Cotton Area Estimation Using Muti-sensor RS Data and Big Plot Survey in Xinjiang

Wang Li1, Wang Changyao1,Hao Pengyu1,Shi Kaifen2, Aablikim Abdullah3

1.The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth

Chinese Academy of Sciences Beijing, China

wangli@radi.ac.cn 2. The Department of Rural Socioeconomic Survey

National Bureau Statistics of China Beijing, China

3. Survery Office in Xinjiang National Bureau Statistics of China

Urumqi, China

Abstract—An accurate agricultural statistics act as an important source of decision making to decision makers. Cotton area is very important especially to Xinjiang, which had 3.2 million tons in cotton output for 2012, accounting for half of the national yield. Although the potential of remote sensing to facilitate agriculture management has been explored but still there are grounds of improvement which can be explored to add some tools and process to a more accurate remote sensing based agriculture management system in any area. This research presents a method to estimate cotton area in Xinjiang. Many field data had been accumulated including each main crop through the Big Plot Survey from 2011 to 2013. Meanwhile the MODIS NDVI product were used to build reference NDVI time seriesaccording to the Big Plot Survey result. Higher spatial resolution RS data (Landsat-8 and HuanJing-1) merged NDVI series in 2013. Through the relative NDVI correction, the 30m merged muti-source NDVI series could be classified with sub-region classification method. Gaofen-1 satellite data were employed to validate the cotton distinguish result, which has 2m spatial resolution. Result shown our workflow is efficiency, and the total area of cotton is 1.692 million hectare in Xinjiang 2013.

Keywords—cotton area; multi-sensor remote sensing data; big plot survey; ;

I. INTRODUCTION

The synoptic view of remote sensing imagery provides the advantage to classify the crops over others. Advance Very High Resolution Radiometer (AVHRR) with coarse spatial (1-8 km) and high temporal resolution was used to classify LULC [1]. MODIS (MODerate-resolution Imaging Spectroradiometer) 250 meter EVI has the ability to map crop phenology stages in the most intensely agriculture areas [2]. In this regard time series MODIS Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) showed promising results [3][4].

However, the coarse spatial resolution generates higher amounts of mixed pixels, creating errors in crop type classification [5]. In response, multi-temporal medium spatial (10-100 m) resolution images were used to increase the amount of pure pixels [6]. Misclassification still occur because cloud-free images which cover all critical periods are difficult to obtain in large area using single sensor, such as Landsat-5TM. Muti-sensor RS data should be merged to increase the possibility of obtaining the critical periods data.

An accurate agricultural statistics act as an important source of decision making to decision makers. Cotton area is very important especially to Xinjiang province, which had 3.2 million tons in cotton output for 2012, accounting for half of the national yield.

In this research, Xinjiang was selected as the study region. Multi-temporal remote sensing images obtained from multi-sensor (Landsat TM, Huang Jing (HJ) and Gao Fen 1 (GF1)), as well as ground truth data acquired from the field survey were used to classify crops in large scale. Big Plot Survey methods were designed, and new technologies were also proposed for crop classification using multi-temporal remote sensing images. Afterwards, interpretation result from high spatial resolution remotes sensing images were employed to validate the cotton distinguish result.

II. STUDY AREA

Xinjiang lies in the north western part of China (34°30′–49°10′N, 77°17′–92°7′E). The topography ranges from desert in the south to the high rise snow covered Tian Shan Mountains in the north serving as irrigation source for agriculture. A continental, dry climate exists in the region which gets slightly wetter to the north. The average annual rainfall is 150 mm. The region has a marked difference in temperatures, during summer it exceeds 40C0 while in winter

it is constantly below -20C0. The crops grow from the March to November. Almost whole of the region rely on irrigation for their agriculture. Cotton, maize, wheat, rice and grapes are the major crops.

Fig.1 showing the study area in red color on country map while in the left the study area (Xinjiang) is enhanced for visualization, also the field sites were given in green, red and yellow color representing field samples of 2011, 2012 and 2013 respectively.

Figure 1 Study areas

III. DATA SET

A. MODIS imagery Sixteen-day composite Terra MODIS 250-m NDVI data

from the MOD13Q1 Vegetation Indices product were used in this research. These data were from NASA's Level 1 and Atmosphere Archive and Distribution System (LAADS). The MODIS NDVI images consisted of 15 scenes (March 21-November 1) in each year and spanned 3 crop years (2011,2012 and 2013). The crop calendar for the study area starts from the month of March and ends at the end of November. Thus each year have 18 composite images with sixteen days interval after processed through MODIS Re-projection Tool (MRT).

The MODIS images for 2011, 2012 and 2013 were used to build the reference NDVI time series, and the MODIS images for 2013 were used to calibrate the TM/HJ/GF NDVI images. A Savitzky–Golay filter was applied to the MODIS NDVI time series to minimise the effects of cloud cover and other sources of noise [7].

B. TM HJ-CCD and GF-1 imageryThe images were selected according to the weather

conditions, image quality and the crop calendar (March, May and September is the critical periods). We selected cloud-free satellite imagery (containing both Landsat 8 (72 secnes) and HJ CCD (18 scenes) imagery) for 3 different dates in 2013 in both study regions.

TABLE I. LANDSAT-5 TM AND HJ-1 CCD IMAGES IN STUDY REGIONS

The Gao Fen 1 (GF-1) Satellites were launched by Chinese government in 2013. It has three CCD cameras are 2m panchromatic band, 8m three visible and near-infrared bands,16m three visible and near-infrared bands.

crop area GF-1 data

Figure 2 Distribution of the GF-1data 2m resolution

C. Big Plot Field data This research designed a field survey system-Big Plot

Field Survey,for obtain the pure pixels in MODIS data in Xinjiang. The rule of field survey system is following:

The field work will be launched at 29 County, which there are the Survery Office of NBS (National Bureau Statistics of China).

The only 3-4 kinds of main crop will be investigated.

At least 3 plots for each crop in County.

Each plots should be larger than 20 hectares, as well as only one crop in it.

Field data has been acquired for the years of 2011, 2012 and 2013 in the study area. The information was collected about the crop type and coordinates were recorded on each

Path/Row 149 148 147 146 145 144 143 142 141 139 138

28579

579

579

29579

579

579

579

579

30579

479

579

579

679

679

31579

579

579

579

689

579

32579

579

579

579

579

579

33479

579

579

34579

Marked HJ-1CCD UnMarked Landsat 8Number the month of the data (3-March,5-May,9-September)

corner of the fields by Global Positioning System (GPS) resulting in a polygon. The field samples of the major crops (cotton, maize, wheat/others, rice and grapes) were used in this study. Other samples were ignored as they have not enough pure pixels to qualify for further analysis.

TABLE II. NUMBER OF FIELD SITES SAMPLE BY YEAR AND CROP TYPE.

268 field polygons were surveyed which contained the data of 11crop classes. Hence we are focusing on cotton. The other main crops of the study area which are maize, wheat with other summer crops, rice and grapes went under analysis.

IV. METHODOLOGY

The overall methodology of the study was presented in Fig.3. This study was composed of four main parts: (1) building a reference time series NDVI, (2) transform TM/HJ NDVI to comparable with reference time series NDVI, (3) crop classification using reference time series NDVI with SVM method and (4) classification validation. The accuracy was compared with the 2m resolution GF-1 data interpretation result.

MODIS NDVI2011,2012,2013)

Big plotSurvey data

Crop sample Reference NDVItime series

MODIS NDVI(2013)

TM/HJ NDVI(3 temporal)

NDVIcalibration

ComparableTM/HJNDVI

ClassficationSVM

GF-1 data

Validation

Figure 3 Methodology of the study

A. Building the reference NDVI time series Sixteen-day composite Terra MODIS 250-m NDVI data

from the MOD13Q1 Vegetation Indices product were used build the reference time series NDVI. Hence there were large enough field plot, we obtained the time series NDVI of multiyear “pure” MODIS pixels for different crop types. ASavitzky–Golay filter was applied to the MODIS NDVI time series to minimize the effects of cloud cover and other sources of noise.

B. Relative NDVI correction for multitemporal TM/HJ images In this research, Landsat-8 TM and HJ-1 CCD images

were merged to build an image time series with 30m spatial

resolution. The merged 30m data will be used to extract cotton area through comparing with the reference NDVI time series. However, the reference NDVI time series were obtained from MODIS data which was 250m resolution. The transformwould be needed.

To build the TM/HJ-1 NDVI and the reference time series NDVI with high uniformity, we calibrated the TM/HJ NDVI images using the MODIS NDVI (2013) [8].

“Pure” pixels were selected for relative NDVI correction,which contained homogeneous crop types. The linear relationships are described in Fig.4.

Figure 4 A case of relationship between the TM/HJ-CCD (NDVI calculated from the TM/HJ images) and the MODIS NDVI (NDVI calculated from the MODIS images)

C. Cotton extraction using the reference time series NDVI To classify the crop types using the 30-m resolution time

series NDVI, we first calculated the maximum NDVI duringthe growing season and then masked the non-agricultural area using a threshold of 0.35. To the agricultural area, the SVM (Support vector machine) method were used to extract cotton from other crops.

SVMs are typically a supervised classifier, which requires training samples. Literature shows that SVMs are not relatively sensitive to training sample size and scientists have improved SVMs to successfully work with limited quantity and quality of training samples [9].

For the implementation of the training and modelling procedure, we employed the SVM library (libSVM) [10]. Our purpose is to distinguish the cotton form other crops, so One-class SVM will be used.

Given training vectors x ∈ R , i = 1,2 … . l without any class information, the primal problem of one-class SVM [11] is

, , 12 − + 1subject to ∅( ) ≥ − (1) ≥ 0, = 1, …

The dual problem is 12subject to 0 ≤ ≤ , = 1, … (2) = 1Where Q = K x , x = ∅(x ) ∅(x ) . The decision

function is

Crop type 2011 2012 2013 Total Cotton 26 22 56 104Maize 17 3 19 39Wheat/summer crops 23 5 4 32Rice 3 4 14 21Grapes 14 1 8 23Pepper 2 3 3 8Tomato 3 3 3 9Sugar beet 3 0 3 6Nuts 2 0 0 2Red dates 4 2 9 15Water melon 3 0 6 9Total 100 43 125 268

March May September

( , ) − (3)In LIBSVM, we solve a scaled version of problem because

numerically may be too small due to the constraint ≤1/ . 12subject to 0 ≤ ≤ , = 1, … (4) =D. sub-region classification for Xinjiang

Xinjiang covers an area of 1.66 million square Kilometers.So big an area which has different phenology. Therefore we cannot use a uniform reference cotton time series to classify.The whole area should be segment. Some researchers use the crop calendar to divide the Xinjiang. In our research, the sub-region towards the TM scene size would be used. If TM scene size is × , the Field data including in 2 × 2 will be used to building reference NDVI time series (Fig.5).

Figure 5 Sub-region classification method

E. 2m resolution GF-1 data interpretation This research designed a validation system using high

spatial resolution remote sensing data (GF-1) for validation. Some 3km*3km grid had been chosen in each scene of GF-1data for interpretation. We surveyed 3 200m*200m on site in each 3km*3km grid, as the reference ground truth data (Fig.6).

Figure 6 GF-1 product map for field work

V. RESULT AND DISCUSSION

The cotton distribution map for Xinjiang are shown in Fig.7 . The results of the accuracy assessment for the study area are summarized in Tables 3. A case of the comparison between extracted cotton with interpretation shown in Fig.8 . According to the result, the total area of cotton is 1.692 million hectare.

Figure 7 Cotton distribution map in Xinjiang 2013

TABLE III. ERROR MATRIX FOR XINJIANG

From the classification results (Table 3) derived by the historical reference time series NDVI, a large number of the cotton pixels can be distinguished without requiring ground-truthed data in the classification process. The overall accuracies were 83.83%. The highest class accuracies were obtained for non-vegetation (97.90%). However, only 67.59%of the other crops were correctly classified. In addition, 76.50%of cotton was correctly classified, while 22.60% was misclassified as other crops. The reason of the lower accuracy of the other crops is the field plot less. Among 268 field polygons, there are 104 cotton samples, and the highest other main crops is only 39 (maize)(Table 2).

Figure 8 The comparison between extracted cotton with interpretation

Class type Cotton Other crops

Nonvegetation Tatal User's acc Producer

acc

Cotton 81555 24098 957 106610 76.50% 79.57%

Other crops 19876 45781 2076 67733 67.59% 63.93%

Nonvegetation 1067 1736 130876 133679 97.90% 97.74%

Tatal 102498 71615 133909 308022 Overall acc 83.83%

Unit number of pixels Kappa=0.749

To visually compare the classification result between extracted from merged NDVI with interpretation from GF-1 image, a series of subset images that were extracted from the GF-1 dataset are shown in Fig.8. In general, both extracted and interpretation had similar classification results. In addition, interpretation result from GF-1 were visual appealing than extracted result from 30m merged NDVI as few salt and pepper noises were observed in both images, which is caused by the huge difference at spatial resolution and consist with some previous studies.

VI. CONCLUSION

In this research, we extracted cotton area from merge muti-source 30m resolution NDVI (Landsat-8 and HJ), and proved the potential of image time series merged from the two sensors for crop extracted in large area at the example of Xinjiang.Result shown our workflow is efficiency, and this study concluded that:

(1) We can obtain image time series of 30 m spatial resolution at high temporal resolution by merging Landsat-8 TM and HJ-1 CCD data. This merged dataset can improved possibility of obtaining the critical periods data.

(2) For to get the “pure” MODIS pixels field plot, the Big Plot Field Survey had been set from 2011 to 2013. The field data can be used to build the each kind of crops reference NDVI time series

(3) Relative NDVI correction should be used before classification. Through the relative NDVI correction, the 30m merged muti-source NDVI series could be classified by “pure”MODIS each kind of crops reference NDVI time series.

(4) Sub-region classification is useful to large area crop distinguish. This method save the process of area division according crop calendar.

Acknowledgment This work was supported by the National Natural Science

Foundation of China (41371358). We thank all the people working in the projects especially for their efforts in the field work.

References

[1] H.Lu, M. R.Raupach , M T. R. Cvicar, and D. J.Barrett , “ Decomposition of vegetation cover into woody and herbaceous components using AVHRR NDVI time series,” Remote Sensing of Environment, vol. 86,no.1,PP.1-18, 2003

[2] T.Sakamoto, B. D. Wardlow, A. A.Gitelson, S. B.Verma , A. E. Suyker, and T. J.Arkebauer, “A two-step filtering approach for detecting maize and soybean phenology with time-series MODIS data,” Remote Sensing of Environment, vol. 114, no. 10, pp. 2146-2159, 2010.

[3] B.Wardlow , S.Egbert, and J.Kastens, “Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains,” Remote Sensing of Environment, vol. 108, no. 3, pp. 290-310, 2007.

[4] J. C. Brown, J. H. Kastens, A. C. Coutinho, D. D. Victoria, and C. R. Bishop, “Classifying multiyear agricultural land use data from Mato Grosso using time-series MODIS vegetation index data,” Remote Sensing of Environment, vol. 130, pp. 39-50, 2013.

[5] Y. Z. Pan, L. Li, J. S. Zhang, S. L. Liang, X. F. Zhu, and D. Sulla-Menashe, “Winter wheat area estimation from MODIS-EVI time series data using the Crop Proportion Phenology Index,” Remote Sensing of Environment, vol. 119, pp. 232-242, 2012.

[6] C. M. Hu, and P. Tang, “Automatic algorithm for relative radiometric normalization of data obtained from Landsat TM and HJ-1A/B charge-coupled device sensors,” Journal of Applied Remote Sensing, vol. 6, Mar, 2012.

[7] J. Chen, P. Jonsson, M. Tamura, Z. H. Gu, B. Matsushita, and L. Eklundh, “A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter,” Remote Sensing of Environment, vol. 91, no. 3-4, pp. 332-344, 2004.

[8] N. Huang, J. S. He, and Z. Niu, “Estimating the spatial pattern of soil respiration in Tibetan alpine grasslands using Landsat TM images and MODIS data,” Ecological Indicators, vol. 26, pp. 117-125, 2013.

[9] P.Mantero, G.Moser, S.B.Serpico, “Partially supervised classification of remote sensing images through SVM-based probability density estimation,” IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no.3,pp. 559-570,2005 43 (3), 559–570. 2005.

[10] G. Mountrakis, J. Im, and C. Ogole, “Support vector machines in remote sensing: A review,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 66, no. 3, pp. 247-259, 2011.

[11] C. C. Chang, and C. J. Lin, “LIBSVM: A Library for Support Vector Machines,” Acm Transactions on Intelligent Systems and Technology, vol. 2, no. 3, 2011.