99%), which are the ones with the highest number of MODIS land cover classification algorithm training sites.Our LCLU change analysis revealed that Botswana’s most significant changes were the net reforestation, net grass loss and net wetland expansion. For Rwanda, although there have been significant forest, grass and crop expansions in some areas, there also have been significant forest, grass and crop loss in other areas that resulted in very minimal net changes. As for Tanzania, its most significant changes were the net deforestation and net crop expansion. Malawi’s most significant changes were the net deforestation, net crop expansion, net grass expansion and net wetland loss. Finally, Namibia’s most significant changes were the net deforestation and net grass expansion.The only noticeable environmental driver was in Malawi, which had a significant net wetland loss and could be due to the fact that it was the only country that had a reduction in total precipitation between the periods when the LCLU maps were developed. Not only that, but Malawi also happened to have a slight increase in temperature, which would cause more evaporation and net decrease in wetlands if the precipitation didn’t increase as was the case in that country. In addition, within our studied countries, forestland expansion and loss as well as crop expansion and loss were happening in the same country almost equally in some cases. All of that implies that non-environmental factors, such as socioeconomics and governmental policies, could have been the main drivers of these LCLU changes in many of these countries in E&S Africa. It will be important to further study in the future the detailed effects of such drivers on these LCLU changes in this part of the world.  相似文献   

3.
Using NOAA/AVHRR 10-day composite NDVI data and 10-day meteorological data, including air temperature, precipitation, vapor pressure, wind velocity and sunshine duration, at 19 weather stations in the three-river-source region in the Qinghai–Tibetan Plateau in China from 1982 to 2000, the variations of NDVI and climate factors were analyzed for the purpose of studying the correlation between climate change and vegetation growth as represented by NDVI in this region. Results showed that the NDVI values in this region gradually grew from the west to the east, and the distribution was consistent with that of moisture status. The growing season came earlier due to climate warming, yet because of the reduction of precipitation, maximal NDVI during 1982–2000 did not show a significant change. NDVI related positively to air temperature, vapor pressure and precipitation, but negatively related to sunshine duration and wind velocity. Furthermore, the response of NDVI to climate change showed time lags for different climate factors. Water condition and temperature were found to be the most important factors effecting the variation of NDVI during the growing season in both the semi-arid and the semi-humid areas. In addition, NDVI had a better correlation with vapor pressure than with precipitation. The ratio of precipitation to evapotranspiration, representing water gain and loss, can be regarded as a comprehensive index to analyze NDVI and climate change, especially in areas where the water condition plays a dominant role.  相似文献   

4.
Although the TRMM-based Flood Detection System (FDS) has been in operation in near real-time since 2006, the flood ‘detection’ capability has been validated mostly against qualitative reports in news papers and other types of media. In this study, a more quantitative validation of the FDS over Bangladesh against in situ measurements is presented. Using measured stream flow and rainfall data, the study analyzed the flood detection capability from space for three very distinct river systems in Bangladesh: (1) Ganges– a snowmelt-fed river regulated by upstream India, (2) Brahmaputra – a snow-fed river that is braided, and (3) Meghna – a rain-fed and relatively flashier river. The quantitative assessment showed that the effectiveness of the TRMM-based FDS can vary as a function of season and drainage basin characteristics. Overall, the study showed that the TRMM-based FDS has great potential for flood prone countries like Bangladesh that are faced with tremendous hurdles in transboundary flood management. The system had a high probability of detection overall, but produced increased false alarms during the monsoon period and in regulated basins (Ganges), undermining the credibility of the FDS flood warnings for these situations. For this reason, FDS users are cautioned to verify FDS estimates during the monsoon period and for regulated rivers before implementing flood management practices. Planned improvements by FDS developers involving physically-based hydrologic modeling should transform the system into a more accurate tool for near real-time decision making on flood management for ungauged river basins of the world.  相似文献   

5.
A global geopotential model, like EGM2008, is not capable of representing the high-frequency components of Earth’s gravity field. This is known as the omission error. In mountainous terrain, omission errors in EGM2008, even when expanded to degree 2,190, may reach amplitudes of 10 cm and more for height anomalies. The present paper proposes the utilisation of high-resolution residual terrain model (RTM) data for computing estimates of the omission error in rugged terrain. RTM elevations may be constructed as the difference between the SRTM (Shuttle Radar Topography Mission) elevation model and the DTM2006.0 spherical harmonic topographic expansion. Numerical tests, carried out in the German Alps with a precise gravimetric quasigeoid model (GCG05) and GPS/levelling data as references, demonstrate that RTM-based omission error estimates improve EGM2008 height anomaly differences by 10 cm in many cases. The comparisons of EGM2008-only height anomalies and the GCG05 model showed 3.7 cm standard deviation after a bias-fit. Applying RTM omission error estimates to EGM2008 reduces the standard deviation to 1.9 cm which equates to a significant improvement rate of 47%. Using GPS/levelling data strongly corroborates these findings with an improvement rate of 49%. The proposed RTM approach may be of practical value to improve quasigeoid determination in mountainous areas without sufficient regional gravity data coverage, e.g., in parts of Asia, South America or Africa. As a further application, RTM omission error estimates will allow refined validation of global gravity field models like EGM2008 from GPS/levelling data.  相似文献   

6.
Efficient water-resource management is essential with regard to food security, growing populations and climate change. This is especially important for low- and middle-income (LMC) countries where food is often locally produced by traditional smallholder farming. Detailed knowledge of the spatio-temporal distribution of irrigation-water consumption provides valuable information to anticipate local food shortages and water scarcity as a result of climate variability. Yet, adequate techniques to quantify irrigation-water consumption at field level over large areas are lacking. Irrigation estimates generally have a coarse resolution making them inadequate for field-level assessments.This study developed a remote-sensing-based approach to quantify spatio-temporal patterns of irrigation-water consumption at field level using the MODIS evapotranspiration product (MOD16A2) and existing land-use maps on the spatio-temporal distribution of irrigated agriculture. Object-based image analysis was used to establish local evapotranspiration differences between irrigated and rainfed fields on a monthly basis, which are the irrigation-water consumption rates of the irrigated fields. This novel method was applied to a study area in the Central Rift Valley in Ethiopia where smallholder farming is dominant and only a few large-scale farms are present. Comparison with irrigation-water-consumption values of a local irrigation scheme showed that the monthly temporal dynamics were captured quite well, but lower values were calculated compared to the scheme's field data. Comparison with two validated remote-sensing based studies in Africa showed good agreement as irrigation-water-consumption estimates were in the same order of magnitude. Irrigation-water consumption follows the temporal rainfall pattern, i.e. irrigation practices intensify with increased water availability. Surface water is commonly used for irrigation in the study area.Our study shows that smallholder practices have a lower irrigation-water consumption compared to modern large-scale farms by approximately a factor 3. Irrigation-water consumption in the area is considerable, especially during the dry season. On average 32 % of excess water (precipitation – evapotranspiration) is consumed for irrigation. For smallholder irrigation and large-scale irrigation specifically this is 28 % and 63 % respectively.The object-based approach presented here is an operational mapping method for field-level irrigation-water-consumption over large areas. MOD16A2 is a global open-source readily-available evapotranspiration product used here although an evapotranspiration product with a higher spatial resolution might be preferred. Our approach can provide irrigation-water-consumption estimates over large areas in data-poor regions, which will increase the understanding of spatio-temporal patterns of smallholder irrigation and provide information to optimize water use.  相似文献   

7.
ABSTRACT

This paper addresses warm season hydroclimatic variability in the southern Appalachian region of the southeastern U.S., where precipitation can vary as much as 127?mm or more, with maximum seasonal totals exceeding 736?mm in extreme cases. Despite the occurrence of droughts, floods, and their socioecological impacts, hydroclimate variability is still poorly understood. This study characterizes the regional scale variations in the hydroclimate by examining the daily distribution of precipitation patterns in different topographic environments. Parameter-elevation relationships on independent slopes model (PRISM) gridded precipitation estimates are used to identify the location and frequency of different types of rainfall events. Several types of clustering algorithms are used as a regionalization approach to define areas where the precipitation regime exhibits similarities in its frequency of occurrence. The results are compared with internal validation statistics and a visualization is used to assess how well the resulting hydroclimatic regions align with different topographic environments. This study reveals the intricate spatial footprint of dry and wet regimes and demonstrates how clustering applications can be used with gridded climate data to determine where extremes are most likely to develop across mountain catchments.  相似文献   

8.
The aim of the study was to evaluate flash flood potential areas in the Western Cape Province of South Africa, by integrating remote sensing products of high rainfall intensity, antecedent soil moisture and topographic wetness index (TWI). Rainfall has high spatial and temporal variability, thus needs to be quantified at an area in real time from remote sensing techniques unlike from sparsely distributed, point gauge network measurements. Western Cape Province has high spatial variation in topography which results in major differences in received rainfall within areas not far from each other. Although high rainfall was considered as the major cause of flash flood, also other contributing factors such as topography and antecedent soil moisture were considered. Areas of high flash flood potential were found to be associated with high rainfall, antecedent precipitation and TWI. Although TRMM 3B42 was found to have better rainfall intensity accuracy, the product is not available in near real time but rather at a rolling archive of three months; therefore, Multi- sensor precipitation estimate rainfall estimates available in near real time are opted for flash flood events. Advanced Scatterometer (ASCAT) soil moisture observations were found to have a reasonable r value of 0.58 and relatively low MAE of 3.8 when validated with in situ soil moisture measurements. The results of this study underscore the importance of ASCAT and TRMM satellite datasets in mapping areas at risk of flooding.  相似文献   

9.
Light Detection And Ranging (LiDAR) has a unique capability for estimating forest canopy height, which has a direct relationship with, and can provide better understanding of the aboveground forest carbon storage. The full waveform data of the large-footprint LiDAR Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and land Elevation Satellite (ICESat), combined with field measurements of forest canopy height, were employed to achieve improved estimates of forest canopy height over sloping terrain in the Changbai mountains region, China. With analyzing ground-truth experiments, the study proposed an improved model over Lefsky's model to predict maximum canopy height using the logarithmic transformation of waveform extent and elevation change as independent variables. While Lefsky's model explained 8–89% of maximum canopy height variation in the study area, the improved model explained 56–92% of variation within the 0–30° terrain slope category. The results reveal that the improved model can reduce the mixed effects caused by both sloping terrain and rough land surface, and make a significant improvement for accurately estimating maximum canopy height over sloping terrain.  相似文献   

10.
Availability of reliable, timely and accurate rainfall data is constraining the establishment of flood forecasting and early warning systems in many parts of Africa. We evaluated the potential of satellite and weather forecast data as input to a parsimonious flood forecasting model to provide information for flood early warning in the central part of Nigeria. We calibrated the HEC-HMS rainfall-runoff model using rainfall data from post real time Tropical Rainfall Measuring Mission (TRMM) Multi satellite Precipitation Analysis product (TMPA). Real time TMPA satellite rainfall estimates and European Centre for Medium-Range Weather Forecasts (ECMWF) rainfall products were tested for flood forecasting. The implication of removing the systematic errors of the satellite rainfall estimates (SREs) was explored. Performance of the rainfall-runoff model was assessed using visual inspection of simulated and observed hydrographs and a set of performance indicators. The forecast skill was assessed for 1–6 days lead time using categorical verification statistics such as Probability Of Detection (POD), Frequency Of Hit (FOH) and Frequency Of Miss (FOM). The model performance satisfactorily reproduced the pattern and volume of the observed stream flow hydrograph of Benue River. Overall, our results show that SREs and rainfall forecasts from weather models have great potential to serve as model inputs for real-time flood forecasting in data scarce areas. For these data to receive application in African transboundary basins, we suggest (i) removing their systematic error to further improve flood forecast skill; (ii) improving rainfall forecasts; and (iii) improving data sharing between riparian countries.  相似文献   

11.
Land use modeling requires large amounts of data that are typically spatially correlated. This study applies two geostatistical techniques to account for spatial correlation in residential land use change modeling. In the first approach, we combined generalized linear model (GLM) with indicator kriging to estimate the posterior probability of residential development. In the second approach, generalized linear mixed model (GLMM) was used to simultaneously model spatial correlation and regression fixed effects. Spatial agreement between actual and modeled land use change was higher for the GLM incorporating indicator kriging. The GLMM produced more reliable estimates and could be more useful in analyzing the effects of driving factors of land use change for land use planning.  相似文献   

12.
Abstract

People are now using geoinformation for many different purposes and consequently one can confidently say that the need for geospatial data infrastructure (GDI) cannot be overstated in sub-Saharan Africa. Geospatial information (GI) is essential to socio-economic planning and development of sub-Sahara African countries. This paper therefore examines: GI during the last centuries in sub-Sahara Africa; recent paradigms in GDI in sub-Sahara Africa; the benefit of GDI to the African economy and the future of GDI in sub-Sahara Africa. This study discovered that most countries in sub-Saharan Africa did not have timely access to accurate geospatial data throughout the last centuries. This significantly hindered meaningful social and economic development. Development of GDI nonetheless, will enhance search and retrieval of geospatial data in Africa. This is one of the benefits that can be derived from implementing GDI in sub-Sahara Africa. Therefore, it is necessary to review cadastral survey laws and regulations so as to incorporate the use of recent geospatial equipment.  相似文献   

13.
This article illustrates two techniques for merging daily aerosol optical depth (AOD) measurements from satellite and ground-based data sources to achieve optimal data quality and spatial coverage. The first technique is a traditional Universal Kriging (UK) approach employed to predict AOD from multi-sensor aerosol products that are aggregated on a reference grid with AERONET as ground truth. The second technique is spatial statistical data fusion (SSDF); a method designed for massive satellite data interpolation. Traditional kriging has computational complexity O(N3), making it impractical for large datasets. Our version of UK accommodates massive data inputs by performing kriging locally, while SSDF accommodates massive data inputs by modelling their covariance structure with a low-rank linear model. In this study, we use aerosol data products from two satellite instruments: the moderate resolution imaging spectrometer and the geostationary operational environmental satellite, covering the Continental United States.  相似文献   

14.
The Moderate Resolution Imaging Spectroradiometer (MODIS) is largely used to estimate Leaf Area Index (LAI) using radiative transfer modeling (the “main” algorithm). When this algorithm fails for a pixel, which frequently occurs over Brazilian soybean areas, an empirical model (the “backup” algorithm) based on the relationship between the Normalized Difference Vegetation Index (NDVI) and LAI is utilized. The objective of this study is to evaluate directional effects on NDVI and subsequent LAI estimates using global (biome 3) and local empirical models, as a function of the soybean development in two growing seasons (2004–2005 and 2005–2006). The local model was derived from the pixels that had LAI values retrieved from the main algorithm. In order to keep the reproductive stage for a given cultivar as a constant factor while varying the viewing geometry, pairs of MODIS images acquired in close dates from opposite directions (backscattering and forward scattering) were selected. Linear regression relationships between the NDVI values calculated from these two directions were evaluated for different view angles (0–25°; 25–45°; 45–60°) and development stages (<45; 45–90; >90 days after planting). Impacts on LAI retrievals were analyzed. Results showed higher reflectance values in backscattering direction due to the predominance of sunlit soybean canopy components towards the sensor and higher NDVI values in forward scattering direction due to stronger shadow effects in the red waveband. NDVI differences between the two directions were statistically significant for view angles larger than 25°. The main algorithm for LAI estimation failed in the two growing seasons with gradual crop development. As a result, up to 94% of the pixels had LAI values calculated from the backup algorithm at the peak of canopy closure. Most of the pixels selected to compose the 8-day MODIS LAI product came from the forward scattering view because it displayed larger LAI values than the backscattering. Directional effects on the subsequent LAI retrievals were stronger at the peak of the soybean development (NDVI values between 0.70 and 0.85). When the global empirical model was used, LAI differences up to 3.2 for consecutive days and opposite viewing directions were observed. Such differences were reduced to values up to 1.5 with the local model. Because of the predominance of LAI retrievals from the MODIS backup algorithm during the Brazilian soybean development, care is necessary if one considers using these data in agronomic growing/yield models.  相似文献   

15.
ABSTRACT

Optical satellite data is an efficient and complementary method to hydrographic surveys for deriving bathymetry in shallow coastal waters. Empirical approaches (in particular, the models of Stumpf and Lyzenga) provide a practical methodology to derive bathymetric information from remote sensing. Recent studies, however, have focused on enhancing the performance of such empirical approaches by extending them via spatial information. In this study, the relationship between multibeam depth and Sentinel-2 image bands was analyzed in an optically complex environment using the spatial predictor of kriging with an external drift (KED), where its external drift component was estimated: a) by a ratio of log-transformed bands based on Stumpf’s model (KED_S) and b) by a log-linear transform based on Lyzenga’s model (KED_L). Through the calibration of KED models, the study objectives were: 1) to better understand the empirical relationship between Sentinel-2 multispectral satellite reflectance and depth, 2) to test the robustness of KED to derive bathymetry in a multitemporal series of Sentinel-2 images and multibeam data, and 3) to compare the performance of KED against the existing non-spatial models described by Stumpf et al. and Lyzenga. Results showed that KED could improve prediction accuracy with a decrease in RMSE of 89% and 88%, and an increase in R2 of 27% and 14%, over the Stumpf and Lyzenga models, respectively. The decrease in RMSE provides a worthwhile improvement in accuracy, where results showed effective prediction of depth up to 6 m. However, the presence of higher concentrations of suspended materials, especially river plumes, can reduce this threshold to 4 m. As would be expected, prediction accuracy could be improved through the removal of outliers, which were mainly located in the channel of the river, areas influenced by the river plume, abrupt topography, but also very shallow areas close to the shoreline. These areas have been identified as conflictive zones where satellite-derived bathymetry can be compromised.  相似文献   

16.
The South African Weather Service (SAWS) operates a radar station network, providing data continuity back to 1994, which is unique for southern Africa. The Geographical Resources Analysis Support System (GRASS) GIS was introduced to the SAWS “Meteorological Systems and Technology” (METSYS) radar research center in 1999 where it is still used for meteorologic research. The predominantly convective nature of precipitation in southern Africa creates a public demand for severe weather information systems for convective cells. Such a system was set up in GRASS GIS, using rule‐based expert systems to classify convective clouds. It isolates storm cells from stacks of reflectivity fields and derives information about their development stages. This data is both archived and also used to send out customised messages to target groups in specific areas of interest. Further, nationwide HTML‐maps can be created, serving as an interactive front‐end for a web‐based weather information system. This service can also be made accessible from remote locations by broad‐casting it as a datastream from satellite through the Worldspace digital radio system.  相似文献   

17.
Downscaling has an important role to play in remote sensing. It allows prediction at a finer spatial resolution than that of the input imagery, based on either (i) assumptions or prior knowledge about the character of the target spatial variation coupled with spatial optimisation, (ii) spatial prediction through interpolation or (iii) direct information on the relation between spatial resolutions in the form of a regression model. Two classes of goal can be distinguished based on whether continua are predicted (through downscaling or area-to-point prediction) or categories are predicted (super-resolution mapping), in both cases from continuous input data. This paper reviews a range of techniques for both goals, focusing on area-to-point kriging and downscaling cokriging in the former case and spatial optimisation techniques and multiple point geostatistics in the latter case. Several issues are discussed including the information content of training data, including training images, the need for model-based uncertainty information to accompany downscaling predictions, and the fundamental limits on the representativeness of downscaling predictions. The paper ends with a look towards the grand challenge of downscaling in the context of time-series image stacks. The challenge here is to use all the available information to produce a downscaled series of images that is coherent between images and, thus, which helps to distinguish real changes (signal) from noise.  相似文献   

18.
 The small number of ground stations for the assessment of the spatial distribution of air pollutants motivates the search for methods that make use of satellite images. One such method, known as Differential Texture Analysis (DTA), is used to measure the Aerosol Optical Thickness in the Visible (AOTV), which correlates highly with air quality. With this method, the presence of clouds and/or land cover changes produce patches of missing values. In this paper we demonstrate that universal kriging can be used to obtain reasonable estimates for these missing values. The methodology was applied to a satellite derived AOTV map of the city of Brescia (Italy). Received: 17 July 2001 / Accepted: 11 December 2001  相似文献   

19.
Spatial predictions of forest variables are required for supporting modern national and sub-national forest planning strategies, especially in the framework of a climate change scenario. Nowadays methods for constructing wall-to-wall maps and calculating small-area estimates of forest parameters are becoming essential components of most advanced National Forest Inventory (NFI) programs. Such methods are based on the assumption of a relationship between the forest variables and predictor variables that are available for the entire forest area. Many commonly used predictors are based on data obtained from active or passive remote sensing technologies. Italy has almost 40% of its land area covered by forests. Because of the great diversity of Italian forests with respect to composition, structure and management and underlying climatic, morphological and soil conditions, a relevant question is whether methods successfully used in less complex temperate and boreal forests may be applied successfully at country level in Italy.For a study area of more than 48,657 km2 in central Italy of which 43% is covered by forest, the study presents the results of a test regarding wall-to-wall, spatially explicit estimation of forest growing stock volume (GSV) based on field measurement of 1350 plots during the last Italian NFI. For the same area, we used potential predictor variables that are available across the whole of Italy: cloud-free mosaics of multispectral optical satellite imagery (Landsat 5 TM), microwave sensor data (JAXA PALSAR), a canopy height model (CHM) from satellite LiDAR, and auxiliary variables from climate, temperature and precipitation maps, soil maps, and a digital terrain model.Two non-parametric (random forests and k-NN) and two parametric (multiple linear regression and geographically weighted regression) prediction methods were tested to produce wall-to-wall map of growing stock volume at 23-m resolution. Pixel level predictions were used to produce small-area, province-level model-assisted estimates. The performances of all the methods were compared in terms of percent root mean-square error using a leave-one-out procedure and an independent dataset was used for validation. Results were comparable to those available for other ecological regions using similar predictors, but random forests produced the most accurate results with a pixel level R2 = 0.69 and RMSE% = 37.2% against the independent validation dataset. Model-assisted estimates were more precise than the original design-based estimates provided by the NFI.  相似文献   

20.
The overarching goal of this study was to produce a global map of rainfed cropland areas (GMRCA) and calculate country-by-country rainfed area statistics using remote sensing data. A suite of spatial datasets, methods and protocols for mapping GMRCA were described. These consist of: (a) data fusion and composition of multi-resolution time-series mega-file data-cube (MFDC), (b) image segmentation based on precipitation, temperature, and elevation zones, (c) spectral correlation similarity (SCS), (d) protocols for class identification and labeling through uses of SCS R2-values, bi-spectral plots, space-time spiral curves (ST-SCs), rich source of field-plot data, and zoom-in-views of Google Earth (GE), and (e) techniques for resolving mixed classes by decision tree algorithms, and spatial modeling. The outcome was a 9-class GMRCA from which country-by-country rainfed area statistics were computed for the end of the last millennium. The global rainfed cropland area estimate from the GMRCA 9-class map was 1.13 billion hectares (Bha). The total global cropland areas (rainfed plus irrigated) was 1.53 Bha which was close to national statistics compiled by FAOSTAT (1.51 Bha). The accuracies and errors of GMRCA were assessed using field-plot and Google Earth data points. The accuracy varied between 92 and 98% with kappa value of about 0.76, errors of omission of 2–8%, and the errors of commission of 19–36%.  相似文献   

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