Assessment of the cropland classifications in four global land cover datasets: A case study of Shaanxi Province, China

Journal of Integrative Agriculture 2017, 16(2): 298–311 Available online at www.sciencedirect.com

ScienceDirect

RESEARCH ARTICLE

Assessment of the cropland classifications in four global land cover datasets: A case study of Shaanxi Province, China CHEN Xiao-yu1, LIN Ya1, ZHANG Min1, YU Le1, LI Hao-chuan2, BAI Yu-qi1 1

Key Laboratory for Earth System Modelling, Ministry of Education/Department of Earth System Science (DESS), Tsinghua University, Beijing 100084, P.R.China

2

National Information Center, Beijing 100045, P.R.China

Abstract Accurate and reliable cropland surface information is of vital importance for agricultural planning and food security monitoring. As several global land cover datasets have been independently released, an inter-comparison of these data products on the classification of cropland is highly needed. This paper presents an assessment of cropland classifications in four global land cover datasets, i.e., moderate resolution imaging spectrometer (MODIS) land cover product, global land cover map of 2009 (GlobCover2009), finer resolution observation and monitoring of global cropland (FROM-GC) and 30-m global land cover dataset (GlobeLand30). The temporal coverage of these four datasets are circa 2010. One of the typical agricultural regions of China, Shaanxi Province, was selected as the study area. The assessment proceeded from three aspects: accuracy, spatial agreement and absolute area. In accuracy assessment, 506 validation samples, which consist of 168 cropland samples and 338 non-cropland ones, were automatically and systematically selected, and manually interpreted by referencing high-resolution images dated from 2009 to 2011 on Google Earth. The results show that the overall accuracy (OA) of four datasets ranges from 61.26 to 80.63%. GlobeLand30 dataset, with the highest accuracy, is the most accurate dataset for cropland classification. The cropland spatial agreement (mainly located in the plain ecotope of Shaanxi) and the non-cropland spatial agreement (sparsely distributed in the south and middle of Shaanxi) of the four datasets only makes up 33.96% of the whole province. FROM-GC and GlobeLand30, obtaining the highest spatial agreement index of 62.40%, have the highest degree of spatial consistency. In terms of the absolute area, MODIS underestimates the cropland area, while GlobCover2009 significantly overestimates it. These findings are of value in revealing to which extent and on which aspect that these global land cover datasets may agree with each other at small scale on each ecotope region. The approaches taken in this study could be used to derive a fused cropland classification dataset. Keywords: land cover, cropland classification, assessment, MODIS, GlobCover2009, FROM-GC, GlobeLand30

1. Introduction Received 29 February, 2016 Accepted 30 June, 2016 Correspondence BAI Yu-qi, Mobile: +86-18611497925, E-mail: [email protected] © 2017, CAAS. All rights reserved. Published by Elsevier Ltd. doi: 10.1016/S2095-3119(16)61442-9

The fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC) (Pachauri et al. 2014) indicates that global warming is an indisputable fact for nearly a century (Hou et al. 2014). Scientists have gradually realized that land use and land cover change (LUCC) is one of the

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main causes of global climate change. Therefore, global land cover data are not only the most critical variable for climate change studies (Bounoua et al. 2002), but also a key source of information for understanding the complex interactions between human activities and global change (Running 2008). Cropland is the primary material condition of agricultural production. Its quality and quantity can directly reflect the fundamental reality and resource endowment of one country. As the fluctuation of grain output is greatly influenced by the change of cropland, global food security issues are closely connected to cropland (Foley et al. 2011). Mastering the global cropland extent not only has a guidance significance for guaranteeing food security and maintaining social stability, but also can provide useful information for farmers, crop yield predictors, food market researchers, and policy/ decision-makers (Liang and Gong 2013). Therefore, a thematic map that can accurately reflect the cropland distribution is highly required, at provincial, state, continental or even global scales. Several global land cover datasets have been developed during the last few decades to get fine classification of land cover at global scale. Cropland, as a significant factor for human activities, existence and multiplication, has been incorporated in all the datasets. Moderate resolution imaging spectrometer (MODIS) land cover product used spectral and temporal information derived from composited 8-day MODIS data in conjunction with ancillary data to provide annual global land cover maps (Friedl et al. 2010). It introduces five classification schemes and two of them contain cropland types. In 2010, the European space agency published a 300 m global land cover map named global land cover map of 2009 (GlobCover2009) (Bontemps et al. 2011). Medium resolution imaging spectrometer full resolution (MERIS FR) time serie was used as the raw material and the land cover classification system (LCCS) (Gregorio A D and Jansen 1998) was utilized as the classification scheme (See et al. 2013). The first 30 m resolution global land cover map, finer resolution observation and monitoring of global land cover (FROM-GLC) were released by Center for Earth System Science at Tsinghua University, China. Besides Landsat satellite imagery, several multi-resolution datasets were utilized as well, such as auxiliary bioclimatic, digital elevation model (DEM) and world maps on soil-water conditions. It had been improved by a segmentation-based approach to heighten the accuracy of classification to form a more advanced version of land cover dataset named FROM-GLC-seg (segmentation) as a consequence (Yu et al. 2013b). Finer resolution observation and monitoring of global cropland (FROM-GC), based on FROM-GLC, FROM-GLC-agg (aggregation) (Yu et al. 2014) and a 250-m cropland probability map, is a 30-m spatial resolution global cropland extent product (with other

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land cover types) (Yu et al. 2013a). 30-m global land cover dataset (GlobeLand30) was generated using an automated pixel-based and object-based classification method (Chen et al. 2015). Even though cropland information can be derived from global land cover datasets, cropland classification of these datasets exhibit great difference. Therefore, comparative studies are necessary to assess their discrepancies. Giri et al. (2005) identified the global spatial agreement and disagreement of global land cover 2000 (GLC2000) and MODIS by area comparison. They found that the percent agreement of global cropland area was 86.5%. Hansen and Reed (2000) confirmed the global cropland agreement of data and information systems global land cover product (DISCover) (Loveland and Belward 1997) and the University of Maryland (UMD) global land cover classification (Hansen et al. 2000) is about 80% by per-pixel comparison. In a spatial and quantitative comparison of MODIS, GLC2000 and national land cover/use datasets (NLCD) over China, fuzzy agreement analysis showed the overall agreement ranged from 50.8 to 52.9%; accuracy assessment referenced by multiple sources presented that their overall accuracy (OA) was around 50% (Gao and Jia 2012). To investigate the degree of agreement among different datasets on a quantitative basis, Bai et al. (2014) proposed a consistency index based on pixel-based confusion matrices rather than accuracy to calculate their spatial agreement over China. Among all the datasets, GLC2000 has the highest agreement (52.2%) with the geodata land cover dataset for year 2005 (GLCD-2005), while UMD has the highest disagreement (66.9%) with GLCD-2005. Several studies have been undertaken to compare these datasets on one specific land cover type at large scale. For example, Liang and Gong (2013) made an evaluation of four global land cover datasets for cropland area estimation in the conterminous United States, which demonstrated the correlation between land cover map estimates and survey estimates are significant. A case study of Ethiopia showed that cropland recorded in global land cover products had lower accuracy than the data collected through Geo-Wiki System (See et al. 2013). Although these studies on comparing land cover datasets at large scale are valuable, identifying the fine difference among them at small scale with a focus just on one important land cover type is still desired. In particular, issues on the land cover type definitions, accuracy of classification in different ecotopes, impacts from the original data used and the process utilized to the accuracy could be further explored. The purpose of this paper is to present an assessment of the classification of cropland in these four global land cover datasets: MODIS, GlobCover2009, FROM-GC and Globeland30. Shaanxi, a major agricultural province

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of China, was selected as the study area. The evaluation proceeds from three aspects: accuracy, spatial agreement and absolute area. Firstly, 506 validation samples were automatically selected using a systematic approach. They were manually interpreted based on Google historical high-resolution images. These samples were used to assess the accuracy of cropland classification in each dataset. Secondly, inter-comparison was applied to identify the spatial distribution of pairwise cropland spatial agreements among these datasets. Finally, a comparative study of the cropland areas of four datasets with the records in the statistical yearbook was carried out to show the differences in the total cropland area.

2. Materials and methods 2.1. Study area Shaanxi, a major agricultural province located in the northwestern heartland of China (105.49–111.27°E, 31.70–39.59°N), was selected as the study area. With the elevation varying from 157 to 3 748 m, Shaanxi has various types of landforms, including plateau, plain, mountain land, basin, transition zone, which results in its complicated natural conditions (Li 2007). Liu et al. (2002) put forward a comprehensive regionalization scheme of Shaanxi, which divided the province into five ecotopes: desert and semi-desert, forest

steppe, plain, mountain forest and subtropics forest (Fig. 1). Cropland is unevenly distributed in the whole province and usually mixed with other land types, such as forest and grassland. Therefore, Shaanxi is quite representative for cropland classification analysis. Evaluating the classification of cropland in these global land cover datasets in Shaanxi is useful in revealing fine difference among them.

2.2. Global land cover datasets The MODIS dataset used normalized bidirectional reflectance distribution function (BRDF) adjusted reflectance, spectral and temporal information from MODIS data and enhanced vegetation index to generate a product of 500 m spatial resolution since 2001 by implementing algorithm ensemble decision tree (Friedl et al. 2010). MODIS land cover type collection 4 (MLCT4) product and a new global dataset of croplands and pastures circa 2000 (Ramankutty et al. 2008) are employed to minimize spatially explicit prior probabilities introduced by biases inherent to treebased classification models, and the sample bias imposed by training data properties. A linear transformation and stabilization of results between years is closely linked with and based upon posterior probability and special cases such as urban land use, wetlands, and deciduous needle leaf forests are dealt with a threshold (Friedl et al. 2010). 14-class UMD, 10-class MODIS leaf area index data (LAI)/

Fig. 1 The study area of Shaanxi, which located in the northwest heartland of China. A, the world map. B, map of Shaanxi Province. C, map of China. I to V represent five ecotopes in Shaanxi. I, desert and semi-desert ecotope; II, forest steppe ecotope; III, plain ecotope; IV, mountain forest ecotope; V, subtropics forest ecotope. The same as below.

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fraction of photosynthetically active radiation absorbed by the canopy data (FPAR), 8-class biome, and 12-class plant functional type layers are produced by cross-walking the 17-class international geosphere biosphere programme (IGBP) layer to pre-mentioned classification schemes using the corresponding posterior probabilities in conjunction with globally accepted classifications for leaf type, phenology and crop type as basis, among which IGBP and UMD have the classification croplands (Friedl et al. 2010). This paper selected the IGBP layer of MODIS as one of the datasets to be examined. The data can be accessible through the website of land processes distributed active archive center (https://lpdaac.usgs.gov/dataset_discovery/modis/ modis_products_table). GlobCover2009, a land cover map with resolution of 300 m, based on time series of medium resolution imaging spectrometer full resolution full swath (MERIS FRS) 2009 mosaics, was produced by an automated service and classification method with underlying principle of regional tune. Through preprocessing steps like geometric corrections, cloud screening, atmospheric corrections and BRDF correction and time compositing, level 1B MERIS FR was transformed into level 3 MERIS Mosaics FR to generate the final product (Arino et al. 2010). After stratification according to natural discontinuities, four processes are then followed in the next stage of generating land cover map: per-pixel classification algorithm consists of supervised method targeting at classes that are not so well represented and unsupervised clustering of spectrally similar pixels, per-cluster temporal characterization, per-cluster classification algorithm and rule based labeling procedure (Bontemps et al. 2011). The global land cover map references the United Nations (UN) land cover classification system (LCCS) to define 22 land cover classes. Croplands are divided into four types: post-flooding or irrigated croplands, rainfed croplands, mosaic cropland (50–70%)/vegetation (grassland, shrubland, forest) (20–50%), mosaic vegetation (grassland, shrubland, forest) (50–70%)/cropland (20–50%) (Bontemps et al. 2011). Data used for calibration are global land cover map of 2005 (GlobCover2005) (Defourny et al. 2006; Bichoron et al. 2008), which contributes for corrections on the unevenly captured data coverage and on flooded forest. The shuttle radar topography mission (SRTM) water body data are an extra dataset to improve water bodies delineation (Bontemps et al. 2011). The data can be downloaded at European space agency data user element (http://due.esrin.esa.int/ page_globcover.php). FROM-GC is a 30-m spatial resolution global cropland extent (with other land cover types) product in the year of 2010. It is based on FROM-GLC (Gong et al. 2013) and FROM-GLC-agg (Yu et al. 2014). It uses the nighttime light impervious surface area and MODIS urban extent in the

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procedure of aggregation, and is supplemented by a 250-m cropland probability map and the Food and Agriculture Organization Corporate Statistical (FAOSTAT) database (Yu et al. 2013a). Decision tree method is chosen and implemented after two 250 m cropland masks: one is a regional 250 m MODIS cropland probability data and the other is generated by the common land cover validation sample database which is consisted of 3 945 cropland samples and 5 852 grasslands samples (Yu et al. 2013a). FROM-GC classifies cropland into several types, i.e., croplands (paddy rice, greenhouse, others), orchard and bare herbaceous croplands (Yu et al. 2013a). Data are accessible on the Tsinghua website (http:// data.ess.tsinghua.edu.cn). Globeland30 with 30-m spatial resolution is another land cover map published by China. It took Landsat thematic mapper (TM) and enhanced thematic mapper plus (ETM+) images of year 2000 and 2010 as original data source and Chinese environmental and disaster satellite (HJ-1)’s imageries as supplementary data. It also integrated pixel-based and object-based methods with interactively knowledge-based verification procedure (POK-based) to accomplish the task of classifications of 10 land cover types through split-and-merge strategy (Chen et al. 2014). A web-service oriented integration of valuable ancillary data, such as those global land cover data with coarser resolution, regional data of 30-m of higher resolution, global DEM and topographic data, ecological zones, online mapping services (e.g., Google map, OpenStreetMap), as well as services associated with land cover (e.g., Geo-Wiki) were included in support of later manual verification procedure (Chen et al. 2015). The final land cover category consists of 10 land cover types: water bodies, wetland, permanent snow/ice, artificial surfaces, cultivated land, forest, shrubland, grassland, bareland and tundra (Chen et al. 2015). The Globeland30 data are online accessible as well (http:// www. globallandcover.com/GLC30Download/index.aspx).

2.3. Validation data Validation data are used to assess the accuracy of cropland classification results in these four global land cover datasets. Since there is no existing cropland reference data available to validate these datasets, we generated the validation samples by ourselves in this study. The selection criteria and steps are discussed below. (1) Homogeneous distribution: To ensure a representative result and expel the particularity of validation data, the study area was divided into 0.2°×0.2° grids, and the center of each grid was selected as the validation samples. (2) Temporal consistency: Only if the time of validation samples corresponds to the mapping time of the global land cover datasets, can the assessment be effective and

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meaningful. Since all of the global land cover datasets were produced around 2010 and the cropland information is easy to interpret during summer, Google historical images of June to October of 2010 were regarded as the main reference data. However, part of the reference data of 2010 were unavailable in Google Earth. Therefore, a small number of samples were collected from the historical images of years 2009 and 2011. Landsat TM images were used as the supplementary materials, if Google images were not available during the summer of 2009–2011. (3) Interpretation criterion: In this paper, tillage, fallow land, orchard and tea plantation are defined as cropland. Before interpreting, we used the ruler tool of Google Earth to draw a 500 m×500 m square centered on each validation sample. Only if over 60% region belongs to cropland can the sample be interpreted as cropland. Based on these principles, 506 validation samples, including 168 cropland samples and 338 non-cropland samples, were labelled after visual interpretation, as shown in Fig. 2.

2.4. Method Data preprocessing Due to the differences among these datasets on spatial references, spatial resolution and cropland definitions (Table 1), a specific preprocessing step was designed in this study. The mosaic tool of ArcGIS software and the vector data of Shaanxi Province were jointly used to mosaic and then clip the multiple images together for a complete land cover map for the whole study area for each dataset. Since the spatial references have direct influence on area calculation and spatial agreement analysis, this study made transformation base on the spatial reference of WGS_1984_UTM_zone_48 N. Project raster tool of ArcGIS software was used to project or re-project the land cover map of MODIS, GlobCover2009 and FROM-GC to UTM_zone_48N projection. Since different global land cover datasets usually take their own classification schemes, the problem of having them compatible with each makes the inter-comparison of multiple land cover data problematic (Herold et al. 2006; Mccallum et al. 2006; See et al. 2013; Waldner et al. 2015). Instead of rigidly treating all cropland related land cover types to be cropland, we gave each type a weight (0–1) to indicate its cropland percentage where ‘0’ stands for non-cropland and ‘1’ for cropland. For example, cropland/natural vegetation mosaics defined as one mixed land type in MODIS was assigned a weight of 0.5. Mosaic cropland (50–70%)/ vegetation (20–50%) and mosaic vegetation (50–70%)/ cropland (20–50%) in GlobCover2009 were assigned 0.6 and 0.4, respectively. The rest of land types listed in Table 2 were considered as pure cropland types, the weights of

Fig. 2 The geolocation of the validation samples.

which were all assigned as 1. Based on this criterion, all the land cover maps were reclassified to cropland map by ArcGIS software. Spatial resolution is of vital importance for spatial agreement analysis. As shown in Table 1, spatial resolution of MODIS, GlobCover2009, FROM-GC and GlobeLand30 are 500, 300, 30 and 30 m, respectively. To match the unified spatial resolution, the resample tool of ArcGIS software was used to resample all the datasets to 500 m. A performance test on the four types of resample methods, i.e., Nearest Neighbor, Bilinear, Cubic and Majority were performed. Each method was tested on GlobCover2009, FROM-GC and GlobeLand30 to determine the best one for use in this study. The testing results are shown in Table 3. According to this result, Nearest Neighbor method retains cropland information better than others, because it imposes the minimal impact on cropland percentage after resampling (0.02 to 0.07%). Therefore, the resampling results of Nearest Neighbor method were selected for further analyses. Accuracy analysis Accuracy assessment is to measure the veracity of cropland classification in different global land cover datasets. This paper assessed the cropland classification results in each of the five ecotopes in Shaanxi Province using the confusion matrix method. 168 cropland validation samples and 338 non-cropland ones were utilized

Cultivated and managed terrestrial areas/ natural and semi-natural primarily terrestrial vegetation.

Mosaic cropland (50–70%)/vegetation (20–50%)

Parcels planted with fruit trees or shrubs; Single or mixed fruit species; fruit trees associated with permanently grassed surfaces. Just harvested, fallow land and all other types of land not covered by vegetation. Lands used for agriculture, horticulture and gardens including paddy fields, irrigated and dry farmland, vegetation and fruit gardens.

Bare herbaceous croplands 83.51 Cultivated land

Mosaic vegetation (50– Natural and semi-natural primarily terrestrial 70%)/cropland (20–50%) vegetation/cultivated and managed terrestrial areas. Croplands Arable and tillage land; land for rice cultivation, land with plastic foam or grass roof protection with distinguishing spectral properties.

Rainfed shrub crops; rainfed tree crops; rainfed herbaceous crops.

Rainfed croplands

Orchard

–

60

Lands with a mosaic of croplands, forests, shrubland, and grasslands in which no one component comprises more than 60% of the landscape. Post-flooding or irrigated Irrigated tree crops; irrigated shrub crops; croplands irrigated herbaceous crops; post-flooding cultivation of herbaceous crops.

Cropland/natural vegetation mosaics

Overall accuracy Cropland related types Class definitions (%) 75 Croplands Lands covered with temporary crops followed by harvest and a bare soil period.

1 1

1

1

0.4

0.6

1

1

0.5

1

Cropland weight

FROM-GC dataset was assessed by cropland area rather than validation samples, so it does not have overall accuracy. Its cropland area shows a good agreement (R2=0.97) with that of FAOSTAT database.

WGS_1984 UTM_zone _48 N

Supervised classification, object oriented classification

Chen et al. (2014)

GlobeLand30 (2010)

Landsat TM/ETM+, land cover data, MODIS NDVI, DEM, thematic data and online resources (Google map, Bing map, OpenStreetMap and Map World)

FROM-GLC, FROMMap integration, GLC-agg, FROM-GLCdecision tree seg, MODIS cropland extension, global common validation samples, FAOSTAT database

WGS_1984

Supervised classification, unsupervised classification

Supervised classification, decision tree

Yu et al. (2013a)

Monthly MODIS L2/L3 composites, EOS land/ water mask, MODIS 16day EVI, MODIS 8-day land surface temperature, DEM

Classification method

FROM-GC (2010)

WGS_1984 Custom spheroid sinusoidal

Input data

MERIS LIB data MERIS FR mosaics

Friedl et al. (2010)

MODIS (2010)

Spatial references

GlobCover2009 Bontemps GCS_WGS_ (2009) et al. (2011) 1984

Developer

Global maps

Table 1 Four Land cover datasets examined in this study

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Table 2 The cropland weight of each cropland related types in MODIS, GlobCover2009, FROM-GC and GlobeLand30 datasets Datasets MODIS

Cropland related types Croplands Cropland/Natural vegetation mosaics Post-flooding or irrigated croplands Rainfed croplands Mosaic cropland (50–70%)/Vegetation (20–50%) Mosaic vegetation (50–70%)/Cropland (20–50%) Croplands Orchard Bare herbaceous croplands Cultivated land

GlobCover2009

FROM-GC

GlobeLand30

Cropland weight 1 0.5 1 1 0.6 0.4 1 1 1 1

Table 3 The percentage of cropland area in GlobCover2009, FROM-GC and GlobeLand30 datasets before and after resampling1) Dataset

Original 54.98 34.28 30.66

GlobCover2009 FROM-GC GlobeLand30 1)

Percentage of cropland (%) Nearest neighbor Bilinear 54.99 (0.02) 54.96 (0.04) 34.27 (0.03) 34.28 (0.00) 30.64 (0.07) 31.26 (1.96)

Cubic 55.28 (0.55) 34.59 (0.90) 30.67 (0.03)

Majority 54.79 (0.35) 32.51 (5.16) 30.04 (2.02)

Numbers out of parentheses are cropland percentage, whereas those in parentheses are the rate of cropland change after resampling. For example, 54.99 (0.02) means the cropland percentage of GlobCover2009 after nearest neighbor resampling is 54.99%, and its rate of cropland change is 0.02%.

to calculate the confusion matrix of different cropland maps. When interpreting the validation sample on Google Earth, we classified a sample as cropland only if 60% or more of the surrounding areas are cropland. Therefore, during this accuracy analysis step, only land types whose cropland weight are equal to or greater than 0.6 were considered. That is to say, the “cropland/natural vegetation mosaics” in MODIS, and “mosaic vegetation (50–70%)/cropland (20–50%)” in GlobCover2009 were not counted. The accuracy indices chosen to assess the cropland classification accuracy were user’s accuracy (UA), produce’s accuracy (PA), F-score index, OA and kappa coefficient (Kappa). These indices are defined as follows: UA=

X ii X i+

(1)

PA=

X ii X +i

(2)

F-score=

2UA×PA UA+PA

(3)

n

OA=

∑ X ii i=1

(4)

N2 n

Kappa=

n

N ∑Xii − ∑ Xi+ X+i i=1

i=1

n

N −∑ Xi+ X+i 2

i=1

(5)

Where, i is the class (cropland or non-cropland); X ii refers to the number of samples that were correctly classified; Xi+ is the number of samples in the cropland maps, X+i stands for the number of samples in validation data, and N is the total number of all the validation samples.

Spatial agreement analysis Spatial agreement analysis is to reveal the spatial differences and similarities among different datasets. It is very useful for both data producers and users, since data producers can easily select training areas, and users can conveniently select study areas that meets their specific requirements (Giri et al. 2005; Gao and Jia 2012). The spatial agreement analysis was performed by qualitative and quantitative comparison. In qualitative comparison, the cropland maps of four datasets were overlaid together to obtain the spatial agreements (areas with the same cropland weight) and disagreements (areas with different cropland weights). Then, a visual comparison was applied to analyze their spatial variation of the cropland among different ecotopes. Quantitative comparison further presented the degree of spatial agreement between each two of four datasets by calculating the spatial agreement index SAij (0–100%) (Yang et al. 2014). If the cropland of two datasets are exactly overlapped, the SA will be assigned a value of 100%. The equation to compute SAij is: SAij=

2 Aij

Ai+ Aj

(6)

Where, SAij refers to the cropland spatial agreement index of dataset i and dataset j, Aij stands for the area of cropland agreement between dataset i and dataset j, Ai is the cropland area of dataset i and Aj is the cropland area of dataset j. Absolute area analysis The aim of absolute area analysis is to compare the cropland area of the cropland maps of different datasets with official statistical records. China Statistical Yearbook (National Bureau of Statistics of China 2011) is the most authoritative and comprehensive statistical

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data source in China. The Shananxi Statistical Yearbook 2011 (Shaanxi Bureau of Statistics and National Bureau of Statistics Investigation Team in Shaanxi 2011) that records the statistics for the year of 2010 was utilized as a reference for the cropland area in this province. This study first calculated the cropland area of each cropland maps by eq. (7): N

A=m∑wi ni

(7)

i=1

Where, A is the cropland area of one dataset; N is the number of cropland related types; wi stands for the cropland weight of land type i, ni equals to the number of pixels of type i, and m is the pixel area. Pixel areas and pixel counts were calculated before resampling to reduce the influence of mixed pixel.

3. Results 3.1. Accuracy assessment The confusion matrices of four datasets were established based on the validation samples. The resulting UA, PA, F-score, OA and kappa coefficient were shown in Table 4, and the detailed accuracy assessment results of each ecotope were displayed in Fig. 3. GlobeLand30 dataset, with the highest OA (80.63%), outperforms other datasets. Its kappa coefficient of 55.54% indicates that it has substantial classification consistency with the validation data. For FROM-GC and MODIS, their OA are 77.67 and 77.47%, respectively. Their kappa coefficients rang from 43.55 to 50.32%. Globcover2009 has the lowest OA (61.26%) and only fair classification consistency with validation data because of its low kappa coefficient (23.65%). In terms of cropland, GlobeLand30 has the highest F-score of 69.75%. The UA of Globcover2009 is the lowest one, i.e., only 44.70%, which implies its high commission rate for cropland classification. Although MODIS has the highest UA of 75.73% for cropland classification, its PA is only 46.43%. It means MODIS is not likely to misclassify other land types as cropland, but tends to underestimate cropland a lot. Compared with cropland, the UA of non-crop and among four datasets (about 80%) are all higher than

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that of cropland, indicating their low commission rate. The PA of four datasets (ranging from 56.80 to 92.90%) vary significantly. Among them, the PA of Globcover2009 is the lowest one, which means its omission rate for non-cropland is higher than that of others. In addition, the highest F-scores (85.76%) of non-cropland is owned by GlobeLand30 as well. The F-scores of cropland show that all the datasets perform well in the plain ecotope (over 80%), but perform poorly in subtropics forest ecotope (less than 40%). The cropland classification effects of MODIS and GlobCover2009 in desert and semi-desert ecotope are poor, for their F-score is only about 20%. As for forest steppe ecotope and mountain forest ecotope, their F-scores of cropland are moderate, i.e., about 60%, but there is an exception that GlobCover2009 only gets the F-score of 33% in mountain forest ecotope, which means GlobCover2009 is not good at extracting cropland in mountainous area as well. In terms of non-cropland, the classification result of mountain forest ecotope, desert and semi-desert ecotope and subtropics forest ecotope among four datasets (82 to 97%, except for GlobCover2009 in subtropics forest ecotope) are better than that of forest steppe ecotope and plain ecotope (44 to 79%). Moreover, the non-cropland F-score of MODIS and FROM-GC are approximate to that of GlobeLand30 in five ecotopes, while the non-cropland F-score of GlobCover2009 is always lower than that of others, especially in forest steppe ecotope (about 45%). Based on the kappa coefficient results, GlobeLand30 outperforms other datasets in each ecotope. The kappa coefficient of MODIS, FROM-GC and GlobeLand30 are close. They get high values in plain ecotope and mountain forest ecotope (about 60%) and gain low values in subtropics forest ecotope (about 20%). What is different is that the kappa coefficient of MODIS in desert and semi-desert ecotope is pretty low, i.e., only about 10%. Compared with other datasets, the kappa coefficient of GlobCover2009 in each ecotope is much lower (less than 22%), except for plain ecotope. In addition, the OA of GlobCover2009 is usually the lowest one, while the OA of other dataset is relatively higher and close to each other. Generally, the accuracy of four datasets roughly follows the same trend in different ecotopes: plain ecotope and

Table 4 Accuracy analysis results of MODIS, GlobCover2009, FROM-GC and GlobeLand30, compared with 506 validation samples Land cover datasets MODIS GlobCover2009 FROM-GC GlobeLand30 1) 2)

Accuracy of cropland (%)1) UA PA F-score 75.73 46.43 57.56 44.70 70.24 54.63 65.54 69.05 67.25 72.44 67.26 69.75

Accuracy of non-cropland (%) UA PA F-score 77.72 92.90 84.64 79.34 56.80 66.20 84.19 81.95 83.06 84.29 87.28 85.76

UA, user’s accuracy; PA, producer’s accuracy; F-score, harmonic mean of PA and UA. OA, overall accuracy; Kappa, kappa coefficient.

Overall accuracy (%)2) OA Kappa 77.47 43.55 61.26 23.65 77.67 50.32 80.63 55.54

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MODIS

GlobCover2009

A

B

20 0

F-score of non-cropland (%)

C

Kappa coefficient (%)

40

I

II

III

IV

D

100

60 40 20 I

II

III

IV

V

I

II

III

IV

V

100 80

80 60 40

60 40 20

20 0

80

0

V

OA (%)

F-score of cropland (%)

60

GlobeLand30

100

100 80

FROM-GC

0 I

II

III

IV

V

Fig. 3 The accuracy analysis results of five ecotopes (I–V) in MODIS, GlobCover2009, FROM-GC and GlobeLand30 datasets. A, the F-score of cropland in five ecotopes. B, the kappa coefficient of five ecotopes. C, the F-score of non-cropland in five ecotopes. D, the overall accuracy (OA) of five ecotopes.

mountain forest ecotope obtain the best classification effect; desert and semi-desert ecotope and subtropics forest ecotope come to the second; forest steppe ecotope reaches the poorest classification effect. Among them, MODIS and GlobCover2009 perform poorly in cropland classification in desert area and GlobCover2009 is insensitive to cropland in mountain forest ecotope.

3.2. Spatial distribution of pairwise cropland agreements Fig. 4-A–D present the cropland classification of four datasets and their spatial agreement of cropland and non-cropland. In contrast, the cropland in MODIS dataset only concentrates in plain ecotope and the south of forest steppe ecotope. For FROM-GC and GlobeLand30 datasets, the cropland not only locates in the central plain of Shaanxi, but also in the north of the province, i.e., desert and semi-desert ecotope and forest steppe ecotope. In GlobCover2009, cropland is distributed almost evenly in the whole province. As for areas of cropland spatial agreement and disagreement, the agreements mainly exist in plain ecotope; the non-cropland agreement pixels sparsely spread

in the north of desert and semi-desert ecotope, the central of forest steppe ecotope, the south of subtropics forest ecotope and mountain forest ecotope. The rest of the whole regions are all belong to spatial disagreement regions, which reveals the significant difference of the spatial distribution of cropland among these datasets. The results of quantitative spatial agreement analysis are shown in Table 5. With an agreement index of 62.40%, the cropland results of FROM-GC and GlobeLand30 datasets have the highest spatial consistency. The cropland agreement of FROM-GC&MODIS and GlobeLand30& MODIS comes the second, for their agreement index is above 50%. As for other datasets, their spatial agreement indices are only about 40%. From the perspective of ecotope, all the datasets obtain the highest spatial consistency in plain ecotope (69.83 to 86.50%), and get pretty low agreement index in desert and semi-desert ecotope (less than 10%) except for GlobeLand30&FROM-GC (55.43%). Although the overall agreement between GlobeLand30 and FROMGC, GlobeLand30 and MODIS are high, their SA values of mountain forest ecotope is only 6.8 and 4.3%, which reflects that the cropland classification of GlobeLand30 in

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Fig. 4 The distribution of cropland and their spatial agreements in four datasets. A, cropland distribution and agreement in MODIS. B, cropland distribution and agreement in GlobCover2009. C, cropland distribution and agreement in FROM-GC. D, cropland distribution and agreement in GlobeLand30. Cropland weight (0–1) is expressed by cropland percentage (0–100%).

mountainous area are usually significantly different from that of FROM-GC and MODIS. Furthermore, the overall area of the agreement among four datasets only makes up 33.96% (cropland: 9.12%, non-cropland: 24.84%) of the whole province area.

3.3. Absolute area analysis results According to Shaanxi Statistical Yearbook 2011 (Shaanxi Bureau of Statistics and National Bureau of Statistics Inves-

tigation Team in Shaanxi 2011), statistics that are related to the cropland definition set in this paper are total sown area, area of orchards, area of tea plantation and area of mulberry field. The total area of each type of cropland is 41 856, 10 833, 854 and 1 083 km2, respectively Shaanxi Statistical Yearbook 2011 (Shaanxi Bureau of Statistics and National Bureau of Statistics Investigation Team in Shaanxi 2011). Therefore, the total cropland area is 54 626 km2 based on the assumption that the area of orchards, tea plantation and mulberry do not overlay with the total sown

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area. It is important to note that since the fallow field was not included in the Shaanxi Statistical Yearbook 2011 (Shaanxi Bureau of Statistics and National Bureau of Statistics Investigation Team in Shaanxi 2011), this total cropland area is actually an underestimation of the actual cropland area in Shaanxi Province. The area calculation results are shown in Table 6. The estimation in MODIS dataset is the smallest one in these four datasets, and the estimation in GloCover2009 is the biggest one. Taking into account that the total area derived from Shaanxi Bureau of Statistics and National Bureau of Statistics Investigation Team in Shaanxi (2011) underestimates the actual area, MODIS dataset underestimates the absolute cropland area in this province, while GloCover2009 significantly overestimates it.

4. Discussion This study shows that the cropland classifications in these four global land cover datasets exhibit a great variation in accuracy assessment, spatial distribution and absolute area. The most likely reasons can be explained from several aspects.

4.1. Cropland definition During the preprocessing step, all the global land cover datasets were reclassified to cropland thematic map accord-

ing to the criterion set in this paper. However, the cropland unified criterion cannot completely expel the effect of their different classification schemes because of the significant differences of their own cropland definitions (Table 1). Cropland definitions of all the datasets contain cultivated land which is used to plant rice, fruits or some other crops. In addition to the common ground, MODIS and GlobCover2009 datasets involves shrubs, trees, natural and semi-natural primarily terrestrial vegetation, grassland and even forest to their cropland maps (Friedl et al. 2010; Bontemps et al. 2011). This paper only defined tillage, fallow land, orchard, tea plantation as cropland, land types like forest, shrub, grassland were all regarded as non-cropland. Cropland weights (Table 2) were defined to reduce error, but they cannot precisely describe the accurate cropland proportion, which might influence their cropland area and classification accuracy. Note that the area of cropland/natural vegetation mosaics type only accounts for about 20% of the cropland area in MODIS. Hence, the difference of cropland definition does not exert obvious effect on its absolute cropland area. The cropland definition of GlobeLand30 and FROM-GC are relatively close, which could be part of reason why they have the highest spatial consistency and similar accuracy. For GlobeLand30, the land type reclassified to cropland only includes cultivated land whose definition is much alike to cropland definition set in this paper. Therefore, its F-scores of both cropland and non-crop land are higher than that of other datasets.

Table 5 Pairwise spatial agreement index SA value of MODIS, GlobCover2009, FROM-GC and GlobeLand30 in Shaanxi Province Region1) I II III IV V Overall 1)

FROM-GC & GlobCover 4.09 50.53 72.17 33.08 35.39 46.89

FROM-GC & MODIS 7.09 46.67 86.50 48.84 77.88 52.63

GlobeLand30 & FROM-GC 55.43 59.39 84.30 50.49 6.80 62.40

GlobeLand30 & GlobCover 2.23 43.84 69.83 32.65 37.26 43.45

GlobeLand30 & MODIS 9.66 43.76 84.77 44.91 4.30 50.76

GlobCover & MODIS 1.28 42.04 74.34 32.06 35.91 41.18

I, desert and semi-desert ecotope; II, forest steppe ecotope; III, plain ecotope; IV, mountain forest ecotope; V, subtropics forest ecotope; Overall, the whole province. The same as below.

Table 6 Comparison of cropland area between Global land cover datasets and records in Shaanxi Bureau of Statistics and National Bureau of Statistics Investigation Team in Shaanxi (2011)1) Region I II III IV V Overall 1)

MODIS 255.3 20 728.5 14 974.8 4 905.4 6 563.4 47 427.4

Cropland area (km2) GlobCover2009 FROM-GC 3 913.9 5 555.5 54 247.5 38 735.1 14 849.8 13 670.4 15 776.5 4 330.0 24 355.3 8 254.8 113 143.0 70 545.8

GlobeLand30 4 292.2 32 124.5 12 881.2 4 342.6 9 447.9 63 088.4

Records in statistical yearbook (km2) Total sown area Area of orchards Area of tea plantation Area of mulberry field Total area of cropland1)

41 856 10 833 854 1 083 54 626

The total area of cropland was calculated by adding total staple crop sown area, orchards, tea plantation and mulberry field area together.

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4.2. Source data The 500-m MODIS images, the major input data of MODIS, determine its spatial resolution of 500 m (Friedl et al. 2010), while the 300-m MERIS images, the major input data of GlobCover2009, determine that the spatial resolution of GlobCover2009 is 300 m (Bontemps et al. 2011). For other 30-m datasets, they all used 30-m images as major input data. It can be concluded from the spatial agreement analysis that datasets derived from the input data with same spatial resolution, i.e., FROM-GC, GlobeLand30, usually obtain higher degree of spatial agreement (over 60%). According to the accuracy results, we can see the higher the spatial resolution of input data, the higher accuracy the datasets achieve (except for GlobCover2009, whose low accuracy might be explained by other factors). Even though all the datasets were resampled to the same resolution, resolution was not a factor that influence the analysis result any more. But we cannot deny that higher resolution images are more beneficial to classification. Therefore, spatial resolution of input data has significant influence on cropland classification. Supplementary data used to derive the classification play an important part in increasing classification accuracy as well. For example, the cropland accuracies of GlobeLand30 and FROM-GC were greatly influenced by its various reference data, including high resolution images, DEM, thematic maps, vegetation index data, Google Earth photos, etc. (Yu et al. 2013a; Chen et al. 2014; Gong et al. 2016).

4.3. Classification method The classification method employed by different global land cover datasets varies from one to another. MODIS used supervised decision-tree classification to classify the land types. For Globcover2009, supervised classification was utilized to identify the land cover classes that are not well represented, such as urban and wetland areas, while unsupervised classification was then applied to create clusters of spectrally similar pixels and classify the remaining classes including cropland (Bontemps et al. 2011). Its original OA of all the classes is about 60%, which is quite similar to its cropland accuracy (61.26%) calculated in this paper. Focusing on the global cropland extent, FROM-GC was obtained by combining various cropland products together using decision tree (Yu et al. 2013a). Taking the advantage of various datasets, the cropland classification of FROM-GC in Shaanxi Province achieves relatively high accuracy (OA: 77.67%). GlobeLand30, with a global cropland overall accuracy of 83.06%, which just kept the consistency of the OA (80.63%) of this paper, not only employed the maximum likelihood classification (MLC), support vector machine (SVM)

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supervised classification and object-based classification, but also conducted human-computer interactive check and verification to make corrections after classification (Chen et al. 2014). According to the accuracy analysis results, it can be deduced that all the classification methods do well in cropland classification in plain region, but are not sensitive to the cropland in subtropicals forest ecotope. Supervised classification is more effective than unsupervised classification in cropland classification in Shaanxi Province, and object-based classification and decision tree classification to some extent are able to increase the accuracy especially in desert area in Shaanxi Province. For sake of reducing errors, integrating the advantages of various cropland products and conducting human-computer interactive verification after classification are necessary.

4.4. Other factors Time inconsistency is another factor that can affect the accuracy result. According to Shaanxi Bureau of Statistics and National Bureau of Statistics Investigation Team in Shaanxi, the rate of sown area change of Shaanxi is less than 0.02% during 2009–2011. Therefore, this paper assumed that temporal variation did not has significant influence on cropland classification. Under this assumption, validation samples were selected referring to the Google historical maps of 2009–2011, and datasets of different years were inter-compared (Globcover2009 is of year 2009, whereas other datasets are all of year 2010). Furthermore, it is worth noting that errors caused by manual operation during the validation samples selection cannot be ignored as the precision of visual interpretation is able to directly impact the accuracy assessment results.

5. Conclusion With an increasing number of global land cover datasets released, cropland surface information can be easily derived from the products. This paper selected Shaanxi Province, a typical cultivated province of China, as the study area to analyze the cropland classification results of MODIS, GlobCover2009, FROM-GC and GlobeLand30 datasets from three aspects, i.e., accuracy, spatial agreement and absolute area. The results indicate that the cropland information vary significantly among these four datasets, because the total area of spatial agreement only makes up 33.96% of the whole area of Shaanxi Province. Compared with other datasets, GlobeLand30 is the most accurate dataset for cropland classification. It not only obtains the highest OA and kappa coefficient, but also has relatively accurate cropland area, which is only 15% larger than the records in Shaanxi Bureau of Statistics and National Bureau

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of Statistics Investigation Team in Shaanxi (2011). In terms of the factors that influence the cropland classification, we deem that classification scheme, source data, spatial resolution and classification method in these datasets could bring impacts to the cropland results of these global land cover datasets. These findings are of value in revealing to which extent and on which aspect that these global land cover datasets may agree with each other at small scale. It not only plays a guidance role for data users in selecting appropriate datasets for their specific applications, bus also provides helpful information for data producers to make further improvement.

Acknowledgements This study was jointly supported by the National High-Tech R&D Program of China (2012AA12A408) and the Independent Scientific Research of Tsinghua University, China (20131089277, 553302001). The authors would like to thank three anonymous reviewers for their constructive and helpful comments.

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