Mapping of Cropping System for the Indo- Gangetic ...

Mapping of Cropping System for the IndoGangetic Plain Using Multi-Date SPOT NDVI-VGT Data

Journal of the Indian Society of Remote Sensing ISSN 0255-660X Volume 38 Number 4 J Indian Soc Remote Sens (2011) 38:627-632 DOI 10.1007/ s12524-011-0059-5

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Author's personal copy J Indian Soc Remote Sens (December 2010) 38(4):627–632 DOI 10.1007/s12524-011-0059-5

RESEARCH ARTICLE

Mapping of Cropping System for the Indo-Gangetic Plain Using Multi-Date SPOT NDVI-VGT Data Sushma Panigrahy & Gargi Upadhyay & Shibendu Shankar Ray & Jai S. Parihar

Received: 28 April 2009 / Accepted: 4 May 2010 / Published online: 12 February 2011 # Indian Society of Remote Sensing 2011

Abstract The present study has been carried out to delineate the existing cropping systems in the IndoGangetic Plains (IGP) using 10 day composite SPOT VEGETATION (VGT) NDVI data acquired over a crop year (June–May). Results showed that it is feasible to identify the major crops like rice, wheat, sugarcane, potato, and cotton in the dominant growing areas with good accuracy. Double cropping pattern is the most prevalent. Rice-wheat, sugarcane based, cotton-wheat, rice-potato, rice-rice, maize/ millet-wheat are some of the major rotations followed. Rice-wheat is the dominant rotation accounting for around 40% of the net sown area. Triple crop rotations was less than 5% of the area and observed in some parts of Uttar Pradesh, Bihar and West Bengal. Single crop rotation of rice-fallow is significant only in West Bengal. Keywords Crop rotation . Remote sensing Indo-gangetic plain . Multi-date . SPOT VGT . NDVI

Introduction Indo-gangetic plain (IGP) in India is the major foodgrain-growing region, which produces about S. Panigrahy : G. Upadhyay : S. S. Ray (*) : J. S. Parihar EPSA, Space Applications Center, ISRO, Ahmedabad 380015, India e-mail: [email protected]

50% of the total foodgrains to feed 40% of the population of the country (Pal et al. 2009). It is comprised of the states of Punjab, Haryana, Uttar Pradesh (UP), Bihar and West Bengal (WB), except Purulia district, and two districts of Rajasthan. It has the highest cropping intensity and most of the agricultural land is irrigated. Today, the Indogangetic states are facing with some of the most serious agroecological problems like declining productivity and falling ground water table, increasing soil salinity (Abrol et al. 2000) and agricultural pest problems due to excessive and improper use of resources. The increased awareness of environmental issue and the need to strive for sustainable management of natural resources has focused attention on the need to map and monitor changes in the cropping systems more frequently and on a regular basis. Remote sensing provides tools for advanced cropping system management (Panigrahy et al. 2002). The use of remotely sensed data facilitates the synoptic analysis of cropping system, and change at local and regional over time. Multi temporal remote sensing data are widely acknowledged as having significant advantages over single date imagery (Townshend et al. 1985) for studying dynamic phenomena. Mapping of cropping patterns can be improved by using variations in phenological patterns of crops as shown in the multitemporal dataset. This offers opportunities for better vegetation description than that could be achieved with a single image. Vegetation indices (VIs) and derived metrics have

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been extensively used for monitoring and detecting vegetation and land cover change (de Fries et al. 1995). Various studies have indicated that the Normalised Difference Vegetation Index (NDVI) is not much affected by topographic factors, soil background, atmosphere, etc. (Agrawal et al. 2003). Crop rotation mapping using IRS data was demonstrated by Panigrahy et al. (2002). Spatial database of parameters like crop area, cropping pattern, crop rotation, crop calendar, crop vigour etc. was created using multi-temporal, multi-spectral data (Panigrahy et al. 1996). However, in most of these studies medium resolution data (e.g. Indian Remote Sensing-Wide Field Sensor, IRS-WiFS) with around monthly interval have been used and results have been presented at district level. For studying the cropping scenario at regional or state level a comparatively coarse resolution with high temporal frequency will be sufficient. Currently, large number of data products, such as 15day composite of MODIS or 10-day composite of SPOT VGT data are available freely on Internet. The purpose of this study was to examine such dataset e.g. SPOT 10day composite NDVI products, and see its utility in analyzing cropping system at IGP and state level.

Study Area The present study covered five states (Punjab, Haryana, Uttar Pradesh, Bihar and West Bengal) in the Indian part of IGP, extending from 730 E longitude and 320 N latitude to 890 E longitude and 210 N latitude. These five states cover nearly 15.65% of the total geographical area of the country and are home to 37.4% of the population of the country. Together, they produce about 50% of the total foodgrains of the country. IGP represents eight agroecological regions and 14 agro-ecological subregions (Pal et al. 2009). IGP has four agro-climatic regions as defined by Planning Commission of India, namely

Table 1 Main characteristics SPOT-4 vegetation sensor

Trans-Gangetic Plains (TGP), Upper Gangetic Plains (UGP), Middle Gangetic Plains (MGP) and Lower Gangetic Plains (LGP). The Plains gradually slope from northwest towards the Bay of Bengal in the east and undergo a gradual transition in climate, physiography, natural vegetation and cropping systems. The rainfall ranges from less than 400 mm per year in the northwest to more than 1800 mm per year in the lower Gangetic plains of Bengal. The IGP is mostly dominated by the loam soil except for the southwestern parts of Punjab and Haryana having sandy soil, which also coincides with low rainfall.

Data Used The remote sensing data used for this study include 10-day composite NDVI product of SPOT-VGT sensor for the period May 2001 to May 2002. The data was downloaded from the VGT free data product Internet site (http://free.vgt.vito.be). The principal characteristics of the sensor (Table 1) are optimised for global scale vegetation monitoring (Stibig et al. 2000). Atmospheric corrections are routinely done using the SMAC model (Rahman and Dedieu 1994) for evaporation, ozone and aerosols effects. More details are available in the VEGETATION Users Guide (1999). 10-day products are generated using maximum value composite method. District level Agricultural Statistics provided by Department of Economics and Statistics of different states was used for comparing the agricultural land use statistics. District level cropping system maps generated from secondary data by Project Directorate of Cropping Systems Research (PDCSR) (Yadav and Rao 2001) were used for matching the spatial distribution of a cropping system in a particular state. Forest Atlas of Forest Survey of India (2001) has been used to statistically compare the forest areas of various states and their spatial distribution.

Ground swath

2250 km

Altitude

830 km

Instantaneous Field of View

1.15 km at nadir; 1.3 km at 50º off-nadir

Absolute positioning pixel

350 m

Pixel geometric superposition

< 0.5 km

Spectral channels

0.43–0.47 μm; 0.61–0.68 μm; 0.78–0.89 μm; 1.58–1.75 μm

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Methodology NDVI product from May 01, 2001 to May 2002 was downloaded from the Internet. One particular date’s data was considered to be the master scene and georeferenced using available georeferenced WiFS data. Albers Equal Area Conic projection was used. All other dates’ data were registered to the master scene and stacked to a single file. The dataset was studied for the behaviour of deciduous and evergreen forests so that the dates during which these areas have a contrasting difference from the rest of the agricultural area could be selected. For example, the evergreen forests showed very high NDVI in the month of June compared to very low NDVI of the other land cover classes. In this way, using the image of selected dates for each class, i.e. the deciduous forest, the evergreen forest and nonvegetation area a K-means unsupervised classification was applied. The spectral identification of the forest classes was carried out taking the help of Forest Atlas of FSI (Forest Survey of India) and studying the spectral profiles. Similarly, the water bodies, wasteland and settlements were classified using the corresponding dates when their discrimination was better. The forest, water, wasteland and settlement, classes were combined together to form the nonagricultural classes, and the mask generated from the remaining area formed the agricultural area. This mask was used in further classification of cropping patterns. By closely observing the image of each sate, i.e., looking into the greening and browning phenomena, the dates suitable for seasonal cropping pattern of each states was decided. As shown in Table 2, the period for each state included not only the crop growth period but also few days before and after that.

Using the data of each identified period of different season, K-means unsupervised classification was followed under the mask of agricultural area of the respective states. The K-means algorithm uses Euclidean distance to describe similarity among pixels characterized by measurements of a single variable at multiple time points. The mean of each spectral class was used to generate temporal NDVI profile. From the knowledge of crop growing period and crop distribution as derived from the district level agricultural statistics of Centre for Management of Indian Agriculture (CMIE) and district level cropping system map of PDCSR the spectral classes were identified as crop classes. The CMIE database provided the information about the major crops grown in a district, while the PDCSR maps showed the first and second major cropping systems of the district. This could result into the seasonal cropping pattern maps. Thus for each state three separate seasonal cropping patterns were obtained which were then merged into a single frame so as to obtain the seasonal cropping pattern map of the whole IGP. The crop rotation shows the temporal pattern of crops grown in a land, for example, if rice grown in a piece of land in kharif is followed by wheat in rabi and millet in summer, the rotation is called ricewheat-millet rotation. The rotations are also called as cropping system, though in strict sense, cropping system not only speaks about the temporal crop pattern but also their interaction among themselves and the management practices they receive. The seasonal cropping pattern of kharif, rabi and summer obtained through classification for each state were integrated, using logical modeling approach to form the crop rotation map. The cropping systems covering negligible area were grouped into class called ‘minor crop rotations’. The state-wise and IGP level statistics

Table 2 Period of data used for seasonal cropping pattern classification of different states States

Period of data used for Kharif

Rabi

Summer

Punjab

May begin 2001 to mid Dec2001

End Nov 2001 to end April 2002

Begin April to end June 2002

Haryana

Begin June 2001 to end Oct 2001


Begin April to end June 2002

Uttar Pradesh

Begin June 2001 to begin Nov 2001


End Mar 2002 to end June 2002

Bihar

Mid July 2001 to end Nov 2001

Begin Nov 2001 to end mar2002

Begin April to end July 2002

West Bengal

End Jun 2001 to end Nov 2001

Mid Dec 2001 to mid Mar 2002

End Feb 2002 to end April 2002



May 2001

June 2001

July 2001

Aug 2001

Sept 2001

Oct 2001

Nov 2001

Dec 2001

Jan 2002

Feb 2002

Mar 2002

Apr 2002

Fig. 1 SPOT VGT NDVI product for 12 months showing the crop growing phenomenon in Indo-Gangetic Plains

for different crop rotations were then computed and analysed. Towards accuracy assessment of the crop rotation map, 83 villages were selected from the cropping system map generated using SPOT data. The villages were selected so that each village is located inside a contiguous area representing a particular cropping system. 10 farmers were randomly selected from each village. A survey of selected farmers was carried out by preparing questionnaire related to major cropping system and its characteristics.

Results and Discussion Figure 1 shows the SPOT VGT NDVI data of IGP for different months of the period 2001–02 starting from June 2001 to May 2002. Each image represents the

first 10-day composite of a month. This figure shows the crop phenology of different regions of IGP. As it can be seen, in Kharif season the redness starts from Punjab during June and advances towards UP and Bihar. However, during May, only part of West Bengal and Bihar was red representing the summer crops grown in this region. Similarly, the redness in western UP in May represents the sugarcane crop. The maturity of kharif crop, indicated by the decreasing redness, also starts from Punjab by end of September and slowly progresses towards the eastern part of the IGP. Similarly, in rabi season, the crop growth starts in Punjab by the month of November end and by February, most of the IGP, except West Bengal, is fully covered with crops. These maps give an idea about the utility of multidate data for not only mapping the cropping pattern, but also studying the variation in crop phenology.

Table 3 Areas occupied by crops in different seasons of Indo-Gangetic Plain Kharif crops

Area (% of NSA)

Rabi crops

Area (% of NSA)

Rice

65.54

Wheat

Maize

11.76

Mustard

67.18 1.33

Cotton

1.95

Pulse

4.08

Pearlmillet

4.87

Potato

0.65

Perennial including Sugarcane

5.96

Potato-Wheat

1.76

Other

1.69

Other crops

6.06

Fallow

7.45

Fallow

15.40

Summer crops

Area (% of NSA)

Summer rice

4.72

Jute

2.59

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Rice-Wheat Sugarcane Based Cotton-Wheat Maize-Wheat Sorghum-Wheat Pearlmillet-Mustard Pearlmillet-Wheat Other-Wheat Maize-Other Pearlmillet-Pulse Rice-Fallow-Fallow Rice-Fallow-Rice Rice-Mustard-Jute Rice-Fallow-Jute Fallow-Wheat Rice-Wheat-Other Rice-Wheat-Jute Non-Agricultural area

Fig. 2 Major Cropping Systems of Indo-Gangetic plain derived from Multi-date SPOT VGT NDVI data

Agriculture land use classification was carried out to discriminate between agricultural area and nonagricultural area such as forests, rivers, urban, wasteland, etc. The analysis showed that majority of the IGP is under agriculture area. Forests are found in Southeastern UP, northern and southern West Bengal, covering Sundarban area. Also, there are forests, distributed in the Northern part of IGP, which is adjacent to the Himalayan region. Cropping Pattern The analysis of seasonal cropping pattern showed that, during kharif season, in IGP states rice is the major crop covering about 65.5% of the agricultural area of the plain (Table 3), extending from Punjab to West Bengal. Other major kharif crops include cotton in Punjab and Haryana, sugarcane, mostly in Haryana, UP and Bihar, pearlmillet, in most of the unirrigated areas of Haryana and UP, maize, in Punjab, UP and Bihar, and sorghum in Haryana. During rabi season, wheat is the major crop of the IGP except West Bengal. The total area covered by the crop is 67% of the agricultural area of the IGP, which is almost equal to that of the rice crop. Crops like sugarcane in Haryana and UP, mustard in Haryana and West Bengal,

potato in Bihar and West Bengal, and pulses in UP and West Bengal are also the major rabi crops. The summer cropping pattern shows that only about 7% of the agricultural land is under crops and most of the IGP has fallow land during the season except eastern Bihar and West Bengal where summer rice and jute are grown. Fodder crops grown in Punjab, Haryana and Table 4 Area occupied by the major cropping systems of the Indogangetic plain as percentage of the agricultural area Major cropping systems Rice-Wheat Rice-Fallow-Fallow

Area (% of NSA) 42.76 9.59

Maize-Wheat

8.13

Sugarcane Based

7.00

Pearlmillet-Wheat

3.80

Fallow-Wheat

3.36

Rice-Fallow-Rice

1.91

Cotton-Wheat

1.89

Rice-Wheat-Other

1.87

Fallow-Pulse

1.83

Rice-Fallow-Jute

0.46

Rice-Potato Minor Cropping Systems

0.51 15.03


western UP which, due to very low NDVI in many cases got mixed with fallow classes. As sugarcane is a long duration crop and occupies almost a year for its growth, it should show uniform land utilization over the seasons. But due to its different planting and harvesting time and rotation with other crops, the temporal data shows different spread of the crop in different seasons.


the SPOT VGT data product, even though of 1 km resolution, can be used for mapping major cropping systems because of its high temporal repetivity. Acknowledgement The study was carried out under the EOAM project “Cropping System Analysis”. Authors are grateful to R R Navalgund, Director, SAC for encouragement.

References Crop Rotation The crop rotations and their spatial distribution obtained by combining the kharif, rabi and summer crops of all the states are shown in Fig. 2 and their share to the total agricultural area are given in the Table 4. Here we can see that most of the Indogangetic Plains area is occupied by the rice-wheat rotation which constitutes about 40% of the agricultural area followed by Rice-Fallow-Fallow and Maize-Wheat. The drier area of Punjab where rainfall is scanty (and has light textured soil) has the cotton-wheat rotation. Whereas, in the drier region of Haryana and Uttar Pradesh, pearlmillet-wheat is the main crop rotation. The southern tip of UP, which might remain flooded and fallow during kharif, have a Fallow-Wheat rotation. The north-east corner of Bihar has an intense cropping system with three crops a year with the rotation of ricewheat-other. In West Bengal most of the area takes only a single rice crop in a year. The intense cropping areas of West Bengal are the districts of Murshidabad, Bardhaman, Nadia and Medinipur, where rice-potato/ mustard-rice/jute is followed. The sugarcanedominated regions of the three seasons were merged to form the “Sugarcane based” cropping system area. Accuracy Assessment The farmers’ survey showed that out of 83, in 82 villages, the major cropping system was the same as generated from the SPOT data. This shows the contiguous areas, which were classified under different cropping systems, were true representatives of the ground situations. However, there might be inaccuracy in the scattered minor patches representing different cropping systems. This study shows that

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