Study on paddy rice planting area and yield monitoring

Transcription

1 Study on paddy rice planting area and yield monitoring based on MODIS A case study of the Jianghan Plain Xin Mei 1,2, Hai Liu 3,1,2,* (1 Key Laboratory of Resources Remote Sensing and Digital Agriculture, Ministry of Agriculture, Beijing , China; 2 Faculty of Resources and Environment. Hubei University, Wuhan , China 3 State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, , China) ABSTRACT The paddy rice is humanity's important crop. Jianghan plain is the important paddy rice planting area in China. However, comparing with others, the study of paddy rice remote sensing is less in there. This study takes the MODIS data from 2001 to 2007 as the main data resources, combines with the land use data, depends on the kind of paddy rice growing periods, and extracts the paddy rice planting area. According to the test, the accuracy surpasses 85%. It attained each various time average NDVI, EVI value by the each year various times paddy rice NDVI, EVI. Then this study takes the grey system theory as a new method, and introduces it to the paddy rice remote sensing monitoring. Taking the paddy rice yield per unit area as the referenced sequence and the each year various times paddy rice NDVI, EVI as the comparative sequence, calculating the gray correction degree, finally the study selects the best time to remote sensing monitoring for the paddy rice yield, and makes the yield estimation model. Key words: rice planting area, yield monitoring, MODIS, NDVI, EVI, LSWI, land use data, grey system theory, estimation model, Jianghan plain 1. INTRODUCTION The agriculture is the foundation of national economy, and agriculture of China is weak, therefore, carrying out the precision agriculture, and accomplishing the modernization is an inevitable trend [1]. The remote sensing technique is one of the most important monitoring technological methods for the precision agriculture. The paddy rice is humanity's important crop [2]. Jianghan plain is one of the nine big commodity grains in China, and it is the important paddy rice planting area. However, comparing with others in China, the study of paddy rice remote sensing is less in there. This * Contract author. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, , China. address:liuhai11191@163.com (H. Liu). International Conference on Earth Observation Data Processing and Analysis (ICEODPA), edited by Deren Li, Jianya Gong, Huayi Wu, Proc. of SPIE Vol. 7285, SPIE CCC code: X/08/$18 doi: / SPIE Digital Library -- Subscriber Archive Copy Proc. of SPIE Vol

2 study hopes to fill up the shortage with the case of some place in Jianghan plain. The scholars in the domestic and foreign have done some researches in this aspect [3-5]. But the precision is low in provincial level scope if only using the MODIS data. This study combines with the land use database, which can improve the extracted precision of paddy rice planting area, enable the monitoring result to have the practical significance and lay the sold foundation for the later period estimation. The gray theory has been used in many aspects, but little in paddy rice monitoring. This study takes the grey system theory as a method, introduces it to the paddy rice remote sensing monitoring, unites the remote sensing image and the gray theory, selects the best period for remote sensing monitoring the paddy rice yield, and sets up the yield estimation model. 2. STUDY AREA The study area is one of city in Jianghan plain, which located between and of the eastern longitude. It covers a total surface area of km 2 and it extends for nearly 55.6km western - eastern. The topography is smooth, and low-lying, it is the plain lake. The climate is the subtropics monsoon, and the average temperature is 16.1, and the average annual precipitation is 1200mm. The sunshine is sufficient; and the quantity of heat is rich. The precipitation is abundance and the rain and heat at the same time. It includes 24 towns, three breeding farms. The total population has 1,080, DATA 3.1 MODIS data MODIS (Moderate-resolution Imaging Spectrometer) belongs to the Earth Observation System (EOS), it is flown on Terra and Aqua satellites [6]. MODIS is in a 705km Sun-synchronous orbit. It views the entire surface of the Earth every 1 to 2 days. MODIS obtains high radiometric resolution images of daylight-reflected solar radiation and day/night thermal emission over all regions of the globe. It collects data in 26 co-registered spectral bands:0.4 to 3μm and 16 bands from 3 to 15μm. MODIS spatial resolution ranges from 250*250m (band1 and 2) to 500*500m (bands 3 through 7) and 1*1 km (bands 8 through 36). It provides long-term observations and yields simultaneous observations of high-atmospheric (cloud cover and association properties), oceanic (sea-surface temperature and chlorophyll), and land surface (land-cover changes, land-surface temperature, and vegetation properties) features. In this article, we obtain 16-day composite images (250-m spatial resolution) of MODIS/TERRA VEGETATION INDICES 16-DAY L3 GLOBAL 250M SIN GRID the V004 product data in June to September from 2001 to It includes turn green stage (the th day), tiller stage (the nd day), joint stage (the th day), boot stage (the th day), head stage (the st day), milk stage (the day).besides that, we also accept two phase 8-day composite images (500-m spatial resolution) of MODIS/TERRA SURFACE REFLECTANCE 8-DAY L3 Proc. of SPIE Vol

3 GLOBAL 500M SIN GRID the VOO5 product data in paddy rice transplanting time(the th day,the th day). 3.2 Land use data The land use status information system is a part of the land management information system. In China, the land use database has been established one after another. It realizes the land use status data and maps storage, the management, the retrieval, the inquiry, the statistics, the analysis, the change and the maintenance, by inputting the land use status survey and change into the computer. The paper studies extract the paddy field from the land use database with the help of GIS software. 3.3 The Field Survey data We have carried on the field survey in August, 2007, with the aid of the GPS, taking the remote sensing and the land use map which extract from land use database as the reference map. We have surveyed the land use characteristics of the study area, and gathered geography information of fifty paddy rice planting areas and fifty non-paddy rice planting areas. Fig. 1 Field survey map 3.4 Statistical Data We gained the paddy rice planting area and yield per unit area from 2001 to 2006 according to the statistical annuals. Through consulting to the related Agriculture department, we gained the data in Table1 Paddy rice planting area and yield per unite area Year Proc. of SPIE Vol

4 Area(hm 2 ) Yield per unit area(kg) METHOD 4.1 Extracting paddy rice planting area from the multi-temporal MODIS images with the help of GIS Vegetation Index Description The vegetation index (VI) is the spectrum quantity value related with surface vegetation status, which withdraws from the multi-spectrum remote sensing data. It can reflect vegetation t status quantitatively and is more objective than the visual interpretation. The vegetation index is also convenient for GIS spatial database directly and is good for the analysis comparison of the similar type. The Normalized Vegetation Index (NDVI) is sensitive to the green vegetation, and assumes the linear relationship with the coverage density.it is often used to estimate the status of vegetation. When vegetation fractional coverage is smaller than 80%, NDVI assumes the linear rise along with vegetal quantity; but when it is bigger than 80%, NDVI sensitivity would drop. NDVI can better adapt to monitor the large scale landscape that vegetation is sparse and fractional coverage difference is large. NDVI may be calculated based on the following equation: NDVI = ρ ρ ) /( ρ + ρ ) (1) ( nir red nir red Where ρ nir is the near infrared band, ρ red is the infrared band. The Enhance Vegetation Index is a modified NDVI. It uses the blue band to modify atmospheric influence to the red band, and constructs one feedback mechanism that adjusts the atmosphere and soil simultaneously. EVI improves sensitively to high-biomass regions and mends vegetation monitoring through a decoupling of the canopy background signal and a reduction in atmospheric influences [7]. EVI is the supplement mutual with NDVI. It has a soil adjustment factor, L, and two coefficients, C 1 and C 2, which describe the use of the blue band in correction of the red band for atmospheric aerosol scattering. The coefficients, C 1, C 2 and L, are empirically determined as 6.0, 7.5 and 1.0, G is a gain factor set to 2.5. EVI may be calculated based on the following equation: EVI = G ( ρ ρ ) /( ρ + C ρ C ρ ) (2) * nir red nir 1 red 2 blue + L Where ρ blue is the blue band. The land Surface Water Index (LSWI) makes use of the shortwave infrared band (SWIR). The SWIR spectral band is sensitive to leaf water and soil moisture, and is used to develop improved vegetation indices that are sensitive to equivalent water thickness (EWT, g H2O/m2), including LSWI [8-9].LSWI monitor the paddy rice field at transplanting Proc. of SPIE Vol

5 time to have the good effect. It may be calculated based on the following equation: LSWI = ρ ρ ) /( ρ + ρ ) (3) ( nir swir nir swir Where ρ swir is the shortwave infrared band Extracting potential paddy rice planting area and selecting vegetation index As the special growth feature, the paddy rice is grown on flooded soils. So it only adapts to the growth in the paddy field. In order to extract more accurately and quickly, we extract paddy field from the land use database as potential paddy rice planting area. According to NDVI, EVI of the paddy rice during the different growth period, some scholars have extracted paddy rice planting area. However, in the study area, we discover the paddy field isn t only planting paddy rice, but also has the big area of cotton, and the cotton has same time planter growth with paddy rice. Therefore, it doesn t easy to extract the paddy rice in the paddy field only uses NDVI and EVI. Compared the habitat's request in different development periods, we discover during the flooding and rice transplanting period, the land surface is a mixture of surface water and green rice plants, with water depths usually between 2 and 15 cm. At the same time, in cotton plant area, it doesn t need flood, and the land surface is a soil and green rice plants. About 50 to 60 days after replanting, canopies cover most of the surface area. Therefore, in the rice paddy plant area extraction, we may choice the flooding and rice transplanting period LSWI that calculate by SWIR which is sensitive to leaf water and soil moisture, and choice the boot stage NDVI and EVI which are sensitive to the vegetation Data pretreatment The MODIS primary data format is the *.hdf,in order to deal with expediently, at first,carrying on the data conversion, the *.hdf was conversed to *.tif, the projection type is modified to Albers equal area projection, and the spatial resolution unifies 250*250m. In data choice, to the 16-day composite (250-m spatial resolution) V004 data, we choose NDVI and EVI, and to the 8-day composite (500-m spatial resolution) V005 data, we choose b02 (the near infrared band) and b06 (the short wave infrared band). To various times NDVI, EVI, b02, and b06, carrying on the projection, we obtain the geodetic coordinates information data. According to equation: LSWI = b b ) /( b + ), we obtain LSWI. Taking the study area boundary map as mask, after ( b06 processing, we obtain the NDVI, EVI and LSWI of the study area Spatial model and extracting paddy rice plant area Taking the paddy field map and vegetation index as data resources, making model, we extract the paddy rice planting area. In the model, the input Vegetation Index includes the LSWI of the two periods before and after Proc. of SPIE Vol

6 transplanted(the th day,the th day)and the NDVI, EVI during milk stage (the day). At the beginning, taking the VI and paddy field distribution map to do the superposition analysis, we attain the paddy rice possible plant area. Then we attain paddy rice plant area through establishing the threshold. Vegetation Index Paddy Field Map Superposition Analysis Paddy Rice Possible Planting Area Thresholding Paddy Rice Planting Area Fig.2 Paddy rice planting area extracting model 4.2 The remote sensing monitors the paddy rice yield base on the grey system theory Grey system theory introduction The paddy rice yield's formation is a complex biological process that is influenced by many factors, and its process has the uncertainly stochastic dynamic characteristic. Therefore, if we utilize remote sensing to monitor paddy rice yield, at the beginning, we must study the biological mechanism, hold the main influencing factors that influence and form the rice yield, and identify to the main periods of time for factors when they call into play. The gray system theory was proposed in 1982 by china Professor Deng Julong. Since established, regardless of the fundamental research or the applied, it has developed rapidly and formed a new research area. The grey system theory has become a very effective method for solving uncertainty problems under discrete data and incomplete information [10-11]. It is widely applied in the agriculture, industry, military, medicine, urban construction, transportation, geology, meteorology, finance, ecology, heredity, hydrology water conservation, education and so on. The gray correlation degree analysis is the important content of the grey system theory. It is the method to scale correlation degree according to similar or different development status degree of the various system factors or behaviors. It assume x 0 (k) and x i (k) respectively be the k time value of the basic sequence and the series for comparison factor, then the k time s gray correlation degree equation is: min min x0 ( k) x j ( k) + 0.5max max x0 ( k) xi ( k) i k i k γ ( x0 ( k), xi ( k)) = x ( k) x ( k) + 0.5max max x ( k) x ( k) 1 γ ( x0, xi ) = n n k = 1 γ ( x ( k), x ( k)) 0 0 i i i k 0 i (4) Proc. of SPIE Vol

7 The association degree size shows how some comparative factors influence analytic factors. It is bigger, indicates the influence is bigger, and it is the contributing factor Gray correction degree analysis It can attain the average NDVI, EVI value for different years or periods according to the NDVI, EVI images of paddy rice for different times. Then taking the paddy rice yield per unit area as the referenced sequence and the each year various times paddy rice NDVI, EVI as the comparative sequence, calculating the gray association coefficient, gray association degree, and sort the association degree by size Making regression model Based on the correction degree size, it selects the optimal factor that influences the paddy rice yield. Taking the optimal factor as independent variable and the paddy rice yield per unite area as dependent variable, it makes the paddy rice yield estimation regression model 5. RESULTS 5.1 Paddy rice plant area extraction and accuracy assessment Paddy rice plant area According to method of 4.1, we attained the each year paddy rice planting distribution maps. Pig.3 Paddy rice planting distribution map in 2001 Pig.4 Paddy rice planting distribution map in 2002 Proc. of SPIE Vol

8 Pig.5 Paddy rice planting distribution map in 2003 Pig.6 Paddy rice planting distribution map in 2004 Pig.7 Paddy rice planting distribution map in 2005 Pig.8 Paddy rice planting distribution map in 2006 Pig.9 Paddy rice planting distribution map in 2007 The paddy rice plating areas of each year are calculated through inquiring image attributes information of the paddy rice planting distribution and combining with the pixel precision of the image. Proc. of SPIE Vol

9 Table2 Paddy rice planting area Time(year) area(hm 2 ) Accuracy assessment There offered two methods to test the extracted effect of the paddy rice planting area. One way is accuracy assessment report created by the classification accuracy assessment function of the software; the other way is compared with the extracted paddy rice area and statistical data to attain the accuracy assessment report. (1) classification accuracy assessment There has two ways to assess classified accuracy and create classified assessment report. The fist way is taking the TM image as reference image, compared with paddy rice extraction image. The second way is compared with the field survey data and paddy rice extraction image. 1) TM as reference image It selected 250 points stochastically in the extracted paddy rice planting distribution map in 2002, compared with the TM image in 2002, and created classified accuracy assessment. The table3 shows that user accuracy is 85.06%, and producer accuracy is 89.16%, and overall classification accuracy is 89.60%. Each kind of accuracy surpasses 85%. Table 3 Accuracy assessment report Reference totals Classified totals Number correct Producers accuracy Users accuracy Non-paddy rice Paddy rice Totals Overall Classification Accuracy = 89.6% 2) Field survey data Accuracy assessment report was created though 100 field survey data which obtained in 2007 compared with extracted paddy rice distribution map in The table4 shows that user s accuracy is 86.58%, and producer accuracy is 86%, and overall classification accuracy is 88%. Each kind of accuracy surpasses 85%. Table 4 Accuracy assessment report Reference totals Classified totals Number correct Producers accuracy Users accuracy Non-paddy rice Paddy rice Totals Overall Classification Accuracy = 88% (2) Area accuracy assessment Proc. of SPIE Vol

10 Using year statistical area, comparing with the extracted paddy rice planting area, it calculated the area accuracy. The table5 shows that the biggest extracted proportion is 90.72% and the least is 85.38%. Each kind of accuracy also surpasses 85%. Table 5 Area accuracy assessment Year Statistic area (hm 2 ) Extracted area (hm 2 ) Proportion (%) Paddy rice yield remote sensing estimation model Extracted the estimated factor base on gray correction degree analysis (1) Extracted the each kind of paddy rice NDVI and EVI value in 2001 to 2007 Table6 Paddy rice each kind of NDVI in NDVI d d d d d d Table7 Paddy rice each kind of NDVI in EVI d d d d d d (2)Calculating gray correction degree Proc. of SPIE Vol

11 Analyzing paddy rice yield per unite area from 2001 to 2007, it showed that the data (529kg) in 2003 was smaller than the average of several years (616kg). Therefore, we rejected it, and took the other six year data to calculate gray correction degree. γn = γn = γn = γn = γn = γn = γe = γe = γe = γe = γe = γe = (3) Correction degree sort γe >γn >γe >γn >γn >γn >γe >γe > γn >γe >γe >γn (4) Extracting estimation factor According to the above analysis, it showed that the most remarkable three factors in eight growing periods among the sixteen influenced factors are e , n , e Their gray associate degrees all surpass 0.8; therefore, we selected them as the optimal influenced factors Yield estimation regression model Taking e ,n and e as independent variable, yield per unite area as dependent variable, we set up the multivariate regression analysis model. Y = X e X e X n (5) Where Y X is the yield per unite area, e 241 is the th day EVI, X e209 X is the th day EVI, n 193 is the th day NDVI. In regression model, multiple R is 0.958, R square is After the variance analysis, F is , Sig. is 0.039, and therefore the regression model is effective. 6. DISCUSSION AND FORECAST There has some deviation in extracted paddy rice planting area. One of the reasons is the low spatial resolution, therefore it exists the mix pixel problem. Although the paper had already done some work to resolve this problem, it still isn t sufficient. So studying the mix pixel decomposition is the next step key emphasis in work. Proc. of SPIE Vol

12 For the paddy rice yield remote sensing monitoring, we had considered various growing period vegetation indices. However, yield s formation is a complex process, and it is influenced by lots of factors. Therefore, studying other influential factors is also the research direction in future. ACKNOWLEDGMENTS The authors would like to thank the Key Laboratory of Resources Remote Sensing and Digital Agriculture Ministry of Agriculture.P. P. China and Hubei natural science Council for providing financial support for this research. REFERENCES [1]Zhao,G.,Q,,Wang,J. and Mu,X., T., Research advancement of precision agriculture, Meteorological and Environmental Sciences, Papers 30(1):84-88(2007). [2]Wang, R. C., Huang,J, F., [Rice yield estimation using remote sensing data], China Agriculture Press, Beijing,28-30(2002). [3]Xiao,X.,M., Stephen,B., Steve,F., et al, Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images, Remote Sensing of Environment, Papers100(1):95-113(2006). [4]Wardlow,B.,D., Egbert,S.,L.and Kastens,J.,H., Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains, Remote Sensing of Environment, Papers 108(3): (2007). [5]Zhang,Y., S., Yuan,L.,F. and Yao, Y., H., Study on Extraction of Paddy Rice Fields from Multi temporal MODIS Images, Journal of Remote Sensing, Papers 11(2): (2007). [6]John, R..J., [Introductory digital image processing: A remote sensing perspective (3rd edn.)], Publishers, Upper Saddle River (2005). [7]Huete,A.,R., Didan,K., Miura,T., et al, Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices, Remote sensing of Environment,83(1): (2002). [8] Maki, M., Ishiahra,M. and Tamura, M., Estimation of leaf water status to monitor the risk of forest fires by using remotely sensed data, Remote Sensing of Environment, Papers 90(4): (2004). [9] Xiao,X., Boles,S., Frolking,S., et al., Landscape-scale characterization of cropland in China using Vegetation and Landsat TM images, International Journal of Remote Sensing, Papers 23(18): (2002). [10]Chang,S.,C, Pai,T.,Y., Ho,H.,H.,et al., Prediction of relative dynamic elasticity modulus by extending a grey system theory, Journal of the Chinese Institute of Engineers. Papers 18(3): (2007). [11] Pai,T.,Y, Hanaki,K.,Ho,H.,H.,et al., Using grey system theory to evaluate transportation effects on air quality trends in Japan, Transportation research part D-transport and environment, Papers 12(3): (2007). Proc. of SPIE Vol