Improved maize cultivated area estimation over a large scale combining MODIS–EVI time series data and crop phenological information

The accurate and timely information of crop area is vital for crop production and food security. In this study, the Enhanced Vegetation Index (EVI) data from MODerate resolution Imaging Spectroradiometer (MODIS) integrated crop phenological information was used to estimate the maize cultivated area over a large scale in Northeast China. The fine spatial resolution China’s Environment Satellite (HJ-1 satellite) images and the support vector machine (SVM) algorithm were employed to discriminate distribution of maize in the reference area. The mean MODIS–EVI time series curve of maize was extracted in the reference area by using multiple periods MODIS–EVI data. By analysing the temporal shift of crop calendars from northern to southern parts in Northeast China, the lag value was derived from phenological data of twenty-one agro-meteorological stations; here integrating with the mean MODIS–EVI time series image of maize, a standard MODIS–EVI time series image of maize was obtained in the whole study area. By calculating mean absolute distances (MAD) map between standard MODIS–EVI image and mean MODIS–EVI time series images, and setting appropriate thresholds in three provinces, the maize cultivated area was extracted in Northeast China. The results showed that the overall classification accuracy of maize cultivated area was approximately 79%. At the county level, the MODIS-derived maize cultivated area and statistical data were well correlated (R² = 0.82, RMSE = 283.98) over whole Northeast China. It demonstrated that MODIS–EVI time series data integrated with crop phenological information can be used to improve the extraction accuracy of crop cultivated area over a large scale.     

Detection and estimation of mixed paddy rice cropping patterns with MODIS data

In this paper, we developed a more sophisticated method for detection and estimation of mixed paddy rice agriculture from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. Previous research demonstrated that MODIS data can be used to map paddy rice fields and to distinguish rice from other crops at large, continental scales with combined Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) analysis during the flooding and rice transplanting stage. Our approach improves upon this methodology by incorporating mixed rice cropping patterns that include single-season rice crops, early-season rice, and late-season rice cropping systems. A variable EVI/LSWI threshold function, calibrated to more local rice management practices, was used to recognize rice fields at the flooding stage. We developed our approach with MODIS data in Hunan Province, China, an area with significant flooded paddy rice agriculture and mixed rice cropping patterns. We further mapped the aerial coverage and distribution of early, late, and single paddy rice crops for several years from 2000 to 2007 in order to quantify temporal trends in rice crop coverage, growth and management systems. Our results were validated with finer resolution (2.5 m) Satellite Pour l’Observation de la Terre 5 High Resolution Geometric (SPOT 5 HRG) data, land-use data at the scale of 1/10,000 and with county-level rice area statistical data. The results showed that all three paddy rice crop patterns could be discriminated and their spatial distribution quantified. We show the area of single crop rice to have increased annually and almost doubling in extent from 2000 to 2007, with simultaneous, but unique declines in the extent of early and late paddy rice. These results were significantly positive correlated and consistent with agricultural statistical data at the county level (P < 0.01).     

Evaluating a thermal image sharpening model over a mixed agricultural landscape in India

Fine spatial resolution (e.g., <300 m) thermal data are needed regularly to characterise the temporal pattern of surface moisture status, water stress, and to forecast agriculture drought and famine. However, current optical sensors do not provide frequent thermal data at a fine spatial resolution. The TsHARP model provides a possibility to generate fine spatial resolution thermal data from coarse spatial resolution (≥1 km) data on the basis of an anticipated inverse linear relationship between the normalised difference vegetation index (NDVI) at fine spatial resolution and land surface temperature at coarse spatial resolution. The current study utilised the TsHARP model over a mixed agricultural landscape in the northern part of India. Five variants of the model were analysed, including the original model, for their efficiency. Those five variants were the global model (original); the resolution-adjusted global model; the piecewise regression model; the stratified model; and the local model. The models were first evaluated using Advanced Space-borne Thermal Emission Reflection Radiometer (ASTER) thermal data (90 m) aggregated to the following spatial resolutions: 180 m, 270 m, 450 m, 630 m, 810 m and 990 m. Although sharpening was undertaken for spatial resolutions from 990 m to 90 m, root mean square error (RMSE) of <2 K could, on average, be achieved only for 990–270 m in the ASTER data. The RMSE of the sharpened images at 270 m, using ASTER data, from the global, resolution-adjusted global, piecewise regression, stratification and local models were 1.91, 1.89, 1.96, 1.91, 1.70 K, respectively. The global model, resolution-adjusted global model and local model yielded higher accuracy, and were applied to sharpen MODIS thermal data (1 km) to the target spatial resolutions. Aggregated ASTER thermal data were considered as a reference at the respective target spatial resolutions to assess the prediction results from MODIS data. The RMSE of the predicted sharpened image from MODIS using the global, resolution-adjusted global and local models at 250 m were 3.08, 2.92 and 1.98 K, respectively. The local model consistently led to more accurate sharpened predictions by comparison to other variants.     

Comparative assessment of CORINE2000 and GLC2000: Spatial analysis of land cover data for Europe

Given the current lack of interoperability between global and regional land cover products, efforts are underway to link the new European global land cover map (GLOBCOVER) with the existing global land cover 2000 map (GLC2000) and European CORINE mapping initiative. Since both datasets apply different mapping standards, key for a successful implementation is a thorough understanding of the heterogeneities among both datasets. Thus, this paper provides an assessment of compatibilities and differences between the CORINE2000 and GLC2000 datasets. The comparative assessment considers inconsistencies between the thematic legends (using the UN land cover classification system-LCCS), class specific accuracies, and the spatial resolution and heterogeneity of the datasets. The results are summarized with implications for the development of the new GLOBCOVER datasets.     

A comparison of geographic datasets and field measurements to model soil carbon using random forests and stepwise regressions (British Columbia,Canada)

We used geographic datasets and field measurements to examine the mechanisms that affect soil carbon (SC) storage for 65 grazed and non-grazed pastures in southern interior grasslands of British Columbia, Canada. Stepwise linear regression (SR) modeling was compared with random forest (RF) modeling. Models produced with SR performed better than those produced using RF models (r² = 0.56–0.77 AIC = 0.16–0.30 for SR models; r² = 0.38–0.53 and AIC = 0.18–0.30 for RF models). The factors most significant when predicting SC were elevation, precipitation, and the normalized difference vegetation index (NDVI). NDVI was evaluated at two scales using: (1) the MOD 13Q1 (250 m/16-day resolution) NDVI data product from the moderate resolution imaging spectro-radiometer (MODIS) (NDVI_MODIS), and (2) a handheld multispectral radiometer (MSR, 1 m resolution) (NDVI_MSR) in order to understand the potential for increasing model accuracy by increasing the spatial resolution of the gridded geographic datasets. When NDVI_MSR data were used to predict SC, the percentage of the variance explained by the model was greater than for models that relied on NDVI_MODIS data (r² = 0.68 for SC for non-grazed systems, modeled with SR based on NDVI_MODIS data; r² = 0.77 for SC for non-grazed systems, modeled with SR based on NDVI_MSR data). The outcomes of this study provide the groundwork for effective monitoring of SC using geographic datasets to enable a carbon offset program for the ranching industry.     

Comparison and relative quality assessment of the GLC2000, GLOBCOVER,MODIS and ECOCLIMAP land cover data sets at the African continental scale

Land cover dynamics at the African continental scale is of great importance for global change studies. Actually, four satellite-derived land cover maps of Africa now available, e.g. ECOCLIMAP, GLC2000, MODIS and GLOBCOVER, are based on images acquired in the 2000s. This study aims at stressing the compliances and the discrepancies between these four land cover classifications systems. Each of them used different mapping initiatives and relies on different mapping standards, which supports the present investigation. In order to do a relative comparison of the four maps, a preamble was to reconcile their thematic legends into more aggregated categories after a projection into the same spatial resolution. Results show that the agreement between the four land cover products is between 56 and 69%. While all these land cover datasets show a reasonable agreement in terms of surface types and spatial distribution patterns, mapping of heterogeneous landscapes in the four products is not very successful. Land cover products based on remote sensing imagery can indeed significantly be improved by using smarter algorithms, better timing of image acquisition, improved class definitions. Either will help to improve the accuracy of future land cover maps at the African continental scale. Data producers may use the areas of spatial agreement for training area selection while users might need to verify the information in the areas of disagreement using additional data sources.     

Efficient collection of training data for sub-pixel land cover classification using neural networks

Artificial neural networks (ANNs) are a popular class of techniques for performing soft classifications of satellite images. They have successfully been applied for estimating crop areas through sub-pixel classification of medium to low resolution images. Before a network can be used for classification and estimation, however, it has to be trained. The collection of the reference area fractions needed to train an ANN is often both time-consuming and expensive. This study focuses on strategies for decreasing the efforts needed to collect the necessary reference data, without compromising the accuracy of the resulting area estimates. Two aspects were studied: the spatial sampling scheme (i) and the possibility for reusing trained networks in multiple consecutive seasons (ii). Belgium was chosen as the study area because of the vast amount of reference data available. Time series of monthly NDVI composites for both SPOT-VGT and MODIS were used as the network inputs. The results showed that accurate regional crop area estimation (R² > 80%) is possible using only 1% of the entire area for network training, provided that the training samples used are representative for the land use variability present in the study area. Limiting the training samples to a specific subset of the population, either geographically or thematically, significantly decreased the accuracy of the estimates. The results also indicate that the use of ANNs trained with data from one season to estimate area fractions in another season is not to be recommended. The interannual variability observed in the endmembers’ spectral signatures underlines the importance of using up-to-date training samples. It can thus be concluded that the representativeness of the training samples, both regarding the spatial and the temporal aspects, is an important issue in crop area estimation using ANNs that should not easily be ignored.     

Identification and mapping of winter wheat by integrating temporal change information and Kullback–Leibler divergence

Crop acreage and its spatial distribution are a base for agriculture related works. Current research combining medium and low spatial resolution images focuses on data fusion and unmixing methods. The purpose of the former is to generate synthetic fine spatial resolution data instead of directly solving the problem. In the latter, high-resolution data is only used to provide endmembers and the result is usually an abundance map rather than the true spatial distribution data. To solve this problem, this paper designs a conceptual model which divides the study area into different types of pixels at a MODIS 250 m scale. Only three types of pixels contain winter wheat, i.e., pure winter wheat pixels (P_A), the mixed pixels comprising winter wheat and other vegetation (M_A) and the mixed pixels comprising winter wheat and other crops (M_B). Different strategies are used in processing them. (1) Within the pure cultivated land pixels, the Kullback–Leibler (KL) divergence is employed to analyze the similarity between unknown pixels and the pure winter wheat samples on the temporal change characteristics of NDVI. Further P_A is identified. (2) For M_A, a proposed reverse unmixing method is firstly used to extract the temporal change information of cultivated land components, after which winter wheat is identified from the cultivated land components as previously described. (3) For M_B which only appears on the border of P_A, a mask is created by expanding the P_A and temporal difference is utilized to identify winter wheat under the mask. Finally, these three results are integrated at a TM scale with the aid of 25 m resolution land use data. We applied the proposed solution and obtained a good result in the main agricultural area of the Yiluo River Basin. The identified winter wheat planting acreage is 161,050.00 hm². The result is validated based on the five-hundred random validation points. Overall accuracy is 94.80% and Kappa coefficient is 0.85. This demonstrates that the temporal information reflecting crop growth is also an important indicator, and the KL divergence makes it more convenient in identifying winter wheat. This research provided a new perspective for the combination of low and medium spatial resolution remote sensing images. The proposed solution can also be effectively applied in other places and countries for the crop which has a clear temporal change characteristic that is different from others.     

Hyperion波段模拟的宽波段遥感影像一致性评价

由于外部成像环境和传感器本身设计指标的差异,不同遥感平台同一波段影像之间常常存在不一致。在多源遥感定量研究中,对外部成像环境导致的差异关注较多,而对以光谱响应函数（SRF）和空间点扩散函数（PSF）为代表的值感器本身设计指标,导致的影像不一致性（差异性）则考虑相对较少,这给后续研究结果的分析与讨论带来一定不确定性。为了提供一种全面、客观对不同遥感平台同一波段影像一致性评价方法,本文采用控制变量法的评价策略,介绍了利用Hyperion高光谱影像结合光谱响应函数模拟各种宽波段影像的方法,并将其用于多平台同一波段影像之间的一致性评价;同时在此基础上探讨了考虑PSF对高-低空间分辨率影像聚合过程的影响,为不同平台、同一波段、不同空间分辨率影像之间的一致性评价提供了技术参考。通过对常见的3种典型传感器Landsat ETM+,Terra MODIS,NOAA AVHRR 3近红外波段进行模拟、比较和一致性评价,结果表明：（1）利用本文所提出的宽波段光谱模拟方法,能较准确地模拟各种宽波段影像（在本文中平均相对误差的绝对值最大为-2.371%）,并可用于多平台同一波段影像的数据评价（从光谱响应的角度）。 （2）为了比较不同平台、同一波段、不同空间分辨率之间的差异性,可将其中高分辨率影像进行尺度上推,得到与其中低分辨率影像相同像元大小的影像,然后再进行评价。在尺度上推过程中,需先考虑PSF效应（可用高斯低通滤波模拟）,然后进行平均值聚合即可。在此基础上进行的一致性评价才是比较客观的,特别是针对异质性区域。本文所采用的评价方法不仅可为评价不同平台波段同一波段影像之间一致性提供很好的解决途径,而且为多源遥感数据选择提供了一种决策依据。     

基于亮度相关矩的MODIS和SPOT影像融合研究

针对MODIS影像空间分辨率较低的问题，提出了一种基于亮度相关矩的多分辨率图像融合方法。该方法首先对SPOT影像进行小波分解，将MODIS影像构成的RGB颜色系统变换到IHS颜色系统；然后，根据强度分量和SPOT影像低频分量的均值和方差来定义图像亮度相关矩；最后，IHS逆变换和小波逆变换得到包含更多信息和有效特征的融合图像。试验结果证明该方法得到的融合图像在保留地物光谱信息和提高空间分辨率上都具有很好的效果。     

A cloud mask methodology for high resolution remote sensing data combining information from high and medium resolution optical sensors

This study presents a novel cloud masking approach for high resolution remote sensing images in the context of land cover mapping. As an advantage to traditional methods, the approach does not rely on thermal bands and it is applicable to images from most high resolution earth observation remote sensing sensors. The methodology couples pixel-based seed identification and object-based region growing. The seed identification stage relies on pixel value comparison between high resolution images and cloud free composites at lower spatial resolution from almost simultaneously acquired dates. The methodology was tested taking SPOT4-HRVIR, SPOT5-HRG and IRS-LISS III as high resolution images and cloud free MODIS composites as reference images. The selected scenes included a wide range of cloud types and surface features. The resulting cloud masks were evaluated through visual comparison. They were also compared with ad-hoc independently generated cloud masks and with the automatic cloud cover assessment algorithm (ACCA). In general the results showed an agreement in detected clouds higher than 95% for clouds larger than 50 ha. The approach produced consistent results identifying and mapping clouds of different type and size over various land surfaces including natural vegetation, agriculture land, built-up areas, water bodies and snow.     

Early-season mapping of crops and cultural operations using very high spatial resolution Pléiades images

The aim of this study was to assess the contribution of very high spatial resolution (VHSR) Pléiades images to both early season crop identification and the mapping of bare soil surface characteristics due to cultural operations. The study region covering 21 km² is located west of the peri-urban territory of the Versailles plain and the Alluets plateau (Yvelines, France). About 100 cropped fields were observed on the ground synchronously with two Pléiades images of 3 and 24 April 2013 and one SPOT4 image of 2 April 2013. The GIS structuring of these field data along with vector information about field boundaries was used for delimitating both training and test zones for the support vector machine classifier with polynomial function kernel (pSVM). The pSVM was computed on the spectral bands and NDVI for both single-date Pléiades and the bi-temporal Pléiades pair. For the single-date classifications of crops, the overall per-pixel accuracy reached 87% for the SPOT4 image of 2 April (6 classes), 79% for the Pléiades image of 3 April (6 classes) and 82% for that of 24 April (7 classes). At the earlier date (2–3 April), the Pléiades image very well discriminated cultural operations (>77%, user’s or producer’s accuracies) as well as fallows and grasslands, while winter cereals and rapeseed were better discriminated by the SPOT4 image winter cereals (>70%, user’s or producer’s accuracies). As Pléiades images revealed within-field spatial variations of early phenological stages of winter cereals that could be critical for adjusting management of zones with delayed development during the growing season, they brought information complementary to multispectral images with high spatial resolution. For the bi-temporal Pléiades image, the overall per-pixel accuracy was about 80% (7 classes), winter crops, grasslands and fallows being very well detected while confusions occurred between spring barley at initial stages (2–3 leaves) and bare soils prepared for other spring crops. Using an additional validation field set covering ∼1/3 of the study area croplands, the crop map resulting from the bi-temporal Pléiades pair achieved correct crop prediction for about 89.7% of the validation fields when considering composite classes for winter cereals and for spring crops. Early-season Pléiades images therefore show a considerable potential for anticipating regional crop patterns and detecting soil tillage operations in spring.     

Leaf area index retrieval using gap fractions obtained from high resolution satellite data: Comparisons of approaches,scales and atmospheric effects

This study is aimed at demonstrating the feasibility of the large scale LAI inversion algorithms using red and near infrared reflectance obtained from high resolution satellite imagery. Radiances in digital counts were obtained in 10 m resolution acquired on cloud free day of August 23, 2007, by the SPOT 5 high resolution geometric (HRG) instrument on mostly temperate hardwood forest located in the Great Lakes – St. Lawrence forest in Southern Quebec. Normalized difference vegetation index (NDVI), scaled difference vegetation index (SDVI) and modified soil-adjusted vegetation index (MSAVI) were applied to calculate gap fractions. LAI was inverted from the gap fraction using the common Beer–Lambert's law of light extinction under forest canopy. The robustness of the algorithm was evaluated using the ground-based LAI measurements and by applying the methods for the independently simulated reflectance data using PROSPECT + SAIL coupled radiative transfer models. Furthermore, the high resolution LAI was compared with MODIS LAI product. The effects of atmospheric corrections and scales were investigated for all of the LAI retrieval methods. NDVI was found to be not suitable index for large scale LAI inversion due to the sensitivity to scale and atmospheric effects. SDVI was virtually scale and atmospheric correction invariant. MSAVI was also scale invariant. Considering all sensitivity analysis, MSAVI performed best followed by SDVI for robust LAI inversion from high resolution imagery.     

Assessment of SPOT 5 and QuickBird remotely sensed imagery for mapping tree cover in savannas

The relative abundance and distribution of trees in savannas has important implications for ecosystem function. High spatial resolution satellite sensors, including QuickBird and IKONOS, have been successfully used to map tree cover patterns in savannas. SPOT 5, with a 2.5 m panchromatic band and 10 m multispectral bands, represents a relatively coarse resolution sensor within this context, but has the advantage of being relatively inexpensive and more widely available. This study evaluates the performance of NDVI threshold and object based image analysis techniques for mapping tree canopies from QuickBird and SPOT 5 imagery in two savanna systems in southern Africa. High thematic mapping accuracies were obtained with the QuickBird imagery, independent of mapping technique. Geometric properties of the mapping indicated that the NDVI threshold produced smaller patch sizes, but that overall patch size distributions were similar. Tree canopy mapping using SPOT 5 imagery and an NDVI threshold approach performed poorly, however acceptable thematic accuracies were obtained from the object based image analysis. Although patch sizes were generally larger than those mapped from the QuickBird image data, patch size distributions mapped with object based image analysis of SPOT 5 have a similar form to the QuickBird mapping. This indicates that SPOT 5 imagery is suitable for regional studies of tree canopy cover patterns.     

Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series

Satellite data holds considerable potential as a source of information on rice crop growth which can be used to inform agronomy. However, given the typical field sizes in many rice-growing countries such as China, data from coarse spatial resolution satellite systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) are inadequate for resolving crop growth variability at the field scale. Nevertheless, systems such as MODIS do provide images with sufficient frequency to be able to capture the detail of rice crop growth trajectories throughout a growing season. In order to generate high spatial and temporal resolution data suitable for mapping rice crop phenology, this study fused MODIS data with lower frequency, higher spatial resolution Landsat data. An overall workflow was developed which began with image preprocessing, calculation of multi-temporal normalized difference vegetation index (NDVI) images, and spatiotemporal fusion of data from the two sensors. The Spatial and Temporal Adaptive Reflectance Fusion Model was used to effectively downscale the MODIS data to deliver a time-series of 30 m spatial resolution NDVI data at 8-day intervals throughout the rice-growing season. Zonal statistical analysis was used to extract NDVI time-series for individual fields and signal filtering was applied to the time-series to generate rice phenology curves. The downscaled MODIS NDVI products were able to characterize the development of paddy rice at fine spatial and temporal resolutions, across wide spatial extents over multiple growing seasons. These data permitted the extraction of key crop seasonality parameters that quantified inter-annual growth variability for a whole agricultural region and enabled mapping of the variability in crop performance between and within fields. Hence, this approach can provide rice crop growth data that is suitable for informing agronomic policy and practice across a wide range of scales.     

Corn Area Extraction by the Integration of MODIS-EVI Time Series Data and China’s Environment Satellite (HJ-1) Data

In this paper, we intent to use the remotely sensed MODerate resolution Imaging Spectroradiometer (MODIS) data and China’s Environment Satellite (HJ-1) data for extracting the corn cultivated area over a regional scale. The high resolution HJ-1 data was to extract corn distribution at a small scale class with Support Vector Machine (SVM). The mean Enhanced Vegetation Index (EVI) time series curve of corn from MODIS was derived for the reference area and validated in a larger area. The MODIS-EVI time series curve derived from the reference area instead of the MODIS-EVI time series curve derived from the study area after validation, which was taken as the standard MODIS-EVI time series curve in for generating a standard MODIS-EVI image of corn. The mean absolute distance (MAD) between the standard MODIS-EVI image of corn and the MODIS-EVI time series image was used to detect the maximum possible extent of corn distribution in the study area. The results showed that the overall accuracy of the method was 82.17 %, with commission and omission errors of 16.85 and 15.40 %, respectively; at the county level, the satellite-estimated corn area and statistical data were well correlated (R ²?=?0.85, N?=?50) for the whole Jilin Province. It indicated that the MODIS data integrated with higher spatial resolution of HJ-1 satellite data could be utilized to enhance the extraction accuracy of corn cultivated area at a larger scale.     

A time-integrated MODIS burn severity assessment using the multi-temporal differenced normalized burn ratio (dNBRMT)

Burn severity is an important parameter in post-fire management. It incorporates both the direct fire impact (vegetation depletion) and ecosystem responses (vegetation regeneration). From a remote sensing perspective, burn severity is traditionally estimated using Landsat's differenced normalized burn ratio (dNBR). In this case study of the large 2007 Peloponnese (Greece) wildfires, Landsat dNBR estimates correlated reasonably well with Geo composite burn index (GeoCBI) field data of severity (R² = 0.56). The usage of Landsat imagery is, however, restricted by cloud cover and image-to-image normalization constraints. Therefore a multi-temporal burn severity approach based on coarse spatial, high temporal resolution moderate resolution imaging spectroradiometer (MODIS) imagery is presented in this study. The multi-temporal dNBR (dNBR_MT) is defined as the 1-year integrated difference between burned pixels and their unique control pixels. These control pixels were selected based on time series similarity and spatial context and reflect how burned pixels would have behaved in the case no fire had occurred. Linear regression between downsampled Landsat dNBR and dNBR_MT estimates resulted in a moderate-high coefficient of determination R² = 0.54. dNBR_MT estimates are indicative for the change in vegetation productivity due to the fire. This change is considerably higher for forests than for more sparsely vegetated areas like shrub lands. Although Landsat dNBR is superior for spatial detail, MODIS-derived dNBR_MT estimates present a valuable alternative for burn severity mapping at continental to global scale without image availability constraints. This is beneficial to compare trends in burn severity across regions and time. Moreover, thanks to MODIS's repeated temporal sampling, the dNBR_MT accounts for both first- and second-order fire effects.     

Comparative assessment of thematic accuracy of GLC maps for specific applications using existing reference data

Inputs to various applications and models, current global land cover (GLC) maps are based on different data sources and methods. Therefore, comparing GLC maps is challenging. Statistical comparison of GLC maps is further complicated by the lack of a reference dataset that is suitable for validating multiple maps. This study utilizes the existing Globcover-2005 reference dataset to compare thematic accuracies of three GLC maps for the year 2005 (Globcover, LC-CCI and MODIS). We translated and reinterpreted the LCCS (land cover classification system) classifier information of the reference dataset into the different map legends. The three maps were evaluated for a variety of applications, i.e., general circulation models, dynamic global vegetation models, agriculture assessments, carbon estimation and biodiversity assessments, using weighted accuracy assessment. Based on the impact of land cover confusions on the overall weighted accuracy of the GLC maps, we identified map improvement priorities. Overall accuracies were 70.8 ± 1.4%, 71.4 ± 1.3%, and 61.3 ± 1.5% for LC-CCI, MODIS, and Globcover, respectively. Weighted accuracy assessments produced increased overall accuracies (80–93%) since not all class confusion errors are important for specific applications. As a common denominator for all applications, the classes mixed trees, shrubs, grasses, and cropland were identified as improvement priorities. The results demonstrate the necessity of accounting for dissimilarities in the importance of map classification errors for different user application. To determine the fitness of use of GLC maps, accuracy of GLC maps should be assessed per application; there is no single-figure accuracy estimate expressing map fitness for all purposes.     

Landsat8和MODIS融合构建高时空分辨率数据识别秋粮作物

本文利用Wu等人提出的遥感数据时空融合方法 STDFA(Spatial Temporal Data Fusion Approach)以Landsat 8和MODIS为数据源构建高时间、空间分辨率的遥感影像数据。以此为基础,构建15种30 m分辨率分类数据集,然后利用支持向量机SVM(Support Vector Machine)进行秋粮作物识别,验证不同维度分类数据集进行秋粮作物识别的适用性。实验结果显示,不同分类数据集的秋粮作物分类结果均达到了较高的识别精度。综合各项精度指标分析,Red+Phenology数据组合对秋粮识别效果最好,水稻识别的制图精度和用户精度分别达到91.76%和82.49%,玉米识别的制图精度和用户精度分别达到85.80%和74.97%,水稻和玉米识别的总体精度达到86.90%。     

Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation

Abstract

While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties, the temporal resolution of the data is rather low, which can be easily made worse by cloud contamination. In contrast, although Moderate Resolution Imaging Spectroradiometer (MODIS) can only achieve a spatial resolution of 250 m in its normalised difference vegetation index (NDVI) product, it has a high temporal resolution, covering the Earth up to multiple times per day. To combine the high spatial resolution and high temporal resolution of different data sources, a new method (Spatial and Temporal Adaptive Vegetation index Fusion Model [STAVFM]) for blending NDVI of different spatial and temporal resolutions to produce high spatial–temporal resolution NDVI datasets was developed based on Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). STAVFM defines a time window according to the temporal variation of crops, takes crop phenophase into consideration and improves the temporal weighting algorithm. The result showed that the new method can combine the temporal information of MODIS NDVI and spatial difference information of HJ-1 CCD NDVI to generate an NDVI dataset with both high spatial and high temporal resolution. An application of the generated NDVI dataset in crop biomass estimation was provided. An average absolute error of 17.2% was achieved. The estimated winter wheat biomass correlated well with observed biomass (R ² of 0.876). We conclude that the new dataset will improve the application of crop biomass estimation by describing the crop biomass accumulation in detail. There is potential to apply the approach in many other studies, including crop production estimation, crop growth monitoring and agricultural ecosystem carbon cycle research, which will contribute to the implementation of Digital Earth by describing land surface processes in detail.