African Journal of Agricultural Research Vol. 7(26), pp. 3828-3838, 10 July, 2012 Available online at http://www.academicjournals.org/AJAR DOI: 10.5897/ AJAR11.2455 ISSN 1991-637X © 2012 Academic Journals

Full Length Research Paper

Crop monitoring using a Multiple Cropping Index based on multi-temporal MODIS data

Dailiang Peng1, Cunjun Li2*, Jingfeng Huang3, Bin Zhou4 and Xiaohua Yang5

1Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, China. 2Beijing Research Center for Information Technology in Agriculture, Beijing, 100097, China. 3Institute of Agricultural Remote Sensing and Information Application, Zhejiang University, Hangzhou, 310029, China. 4Institute of Remote Sensing and Earth Sciences, Hangzhou Normal University, Hangzhou, 310036, China. 5Space Weather Center, Meteorological and Hydrographic Department, Beijing, 100094, China

Accepted 22 May, 2012

Food shortage and security attracts global attention and at this moment in time, intensive farming has a large impact on agricultural resources. In this respect, multiple cropping is an effective agricultural practice increasing the combined yield of crops and agricultural output. Over-cropping however, is a major cause of cultivated land degradation. The multiple cropping index (MCI) is an important parameter in arable farming systems. It reflects the utilization of water, soil, incoming radiation, as well as other natural resources. Hence, MCI monitoring is an important activity in the resources and food security assessment of agriculture. Therefore, the objective of this paper is to investigate the MCI monitoring method using multi-temporal moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, for the time period of 2001 to 2004 in the study area of Southeastern China. The annual cycle of crop phenology inferred from remote sensing is characterized by four key transition periods: (1) greenup; (2) maturity; (3) senescence and (4) dormancy. The maximum of the NDVI time-series profile for cropland is a proxy for maximum leaf area. Hence, MCI of arable land in Southeastern China from 2001 to 2004 was monitored by the acquisition of peak frequencies in NDVI time series profiles. The results showed that the MCI increases from north to south for every year, 41.18% areas of Southeastern China had the largest MCI in 2004. The MCI from the MODIS-NDVI elicit a significant correlation with statistical MCI, and most of the relative errors were less 10%. All these results indicated that the method used to estimate MCI described in this paper is dependable.

Key words: Multiple cropping index, moderate resolution imaging spectroradiometer (MODIS), normalized difference vegetation index (NDVI), phenology, Southeastern China.

INTRODUCTION

Half of the world‟s population in the developing countries reforms brought about a massive land development all lives on a low per capita income and experiences over the country, leading to the loss of arable land at an sporadic shortages of nutritious food stuffs. Clearly, alarming rate. However, newly reclaimed low-grade ensuring an adequate availability of food is a major arable land in environmentally fragile frontier regions has challenge. Agriculture activity in China has changed dra- never been able to compensate for the loss of fertile land matically since the 1950‟s. China‟s arable land resources in the southeastern part of China where multiple cropping are extremely scarce in comparison to the world‟s index (MCI) and population density are high (George and average. A dramatic economic expansion since the1978 Samuel, 2003). Brown (1994) even put forward: „Who will feed China?‟ There is an urgent need for China to use its limited arable land resources more efficiently for the sake of not only its own growing population but also the *Corresponding author. E-mail: [email protected] or dlpeng@ globalizing world. ceode.ac.cn. Although chemical fertilizer, agricultural chemicals and Peng et al. 3829

high quality seed can increase food production per unit acceptable temporal resolution is constrained by the low area, improved arable farming systems are a better spatial resolution of this remote sensing imagery. alternative (Ogbuehi and Orzolek, 1987; Philippe and Moderate Resolution Imaging spectroradiometer Paul, 2007). Traditional crops and cropping systems in (MODIS) aboard the NASA‟s earth observing system small-farm agriculture are the result of many years of (EOS) Terra satellite, combines high spatial and temporal evolution and selection by farmers. These systems are resolution (250 m pixel size for MODIS band 1 and 2, and genuinely based on intercropping and multiple cropping almost daily overpasses) (Gonzalo et al., 2007). In (Jaime, 1987). Multiple cropping effectively increases the contrast to its predecessors, MODIS incorporates combined yield of crops (Ogbuehi and Orzolek, 1987), enhanced cloud screening, atmospheric correction, im- and it can be of great help to make better and more proved geo-referencing and a new compositing scheme intensive use of available resources, thus increasing which reduces angular, sun-target-sensor variations agricultural output and income (Beets, 1975; Sundara (Running et al., 1994, 1999; Sinkyu et al., 2003). Hence, and Subramanian, 1987). On the other hand, multiple MODIS vegetation data are more suitable for regional cropping can enhance the level of soil organic matter vegetation research. accretion (Ayanlaja and Sanwo, 1991), and suitable The objective of this paper is to study the method of combinations of crops can improve soil quality (Nair, MCI monitoring using MODIS imagery using the relation- 1973; Wade and Sanchez, 1984). ship between the seasonal variation of the NDVI for MCI is the planting frequency of the crop in the same cropland and different multiple cropping systems. South- arable land in one year (Shen and Liu, 1983), which can eastern China was selected as the study region to test reflect the utilization ratio of water, soil, light energy, and this method of MCI monitoring. other natural resources (Fan and Wu, 2004). Although research of arable farming systems are constantly reported, especially in recent years (Gerowitt, 2003; MATERIALS AND METHODS Helander and Delin, 2004; Gosling and Shepherd, 2005; Study area Yann et al., 2007), MCI studies mainly focused on the 1980‟s (Beets, 1975, 1982; Plucknett, 1981; Ilyas et al., Southeastern China approximate latitudinal and longitudinal ranges 1989), The 1980‟s have been neglected quite some time are N23°~N38°and E114°~E123°, respectively (Figure 1); the total 2 as a result of the influence of mono-cropping oriented land area is about 605 800 km including 58 cities. In this paper, Southeastern China was selected as the study region for the research in the western world (Fan and Wu, 2004). following reasons: Hence, monitoring MCI in arable farming systems is quite meaningful for agricultural resources assessment and the (1) It has a favorable natural environment suitable for intensive crop world‟s food security. It is a requirement for policy makers cultivation, and is the main food provider in China. China feeds 22% to plan and evaluate managerial decisions in a rational, of its population with less than 10% of the arable land in the world. objective way in this respect. MCI is traditionally Moreover, Southeastern China feeds about 25% of its population with 17% of the arable land of China. Hence, food production in this calculated by the ratio between the total harvest area and region is very important for China and world. the arable land area in one year with statistical data of (2) Due to the government policy to protect arable land in China, local governments (Shen and Liu, 1983; Wang and Li, the total area of arable land in Southeastern China has not 2003; George and Samuel, 2003). This method is hardly decreased. The per-capita area has decreased slightly between used in agro-climatic and crop growth models at all and 2001 and 2004 because of the increasing population. On the other hence, is not simple for policy makers to find data on the hand, the per-capita arable land area in Southeastern China is lower than at the national level (about 0.1 hectares for the years temporal and spatial variations. Therefore, an urgent mentioned) (Table 1). Hence, the problem of food security is widely need is the development of a new method using new concerned in this area. data to extract MCI independent of statistical data (Fan (3) The variability of topography in Southeastern China is specific and Wu, 2004). and therefore typical. There are many hills and mountains in the Remote sensing techniques have been developed from southern part of the study area, which is a typical broken and mountainous terrain, and the dominant terrain is flat in the northern 1960‟s onward and rapidly developed over the past 50 part of the study area, and most of the arable land is planted with years. It is applied nowadays in investigating, appraising, one crop type. programming and managing agricultural resources, to perform forecasting, monitoring and the evaluation of calamities, to perform dynamic agro-monitoring, and to Experimental data estimate agricultural yields (Wang and Malingreau, 1990; MOD13Q1 16-day composite NDVI imagery acquired from 2001 to Wang and Hang, 2002). Nevertheless, research of MCI 2004 has been collected by the Warehouse Inventory Search Tool using remote sensing techniques is very scarce (WIST). The spatial resolution is 250 m. MODIS NDVI improves (Panigrahy et al., 2005). Fan (2003) and Gu (2000) with the extraction of canopy biophysical parameters, a new extracted MCI in China using SPOT/VGT (1 km spatial compositing scheme that reduces angular, sun-target-sensor variations with an option to use BRDF models is utilized, and the resolution) multi-temporal normalized difference gridded vegetation indices include quality assurance (QA) vegetation index (NDVI) data. But monitoring MCI with information indicating MODIS product quality. MOD12Q1 land cover 3830 Afr. J. Agric. Res.

Figure 1. The location of the study area.

data obtained by WIST corresponding with MODIS NDVI with a namely: spatial resolution of 1 × 1 km has been used to extract cropland for MCI monitoring. MOD13Q1 and MOD12Q1 product were re- (1) Greening: The date of photosynthetic activity onset; projected to geographic coordinates (longitude/latitude) projection. (2) Maturation: The date after the phase (1). The plant green leaf The administrative map at the scale of 1/250,000 in the study area area reaches its maximum; has been used to create a subset of imagery at the city level. Crop (3) Senescence: The date at which photosynthetic activity and growth period and agricultural statistic data were used to analyze green leaf area rapidly decrease; characteristics of crop and validate spatial and temporal variation of (4) Dormancy: The date at which physiological activity becomes the MCI estimation in study area. reaches close to zero levels.

These physiological processes can be found when making use of Methodology the multi-temporal profile of the NDVI, and different multiple cropping systems have different types of multi-temporal profiles Annual cycles of crop phenology inferred from remote sensing are (Peng et al., 2007). To study the relationship between seasonal characterized by four key transition dates (Zhang et al., 2003), variation of NDVI and different multiple cropping systems, Peng et al. 3831

Table 1. The arable land in Southeastern China from 2001 to 2004.

2001 2002 2003 2004 Percent (%) Per Percent (%) Per Percent Per Percent (%) Per Province Cultivated Cultivated Cultivated Cultivated of the capital of the capital (%) of the capital of the capital land (ha) land (ha) land (ha) land (ha) nation ha nation ha nation ha nation ha Shandong 7,689,300 5.91 0.00852 7,689300 5.91 0.00848 7,689300 5.91 0.00844 7,689300 5.91 0.00839 Jiangsu 5,061,700 3.89 0.0713 5,061,700 3.89 0.0710 5,061,700 3.89 0.0707 5,061,700 3.89 0.0702 Anhui 5,971,700 4.59 0.0944 5,971,700 4.59 0.0942 5,971,700 4.59 0.0936 5,971,700 4.59 0.0924 Shanghais 315,100 0.24 0.0237 315,100 0.24 0.0236 315,100 0.24 0.0235 315,100 0.24 0.0233 Zhejiang 2,125,300 1.63 0.0470 2,125,300 1.63 0.0469 2,125,300 1.63 0.0467 2,125,300 1.63 0.0464 Fujian 1,434,700 1.10 0.0432 1,434,700 1.10 0.0431 1,434,700 1.10 0.0428 1,434,700 1.10 0.0426

Table 2. Growth processes for different cropping systems.

Site Cropping type Crop types Seeding Transplanting Green heading Mature Wendeng county One crop per annual Wheat 2001-10-03 / 2002-02-22 2002-05-08 2002-06-10

Wheat 2001-10-31 / 2002-01-26 2002-03-30 2002-05-22 Feidong county Double cropping Rice 2001-05-14 2002-06-12 2002-06-18 2002-09-24 2002-09-24

”/” means wheat does not have this growth stage.

the Wendeng and Feidong counties in the People‟s The cropland areas were extracted by MOD12Q1 data, indicating a triple cropping for that year. The three peaks Republic of China were selected. According to local and then the corresponding area MODIS NDVI from 2001 appear in March, June and September, respectively. statistical data, the main vegetation types in these two to 2004 in Southeastern China were obtained using the Winter wheat, early and late rice were planted in fields counties are crops. Hence, the seasonal variation of the administrative map. According to the MOD13Q1 QA covered by this pixel. The NDVI seasonal profile in Figure NDVI in these two counties is attributed to crop growth. In information, NDVI with the good quality were selected, and 4d elicits small fluctuations since the NDVI is very low due Wendeng, the main crop is wheat (Table 2), and the the low quality or contaminated NDVI were replaced by the to large fractions of bare soil or fallow grounds for this multiple-cropping system aimed at one crop per year. average values of 32-day NDVI before and after this low pixel. Figure 2 indicates that one cycle for the NDVI seasonal quality or contaminated day. The results are subsequently It is inferred that the NDVI seasonal profile peaks are variation occurs. stacked by time sequence. Four typical NDVI seasonal equal to the value of the Multi Cropping Index (MCI) (Fan, The NDVI reaches its maximum during the heading variation profiles were selected (Figure 4) from these 2003; Peng et al., 2007). The equation of MCI in a region period. In Feidong, paddy rice and wheat are the main stacks. Figure 4a shows a single-peak profile indicating a can be expressed as follows. crops, and the multiple-cropping system is based on mono- crop system for that specific year for the pixel double cropping. Figure 3 shows the two cycles for the observed. The maximum appeared at the middle ten days NDVI seasonal variation. The NDVI maxims appear during of May typical for spring wheat. Figure 4b gives a double- sumpeaki the wheat and paddy rice heading periods, respectively. peak profile indicating a double cropping system for that MCI(%) Fi  100   100 sumpixel During recent years, counties with triple cropping as the specific year for this specific pixel. The two peaks appear i (1) main multiple-cropping system are difficult to find, but at the last ten-day period of April and August, respectively, some pixels indicate that triple-cropping occurring in due to the winter wheat and single rice which were planted Where Fi is the peak frequency of NDVI seasonal variation certain years was found as shown in Figure 4c. in that year. Figure 4c shows a three-peaked profile profile; sumpeaki is the sum of peaks for all NDVI seasonal 3832 Afr. J. Agric. Res.

Figure 2. Seasonal variation of MODIS-NDVI for one crop per year in Wendeng county (the number in the map means the day of year).

variation profiles; sumpixeli is pixels where NDVI time series profiles follows: 23 NDVI values in one year (the temporal resolution of have characteristics like the ones in the Figure 4a to c. The MODIS NDVI in this paper is 16 days) are put in an array algorithm to calculate the NDVI peaks has been coded according to chronologically when an arable land pixel is selected, subtracting a Peng et al. (2007). value later in time from a value previous to the one earlier in time. Furthermore, the number of peaks is obtained by the double Doing so, 22 NDVI values are obtained. For these 22 NDVI, the subtraction algorithm (Figure 5). An example of this algorithm is as negative values are assigned the value of –1, and the positive Peng et al. 3833

Figure 3. Seasonal variation of MODIS-NDVI for double cropping per year in Feidong county (the number in the map means the day of year).

values a value of 1. Subsequently, these values (-1 or 1) are cycle. In this paper, the peaks for every pixel are integer values, but chronologically put into an array. Another subtraction operation is when the MCI was estimated at city level, the multiple cropping performed next to create a second array with 21 data (-2, 0, and 2). system may be different for each pixel in this city. Hence, the result The -2 numbers are considered as the peaks for this pixel data of MCI calculated by Equation (1) is in that case a floating-point sequence. The value of “sumpeak” and “sumpixel” for all pixels in value. Meanwhile, MCI equal to 0 means that all the cropland in the one city were thus obtained. The MCI for the city mentioned are city is a fallow land. In Southeastern China, this configuration does calculated according to Equation (1). not occur. If the MCI is between the values of 100 and 150, this In Southeastern China, there are four possible values which Fi means that some of the arable land is a double cropping area for can take for a pixel, that is 0, 1, 2, and 3, and four corresponding that specific year. Most of the arable land in the city is a single possible values for MCI, 0, 100, 200, and 300 by equation (1), cropping on a yearly basis. If the MCI is larger than 150 and less which indicates whether arable land is fallow ground, one cropping, than 200, this indicates that some of the arable land has one crop double cropping or triple cropping land cover one yearly growth per year, while most of the arable land in the city is double 3834 Afr. J. Agric. Res.

Figure 4. NDVI time series profiles of arable land crop at the site of 37.18°N, 122.03°E in Wendeng county (a); 31.90°N, 117.67°E in Feidong county(b); 24.93°N, 118.49°E in Quanzhou city(c); 32.89°N, 115.80°E in Fuyang county (d), respectively.

cropping, and so on. data. Another MCI dataset was calculated with Equation Traditionally, MCI is calculated using the area of the total harvest (2). The comparisons between MCI from MODIS-NDVI and the arable land. It can be expressed as (Wang and Li, 2003): and that from statistical data from 58 cities in South-

the total harvest area in one year eastern China were analyzed in Figure 6. MCI correlation MCI(%)= 100 between MODIS-NDVI and statistical data is significant at the arable land area (2) 99% probability level over four years. There were 74.14, 72.41, 62.07 and 86.21% surface areas in Southeastern If the total harvest area is two times that of arable land area in one China where the relative errors are less than 10% in year, which means double cropping systems, and two cycle (or two peaks) for NDVI seasonal variation profiles, the result of this 2001, 2002, 2003, and 2004, respectively, which method can be used to validate MCI estimation using Equation (1). indicates that the spatial distribution of MCI is similar with the result of statistical data. Hence, the method used to estimate MCI described in this paper is plausible. RESULTS

MCI Validation MCI spatial distribution

The total harvested and arable land areas for each city in MCI for 58 cities in Southeastern China was calculated Southeastern China were determined using statistical with the algorithm in Equation 1. Subsequently, the Peng et al. 3835

Figure 5. The flow charts for the double subtraction algorithm, where N, T and M mean NDVI, the result of the first and second subtracting calculation, respectively.

spatial distribution of MCI was mapped with GIS techni- DISCUSSION ques (Figure 7). It was found that most of the MCI values were between 100 and 200. This means that the main Generally, the arable land can be planted with different multiple-cropping system in Southeastern China is single crops when water-heat resources exceed the main crop or double cropping. The spatial distribution of MCI shows requirement (Fan, 2003). In Southeastern China, due to that the MCI in Southeastern China is increasing from the abundant water and heat resource, some bare lands north to south for the periods of 2001 to 2004, and most always have many kinds of wild plants, which become the of the inland MCI values are larger than that in those at potential disturbance for crop identification. On the other the coastal region (Figure 7). hand, the spatial resolution of MODIS NDVI is 250 m. In the south part of the Southeastern China, some arable land plots are less than one pixel, which is another MCI inter-annual variation important error for crop detection, and may decrease the accuracy of MCI estimation. Actually, the above distur- In the first three years, the inter-annual variation of MCI bances can be neglected because the MCI is calculated was small; in 2004, there were 41.18% areas in at the city level in this paper, and the discrepancy of Southeastern China where MCI was larger than those in agricultural resources and MCI in this unit is very small. If any other three years. On the other hand, there are some arable land pixels in this city could not be identified 72.06% areas in Southeastern China where the MCI in because of wild plants or mixed pixels, these pixels will 2004 was larger than that in 2003, which indicates that be deleted as the invalid pixels in the algorithm. Only MCI increased in 2004 in most of the study areas. In identifiable arable land and corresponding pixels whose 2004, the Chinese government proclaimed to increase NDVI seasonal variation corresponded to one of the four the price of agricultural products and waive agricultural typical profiles in Figure 4 were selected to calculate MCI taxation, which improved farmers' enthusiasm for for this city. agricultural production to some extent, and then MCI in According to the light, water, air, soil and other agri- nationwide scope was more obviously increased than cultural resources, arable land has its maximum potential over the past several years. productivity; the larger MCI values for arable land means 3836 Afr. J. Agric. Res.

Figure 6. Comparison between MCI from MODIS-NDVI and that from statistical data.

that famers plant more times and more agricultural Conclusions resources are utilized. Undoubtedly, multiple cropping induces arable land degradation, especially for agri- Statistical MCI were used to validate estimating MCI, and cultural soils, water, air and habitat resources (Devendra it indicated that the algorithm to calculate the MCI for and Thomas, 2002; Bailey et al., 2003). Government MODIS NDVI seasonal variation profile was dependable. policy makers can make some rational and objective In addition, the methods in this study can be applied in decisions according to the local MCI and its spatial distri- other regions in the world such as the high economically- bution; for example, decreasing the price of agricultural developed areas with limited resources, to generate an products when MCI is too large to protect the updated continuous MCI database that can be used for environment and arable land sustainable utilization. On research on the issue of food security, agricultural other hand, improving agriculture production conditions production and environment management. and increasing agricultural product prices to encourage farmers to stay active in agriculture to be able to face food shortages. Hence, the monitoring of MCI is ACKNOWLEDGEMENTS important for agriculture management, food security and environmental protection. This research was supported by the National Natural Peng et al. 3837

Figure 7. Spatial distribution for MCI in Southeastern China from 2001 to 2004.

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