Spatial evaluation of crop maps by spatial production allocation model in China

Jieyang Tan, Zhengguo Li, Peng Yang, Qiangyi Yu, Li Zhang, Wenbin Wu, , Pengqin Tang ( ) Key Laboratory of Agri-informatics, Ministry of Agriculture / Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences No.12 Zhong Guan Cun South Street, Haidian, Beijing, China Tel: 13522529177 Fax: 010-82105635 E-mail: jieyangtan@163.com E-mail: tangpengqin@caas.cn

Zhenhuan Liu Geography and Planning School of Sun Yat-sen University Guangzhou, China Liangzhi You Environment and Production Technology Division International Food Policy Research Institute Washington, DC, USA

Abstract—Spatial Production Allocation Model (SPAM),

developed by International Food Policy Research Institute (IFPRI), is one of broadest spatial models that applied a cross-entropy method to downscale the area and yield for each crop with a resolution of 5 arc minute globally for the year 2000 and 2005. To evaluate the accuracy of three staple crops (rice, wheat and maize) in China allocated by SPAM, we compared these crop maps with remote sensed cropland derived from national land cover datasets. This is done through a comparison scheme that accounts for spatial difference at the pixel level. Four types (no-existing, mis-allocated, over-estimated and reasonable) were formulated in this scheme that was used to evaluate the per-pixel area accuracy of each of the three crops on national and provincial scales. Overall, the map of maize has the highest area accuracy with a 64% percentage of reasonable pixels that covers 96% of the total maize area, in contrast, 57% (90%) and 44% (81%) for the wheat and rice map respectively. Further, crop area consistency in rain-fed cropland is better than that in irrigated cropland. Through the evaluations, we can provide decision makers with information on the SPAM products exist as well as the strengths and weaknesses. Meanwhile, some recommendations can be concluded on priorities for further work on the improvement of the reliability, utility and periodic repeatability of crop distribution products.

Key words— Crop distribution; Crop statistics; Spatial validation; China

I. INTRODUCTION Cropland accounts for nearly 15 million km2 of the Earth’s

land cover, amounting to 12% of the Earth’s ice-free land surface, yet information on the distribution and performance of specific crop is often available only through national or sub-national statistics [1]. Crop distribution data is generated by the down-scaling of crop statistics and increasingly applied in climate change [2-3], food security [3-4], livestock production systems [5-6], ecosystem service valuation [7], irrigation and rural road infrastructure [8-9], fertilizer input use [10].

Although remote sensing products have been widely used

in regions with single crop type, over a large scale (i.e. national, continental, or global scale), crop information at pixel level is still difficult to be obtained simply on the basis of remote-sensing technology due to the impacts of mixed types in pixel, atmospheric disturbances, and scale effects [11-13]. Recently, there have been multiple independent efforts to incorporate the detailed information available from statistical surveys with supplemental spatially distributed information to produce a spatially explicit global dataset specific to individual crop varieties for the year 2000. For example, Ramankutty et al. (2008) and Monfreda et al. (2008) completed spatial distribution of 11 main crops globally in 2000 [14-15]. Based on this, Portmann et al. (2010) integrated irrigation information to produce spatial distribution of 26 main crops (including both irrigated and rain-fed) [16]; You and Wood (2006) developed the spatial production allocation model (SPAM), which is based on the theory of minimum cross-entropy and has been widely applied on crop spatio-temporal distribution in Latin America and the Caribbean [17] and in sub-Saharan Africa [18].

Although both M3 cropland datasets [15] and MIRCA datasets [16] provide crop distribution data in China, the use of provincial statistical information only in 2000 makes these two datasets unable to be used for crop dynamic analysis. To mitigate the insufficiencies of previous studies, the SPAM-China model has been developed by integrating agricultural statistical data at the county level in China [1, 19]. However, there has been lack of comprehensive and region-specific evaluation analysis about crop distribution in China. The precision information is necessary for evaluating scientifically the impact of global changes on agricultural production, food security, and biogeochemical cycles in China.

This study aims to explore and quantify the accuracy of spatial distribution of three staple crop (rice, maize and wheat) in China generated from SPAM in 2005, by a pixel-to-pixel comparison with cropland from national land cover datasets over the same period. The analysis explores not only the precision difference among the three cropping systems but also the regional and management differences, which will provide users with information vital for selecting crop maps appropriate for each intended application.

This work is supported by the National Natural Science Foundation of China (No. 41171328, 41201184)

II. MATERIALS AND METHODS

A. Crop maps by SPAM Crop maps in 2005 were generated with a resolution of 5

arc minute by the spatial production allocation model (SPAM). These datasets contain 42 crops (cereal crops, oil crops and other crops) with three different production systems globally. Four variables have been generated in each 5 arc minute grid cell, i.e. physical area (share of cropland area), harvested area, unit yield and production. In the present study, the physical area of rice (SPAMrc), maize (SPAMmz), including irrigated and rain-fed maize (SPAMir-mz and SPAMrf-mz), wheat (SPAMwt), including irrigated and rain-fed wheat (SPAMir-wt and SPAMrf-wt) were extracted for spatial evaluation.

B. Land cover products A national land use/cover product of China (NLUD) in

2005 had been applied as reference datasets to evaluate the results of SPAM in this study. NLUD, supported by the Chinese Academy of Sciences (CAS), which are published online at Data Sharing Infrastructure of Earth System Science (http://www.geodata.cn). NLUD in 2005 include 6 land cover types (forest, grassland, cropland, city, wetland and water, desert) and 25 subclasses with a resolution of 10'' (approximately 300 m at the equator). This dataset were produced by using remote sensing images with a spatial resolution of 30 m, visual interpretation, field survey and other auxiliary information [20]. In this study, paddy land (NLUDpd), dry land (NLUDdd), including irrigated and rain-fed dry land (NLUDir-dd and NLUDrf-dd) were extracted to obtain an assessment of the per-pixel proportion of each of the SPAM-allocated crops within the cropland extent.

C. Data preparation Due to the large degree of variation in data from sources

with different spatial and temporal resolutions, it was necessary to reprocess and standardize the data. For this, all crop maps were converted into the geographic coordination system with a cell size of 5' in a standard GIS software environment (ESRI, ArcGIS 10.1). To capture as much of the regional range of variation in land cover as possible, the land

cover products with a spatial resolution of 10'' were aggregated in a 5' × 5' grid, while the summed acreage information within each grid cell was preserved. In this manner, the areal coverage of the land cover types was preserved in the up-scaling process, which was not the case when a discrete classification was aggregated using the nearest neighbor or majority reclass approaches.

D. Spatial evaluation for crop maps Since the true crop distribution is still unavailable in China

now, we could not validate the crop maps directly. Instead, a pixel-to-pixel comparison between SPAM maps and NLUD have been implemented here. To provide some sense of discrepancy among the two datasets, we developed an evaluation scheme based on a comparison between the acreage of the SPAM-allocated crops and the acreage of specific cropland (Table I). The assumption about this evaluation scheme is that rice can only be planted in paddy land, while wheat or maize just grows in dry land. Five pixel types were defined as the results of the comparison. Firstly, pixels without any cropland or crop (both SPAM and NLUD are zero or null) were identified as empty. These pixels represent areas in which no cultivation or plantation exists. Secondly, pixels with cropland distribution, while no allocated crop (NLUD >0 and SPAM = 0) were defined as no-existing. These areas represent no specific crop were planted in the cropland, due to crop alternation or cropland abandonment. Thirdly, pixels with some allocated crop, while no corresponding cropland (NLUD = 0 and SPAM > 0), were defined as mis-allocated, which represent unsuitable crop allocation by SPAM. Fourthly, pixels with some allocated crop and cropland (NLUD > 0 and SPAM > 0, but SPAM > NLUD), were defined as over-estimated, since the acreage of crop are estimated too high in such areas. Finally, pixels with appropriate acreage of crop and cropland (NLUD > 0, SPAM > 0, and SPAM < NLUD), were defined as reasonable in crop maps. Specifically, spatial evaluation for different production systems (irrigated and rain-fed) were also implemented for wheat and maize (Table II).

TABLE I. EVALUATION SCHEME FOR THE MAPS OF THREE STAPLE CROPS

SPAM NLUDpd NLUDdd

NLUDpd = 0 NLUDpd > 0 NLUDdd = 0 NLUDdd > 0

SPAMrc = 0

SPAMrc ≤ NLUDpd

Empty

Empty

No-existing

Reasonable

-a

-

-

-

SPAMrc > NLUDpd Mis-allocated Over-estimated - -

SPAMwt = 0 - - Empty No-existing

SPAMwt ≤ NLUDdd - - Empty Reasonable

SPAMwt > NLUDdd - - Mis-allocated Over-estimated

SPAMmz = 0 - - Empty No-existing

SPAMmz ≤ NLUDdd - - Empty Reasonable

SPAMmz > NLUDdd - - Mis-allocated Over-estimateda. Slash in the table means the corresponding pairs are not existed.

TABLE II. EVALUATION SCHEME FOR IRRIGATED AND RAIN-FED CROPS

SPAM NLUDdd-ir NLUDdd-rf

NLUDdd-ir = 0 NLUDdd-ir > 0 NLUDdd-rf = 0 NLUDdd-rf > 0

SPAMwt-ir = 0

SPAMwt-ir ≤ NLUDdd-ir

Empty

Empty

No-existing

Reasonable

-a

-

-

-

SPAMwt-ir > NLUDdd-ir Mis-allocated Over-estimated - -

SPAMmz-ir = 0 Empty No-existing - -

SPAMmz-ir ≤ NLUDdd-ir Empty Reasonable - -

SPAMmz-ir > NLUDdd-ir Mis-allocated Over-estimated - -

SPAMwt-rf = 0 - - Empty No-existing

SPAMwt-rf ≤ NLUDwt-rf - - Empty Reasonable

SPAMwt-rf > NLUDwt-rf - - Mis-allocated Over-estimated

SPAMmz-rf = 0 - - Empty No-existing

SPAMmz-rf ≤ NLUDmz-rf - - Empty Reasonable

SPAMmz-rf > NLUDmz-rf - - Mis-allocated Over-estimated

a. Slash in the table means the corresponding pairs are not existed.

III. RESULT

A. Spatial evaluation of crop maps on national scale

Fig. 1. Comparison between the acreage of the SPAM-allocated crops and the acreage of specific cropland. (a) SPAM rice vs. paddy land; (b) SPAM wheat vs. dry land; (c) SPAM maize vs. dry land.

1) Rice Spatial difference of three crop maps (rice, wheat and

maize) at national scale were investigated by a pixel-to-pixel comparison with aforementioned five types of pixel. No-existing pixels are illustrated in light green as shown in Fig.1a. It can be indicated that these pixels are mainly located in central and southwestern part of China, and also can be found sporadically in southeast China. In general, no-existing pixels account for 27% of the total pixels (omit the empty pixels) in China, which occupied more than 17% of the total paddy area in China correspondingly.

Mis-allocated are illustrated in mars red as shown in Fig.1a. It can be indicated that these pixels are mainly located in northern and northeastern part of China, and also can be found sporadically in Xinjiang province. In general, mis-allocated pixels account for 17.89% of the total pixels in China, which occupied nearly 6% of the total SPAM rice area in China correspondingly.

Over-estimated are illustrated in orange as shown in Fig.1a. It can be indicated that these pixels are mainly located in southern and northeastern China sporadically. In general, over-estimated pixels account for 10.75% of the total pixels in China, which occupied nearly 13.51% of the total SPAM rice area in China correspondingly.

Reasonable are illustrated in leaf green as shown in Fig.1a. It can be indicated that these pixels are mainly located in southeastern China and part of northeastern China. In general, reasonable pixels account for nearly 44% of the total pixel in China, which occupied nearly 81% of the total SPAM rice area in China correspondingly.

2) Wheat No-existing pixels are illustrated in light green as shown in

Fig.1b. It can be indicated that these pixels are mainly located in southern and northeastern China, and also can be found sporadically in southwestern China. In general, no-existing pixels account for 39.08% of the total pixels in China, which occupied 20.74% of the dry land area in China correspondingly.

Mis-allocated are illustrated in mars red as shown in Fig.1b. It can be indicated that these pixels are mainly located in south of Huang-Huai plain and part of middle-lower Yangtze Plain. In general, mis-allocated pixels account for 2.28% of the total pixels in China, which occupied 3.88% of the total SPAM wheat area in China correspondingly.

Over-estimated are illustrated in orange as shown in Fig.1b. It can be indicated that these pixels are also mainly located in middle-lower Yangtze Plain sporadically. In general, over-estimated pixels account for 1.98% of the total pixels in China, which occupied 6.14% of the total SPAM wheat area in China correspondingly.

Reasonable are illustrated in leaf green as shown in Fig.1b. It can be indicated that these pixels are mainly located in North China Plain and Northeast Plain, also can be found in most part of southwestern China and part of Xinjiang province. In general, reasonable pixels account for 56.66% of the total pixels in China, which occupied nearly 90% of the total SPAM wheat area in China correspondingly.

3) Maize No-existing pixels are illustrated in light green as shown in

Fig.1c. It can be indicated that these pixels are mainly located in some hilly regions and plateaus (e.g. hilly region in southern China). In general, no-existing pixels account for 30.81% of the total pixels in China, which occupied 13.87% of the total dry land area in China correspondingly.

Mis-allocated are illustrated in mars red as shown in Fig.1c. It can be indicated that these pixels are mainly located in part of southern China (e.g. middle-lower Yangtze Plain and some hilly regions in southern China). In general, mis-allocated pixels account for 2.01% of the total pixels in China, which occupied 1.12% of the total SPAM maize area in China correspondingly.

Over-estimated are illustrated in orange as shown in Fig.1c. It can be indicated that these pixels are also mainly located in part of southern China sporadically (e.g. middle-lower Yangtze Plain and some hilly regions in southern China). In general, over-estimated pixels account for 3.14% of the total pixels in China, which occupied about 2.88% of the total SPAM maize area in China correspondingly.

Reasonable are illustrated in leaf green as shown in Fig.1c. It can be indicated that these pixels are mainly located in most parts of North China Plain and Northeast Plain, also can be found in parts of southwestern China and Xinjiang province sporadically. In general, reasonable pixels account for 64.04% of the total pixels in China, which occupied 96% of the total SPAM maize area in China correspondingly.

B. Spatial evaluation of irrigated and rain-fed crop maps on national scale

1) Wheat In order to investigate the spatial accuracy of crops maps

under different production systems (irrigated and rain-fed), mis-allocated pixels have been analyzed on national scale. For irrigated wheat (Fig.2a), it can be indicated that these pixels are mainly located in middle-lower Yangtze Plain, also can be found in central and western Chinese Loess Plateau sporadically. In general, mis-allocated pixels account for around 3.5% of the total pixels China, which occupied nearly 14.2% of the total SPAM irrigated wheat area in China correspondingly.

However, for rain-fed wheat (Fig.2b), mis-allocated pixels can be indicated that these pixels are mainly located in west of

the Huang-Huai plain. In general, mis-allocated pixels account for around 2.3% of the total pixels in China, which occupied nearly 17% of the total SPAM irrigated wheat area in China correspondingly.

2) Maize Meanwhile, for irrigated maize in China (Fig.2c), it can be

it can be indicated that these pixels are also located in middle-lower Yangtze Plain, and can be found in northeastern China. In general, mis-allocated pixels account for around 44% of the total pixels in China, which occupied nearly 15% of the total SPAM irrigated maize area in China correspondingly.

For rain-fed maize (Fig.2d), it can be indicated that these pixels are mainly located in North China Plain, also can be found in southwestern China. In general, mis-allocated pixels account for around 2% of the total pixels in China, which occupied nearly 7.7% of the total SPAM irrigated maize area in China correspondingly.

Fig. 2. Comparison between the acreage of the SPAM-allocated crops and the acreage of specific cropland under different production systems (irrigated and rain-fed). (a) and (b) represent SPAM irrigated and rain-fed wheat vs. irrigated and rain-fed dry land; (c) and (d) represent SPAM irrigated and rain-fed maize vs. irrigated and rain-fed dry land.

IV. DISCUSSIONS Since the true crop distribution is still unavailable in China

now, we could not validate the crop maps directly, so our current work has proposed a spatial approach within a cross-comparing framework to evaluate the area of crop maps on a pixel basis by utilizing satellite-derived cropland cover. Overall, it can be inferred from that the maps of wheat and maize area have the higher accuracy in contrast with the map of rice. Some distinct difference during the comparison between SPAM results and NLUD may largely be ascribed to the methodologies of cross-comparing framework and the input data of SPAM.

A. Methodologies of cross-comparing framework Firstly, difference may be arisen from the assumption that a

specific crop can only be planted in a unique corresponding croplands. For instance, the assumption about this evaluation scheme is that rice can only be planted in paddy land, but actually is not. Generally, rice is planted in paddy land in south

of China, especially in plain adjacent to rivers or lakes. It also can be inferred from Fig.1a, there is only a small number of mis-allocated pixels in southeastern China. However, there exists rice in parts of northern and southwestern China, i.e. the North China Plain, Northeast China Plain, and Yun-gui plateau. According to local investigation and experts interviews, rice, so-called upland rice, can also be planted in mountainous regions and some irrigated dry land. These are the reasons why large number of mis-allocated pixel exist in northern China and southwestern China.

Secondly, crop alternation or cropland abandonment may lead to large number of no-existing pixels for three staple crops. For example, Paddy land not only can be planted rice, but other aquatic plants also can be planted in paddy land such as lotus root or water hyacinth in southern China. On the other hand, cropland abandonment accompanying economic development has been observed in many regions of China, especially occurred in mountainous areas of southwestern China [21], which makes lots of no-existing pixels and abandoned cropland areas in those regions.

B. Input data of SPAM. Cropland extent definition may make some differences

between the SPAM results and NLUD. Cropland data layer, a critical input data for SPAM, which is derived from Global land cover in 2000 (GLC2000) datasets with 1 km spatial resolution and is used to disaggregate crops census from administrative units to grid cells. However, our reference cropland datasets, which include 3 subclasses (paddy land, irrigated and rain-fed dry land), are derived from NLUD in 2005 with approximately 300 m spatial resolution. Thus, we firstly can infer from that the spatial resolution of NLUD is finer than the cropland data from GLC2000. This discrepancy among the two datasets may cause some differences in allocation of crops. Meanwhile, NLUD has the exactly time in 2005, which the temporal scale is suitable than GLC2000 dataset. Secondly, cropland derived from GLC2000 fail to provide more detailed about the subclasses of cropland (e.g. paddy land and dry land), while cropland derived from NLUD has subclasses of it. For instance, GLC2000 only has cropland entirely, which have poor accuracy about paddy land. This may has negative impact on the allocation of SPAM rice. However, NLUD can provide finer resolution of croplands, including paddy land, irrigated and rain-fed dry land. Furthermore, we can use NLUD instead of GLC2000 as an input data to improve the accuracy of SPAM in the future. Thirdly, inconsistency between global map of irrigation area and NLUDir-dd may has negative influence on the evaluation of irrigated and rain-fed production systems. Global map of irrigation area, published by S Siebert in 2005, is used as an input data for SPAM. However, irrigated statistic data source of this global irrigation map in China was mainly come from Chinese Agricultural Yearbook in 1995, but NLUDir-dd was derived from remote sensing images around 2005. Thus, the inconsistency in the temporal scale between these two datasets may also bring uncertainty to the evaluation.

V. CONCLUSIONS SPAM has already been developed to disaggregate national

and sub-national statistics to global, spatially distributed crop

maps. These maps can describe crop areas of three staple crops in China, further disaggregated by irrigated and rain-fed production systems for maize and wheat. To provide decision makers with information on the area accuracies of SPAM, we have proposed a spatial approach within a cross-comparing framework to evaluate the area of crop maps on a pixel basis by utilizing satellite-derived cropland cover. Overall, the map of maize has the higher area accuracy in contrast with the maps of wheat and rice. Further comparisons showed either maize or wheat maps in rain-fed systems have better accuracies than those in irrigated systems.

Our results show considerable spatial difference between crop maps and cropland distribution across irrigated and rain-fed systems. Reducing such difference is critical for land cover change research to link local information to regional trends. Our current approach indicates that remote sensing derived cropland would be valuable datasets for reducing the spatial difference of agricultural production, infrastructure and natural resources. We also realize that the changing of cropping patterns over time is as important as the cropping patterns over space. We do not compare crop maps over time but we are preparing related data for evaluating crop maps for 2010 right now. Therefore, we have committed ourselves to estimate similar crops maps continuously in the future. In long term, satellite technology, combined with ground-based data collection, may allow direct estimation of crop areas and yields. Such technology advance would greatly improve the quality, reliability and timeliness of spatial crop data as our current effort is trying to achieve.

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