[IEEE 2012 First International Conference on Agro-Geoinformatics - Shanghai, China (2012.08.2-2012.08.4)] 2012 First International Conference on Agro- Geoinformatics (Agro-Geoinformatics) - Estimation of cotton yield based on net primary production model in Xinjiang, China

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Estimation of Cotton Yield Based on Net Primary Production Model in Xinjiang,China Xiuliang Jin National Engineering Research Center for Information Technology in Agriculture Beijing , China Jinxiuxiuliang@126.com Xinguang Xu National Engineering Research Center for Information Technology in Agriculture Beijing , China xxg_xin_gang@126.com AbstractTime series data of the China environment and disaster reduction satellite (HJ) ,TM images and improved Carnegie Ames Stanford Approach (CASA) model were used in this study to estimate cotton yield in 121groups Xinjiang province. we used CASA model to calculate the cotton net primary production (NPP),(NPP=(SOL FPAR0.5)).Finally, yield was estimated through converted the NPP to biomass, then cotton yield were obtained and validated by the field data. For NPP model, the relative error between the predicted cotton yield and the actual yield HJ was -18.00%,and TM images was -16%. It was feasible to predict cotton yield by HJ satellite data for estimating cotton yields. But in this paper, the light use efficiency () as the constant, we had not considered the influence of the temperature and precipitation of the space variability and climate condition, all of these needed further study. Keywords- remote senseing; cotton; net primary production (NPP); the fraction of the photosynthetically active radiation (FPAR); yield estimation I. INTRODUCTION Xinjiang is an important base of commercial cotton in china. Cotton production hasmade great contributions to the promotion of economic development in the Xinjiang region. Remote sensing technology may access to timely and accurate the cotton growing and producting information, it has great significance for sustained and healthy development of the cotton industry. Remote sensing has developed rapidly as a high-tech, with wide coverage, fast access to information, informative, immediate and,low cost, and provided technical support and security for monitoring rapid crop growth and estimating accurate yield rapidly [1-2]. Crop yield was estimated in a wide range, especially crop yield estimation has been widely used in state-level [3-5]. At present, crop biomass is generally calculated by using net primary productivity (NPP), because have a high significant positive correlation with NPP, and the NPP can be obtained through remote sensing methods. NPP was determined by the photosynthetical -ly active radiation (PAR),Fraction of absorbed PAR (FPAR) and light use efficiency constant () [6-7]. NPP was usually estimated through statistical models, parameter estimation model and process model. The parameter model of CASA which is widely used that has a few factors and easy to obtain. FPAR is the key structural variables for estimating NPP, and has great significance in the regional, national and global net primary production and climate, hydrology, biology, geochemistry and ecological research. There are many studies of crop yield assessment by using NPP [8-11]. Ren et al. constructed models for plain yield estimation of winter wheat by using MODIS image data gained NPP, the results showed that between the predicted biomass by using NPP and actual biomass relative error was -4.30%, the average of predicting biomass and actual yield Relative error was -4.41% for winter wheat [12,13]. Long et al.(2010) used of NOAA/AVHRR NDVI remote sensing data to construct estimates of NPP model of grassland in Inner Mongolia from 1982 to 2006,the total of NPP increased from 443.68 to 536.88Mt C/a, the average increased 0.861Mt C/a [14]. David et al. (2003) calculated NPP for wheat yield by using TM images, the results showed that the error of estimation yield and actual yield was less than 4% [15]. Bastiaanssen (2003) using the image fusion technology, builded a model of NPP of crop production for rice and wheat yield estimation, error was 1075 kg/hm-2 and 246 kg/hm-2 [16]. Tao et al. (2005) using the GLO-PEM2 and the CASA model to estimate for maize production, the results showed that the yield estimation problem was too high or too low in the different regions, but the accuracy of the CASA model model was higher than the GLO-PEM2 [17]. Therefore, the regional crop production obtained by calculating the NPP has some research base. In recent years there were some investigations of using remote sensing data with spatial resolution was low and mainly were carried out in the national or regional scale, using the CASA model to estimate crop production was mainly food crops. However, there is little information in current literature about high-resolution satellite imagery, especially Chinese remote sensing data combined with the CASA model made for small-scale cotton production estimation. In this paper, the Chinese environment with high-resolution satellite images (HJ satellite) data as the information source, the CASA model Improved for estimating biomass accumulation in the key cotton growing season. The main objective of this study was to explore for adapting to the methods of small-scale cotton yield estimation, provided the basis of the operating system for cotton production. II. MATERIALS AND MEHTODS A. Study Area Field measurements were carried out in the 121 farm groups of Shihezi in Xinjiang province in 2009 and 2010(Figure 1), with a typical continental climate. The farm is located at latitude 44 37 '~44 58', longitude 8520 ~ 8550',the total area of 704.74 square kilometers. The cotton is 85% of crop cultivated area. Regional climate: the maximum temperature was 43.1 in summer; the minimum temperature was -42.3 in winter, temperature changes rapidly in all season, the temperature difference between day and night is larger. The annual average of little precipitation is 141.8 mm, the annual average evaporation is 1826.2 mm, annual sunshine time was 2861.6 hours, average frost-free period was 166 days. B. FPAR Algorithm The airborne remote sensing platform were used to estimate FPAR (Gamon et al.,2004).Within a certain range, the linear relationship existed between FPAR and NDVI (Fensholt et al.,2004). This research were( Potter et al.) proposed Eq(1): minmax min( , ) min[ ,0.95]SR SRFPAR x tSR SR= (1) Where The SR is ratio vegetation index which is composed of near infrared and infrared reflectance, its value was 1.02 that is relate to vegetation types, changed from 4.14 to 6.17, the farmland is generally 4.14, FPAR was different with vegetation types and seasons change (Christopher et al.1995; Hanan et al.,1995).SR (x, t) by the NDVI (x, t) was obtained, Eqs.(2) and (3): 1 ( , )( , )1 ( , )NDVI x tSR x tNDVI x t += (2) ( , ) ( , )( , )( . ) ( , )NIR x t R x tNDVI x tNIR x t R x t=+ (3) Where NDVI (x, t) Eq.(2) is on the pixel x of t month, the NIR (x, t) Eq.(3) and R (x, t) are near red-infrared and red reflectance on the pixel x of t month ,respectively. C. NPP Construction and Cotton Yield Algorithm NPP model has great potential to estimate crop yield. It not only monitored rapidly large-scale vegetation NPP, but also help to link the environment and vegetation variables, and using NPP to descripte quantitative the interaction between environment and vegetation. Therefore, this study used plant NPP model to estimate cotton biomass. Eq.(4) as follows: NPP=(SOLFPAR0.5) (4) Where, SOL is the surface global solar radiation (Solar radiance, W m-2), FPAR is the fraction of absorbed PAR for the vegetation canopy. 0.5 was that vegetation can account for the ratio of global solar radiation, is the light energy efficiency that is the rate of light transition into dry matter and dimensionless, is a variety that it is relate to temperature and precipitation, you can make it as a constant. The studies of crop yield assessment in a wide range, to be regarded as a variety. As study was small area, so selected the maximum rate of light energy as constant.The constant() were 1.45% through studied the measured data and investigated local literatures.So Eq.(4) can be simplified as Eq (5): NPP=0.5PAR (5) FPAR and PAR can be calculated by remote sensing data, NPP can be obtained from biomass harvest, NPP model was implemented in ENVl4.7. Cotton production formula constructed as Eq.(6): Yield=NNP (6) Where, is the economic coefficient that calculated by the seed cotton yield , in this study was to use the dry weight and seed cotton yield in the vomiting of the forty-nine sampling sites, the average of the forty-nine sampling sites was 0.395 . D. Romete Sensing Data Processing The studies used the HJ-1 image data in 2009 and 2010 from June to August, track number is 457/72, the data for secondary level. All HJ satellite image data were analyzed by the ENVI4.7. Ground control point were gained by differential GPS and executed a field survey. The HJ satellite image data were completely corrected by the ENVI module. Map projection used Lat/lonWGS-84, pixel size 30 m30 m. This atmospheric correction was performed using the "6S" model. The remote sensing image processing software developed by institute of remote sensing applications Chinese academy of sciences was used.In this study, the data included the FPAR, multi-spectral environmental satellite data, NDVI (NDVI by the Eq. (3) calculated ) and SOL (SOL (x, t) represented pixel x in t month, at the global solar radiation, MJm-2month-1, SOL=PAR/0.5, which measured PAR obtained using the instrument on the ground.). E. Calculated FPAR Crop photosynthesis was mainly absorbed by 400~700 nm wavelength radiation, crop health had a more sensitive response to the 700~1350 nm , and the health status was also related to absorption and photosynthesis of plants, so the reflectance data of 350-1350 nm was selected for analysis. Photosynthetic active radiation was partly absorbed by the vegetation canopy during transmission, partly a direct reflection and through the canopy, then the canopy reflectance back into the atmosphere, the remainder was absorbed by soil. Therefore, The PAR was obtained through Eq.(7): Units:g m-2 Units:g m-2 Units:g m-2 121 groups FPAR=[ PARci-PARcr-(PARgi-PARgr)]/PARci (7) Where PARci is photosynthetically active radiation(PAR) incident above the canopy, PARcr is PARreflectance above the canopy, PARgi is PARincident above the ground, PARgr is PAR above the ground. The FPAR indicated that how much light energy was intercepted by the cotton canopy, the greater light energy was intercepted , the more value is, on the contrary the value is less. F. Cotton NPP Data Acquisition The fifteen cotton samples were gathered by the GPS from June to August. Sampled time and transited time of satellite images were synchronization. The sampled methods was in the shape of Z ,measured five representative samples with the size of the 3 m3 m, and then to average. Calculated the subplots average conversion to plots. The NPP was changed by sampling data in each month. G. Cotton Yield Determined Cotton yield were measured in 121 groups, selected 49 representative samples. Measured accurately cell area that was 15 m15 m. Measured the total number of plant cotton district, and recorded the total number of diameter greater than 3cm of bolls, each treatment selected 10 bolls and then dryed to constantly weight, according to Eq. (8) calculated cotton yield . Cotton yield (Kghm-2) =plot area (m2)cotton bollscotton boll weight/10000 (8) III. RESULTS AND CONCLUTION A. NPP Changing and Calculation Calculated the production of PAR absorbed by farmland and light use efficiency() to obtianed the NPP distribution in different months. The results showed that NPP had a siginificantly differences from June to August(Figure 2). 93% of NPP was 100~150g m-2,and only 7% is lower than 100 g m-2 in June. 97% of NPP was 100~250 g m-2, 3% greater than 200 g m-2, the average was 138 g m-2 in July. 95% of NPP was 100200 g m-2, 5% greater than 200 g m-2,the averaged of 182 gm-2 in June, July, August cumulatived NPP accounted for 25%,30% and 45%, respectively. Figure 1. Location of the research region Figure 2. Distribution of average NPP of June to August,2009 and 2010 B. Calculation Cotton Production and Ground Verification The study of cotton from June to August was ordered as budding, flowering and bolling and vomiting. Cotton biomass and NPP had increased gradually until the photosynthesis of cotton was stopped. Therefore, the results was verified by the relationships between NPP cumulated and biomass measured on the ground from June to August. In order to establish the relationship between actual investigation of cotton production and net primary productivity, cotton production measured and net primary productivity calculated by the Eq.(8). Cotton yield were measured in the vomiting of 121 groups. Remote sensing images NPP calculated by Eq.(5), and then established the correlation between yield obtained on the ground and NPP calculated. The results showed that NPP obtained by biomass and cotton production by actual investigated had a significant linear relationship (R2=0.8782**)(Figure 3). y = 27.941x - 7273R2 = 0.8782150020002500300035004000450050005500600065007000330 350 370 390 410 430 450 470 490 510Net primary production g/m2The yield of cotton kg/hm2 Figure 3. Relationships between NPP and actual cotton yield Figure 4. Distribution of average yield,HJ,2009 and 2010 Figure 5. Relationships between estimated yield and actual cotton yield. Cotton NPP be converted into seed cotton yield By Eq.(6), then analyzed the relationship between the NPP be converted into seed cotton yield and measured. To reduce the error, the environmental satellite images were used to calculate the spatial distribution of NPP, used the corresponding ground survey points around the average diameter of 120 m as this points of NPP, analyzed the relation between the average of cotton NPP and measured cotton seed yield on the ground. The results indicated that the cotton production estimated by NPP, and then calculated cotton yield to generate yield distribution map (Figure 4.) by generated a 60 m radius of the survey point buffer, and calculated the 60 m cotton production, and then made comparison with the actual survey yields. The results showed that the average absolute error of all survey points was -877.5Kg/hm2,the average relative error was -18.00% (Figure 5). In this study, these were explored ,and confirmed that the environmental satellite data source used for cotton production was feasible. ACKNOWLEDGMENT The study was supported by the National Natural Science Foundation of China (Grant No. 41001244), Beijing Nova Program (Grant No.2011036). The authors are grateful to Mr. Weiguo Li, Mrs. Hong Chang for data collection. REFERENCES [1] J. F. Shanahan, J. S. Schepers, D.D. Francis, Use of remote sensing imagery to estimate corn grain yield. Agronomy Journal,2001,93:583-589. [2] J. F. Huang, S. C. Tang, A. I. Ousama, Rice yield estimation using remote sensing and simulation model.Journal of Zhejiang University Science A,2002, 3:461-466. [3] P. C. Doraiswamy, T. R. Sinclair, S. Hollinger, Application of MODIS derived parameters for regional crop yield assessment.Remote Sensing of Environment,2005, 97:192-202. [4] M. Launay, M. Gurif, Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications. Agriculture Ecosystems and Environment,2005,111:321-339. [5] J. Q. Ren, Z. X. Chen, Q. B. Zhou, Regional yield estimation for winter wheat with MODIS NDVI data in Shan Dong,China.International Journal of Applied Earth Observation and Geoinformation,2008,10:403-413. [6] S. Brogaard, M. Runnstrom, J. W. Seaquist, Primary production of Inner Mongolia,China,between 1982 and 1999 estimated by a satellite data driven light use efficiency model.Global and Planetary Change,2005, 45: 313-332. [7] F.L.Tao, M. Yokozawa, Z. Zhang, Y. Xu, Y. Hayashi, Remote sensing of crop production in China by production efficiency models: models comparisons, estimates and uncertainties. Ecological Modelling, 2005,183, 385396. [8] G. B. Bonan, The land surrface climatology of the NCAR land surface model coupled the NCAR community climate model .J.Clim.1998,11,1307-1327 [9] R. E. Dickinson, S. A. Henderson, P. J. Kennedy, M. F.Wilson, Biosphere-Atmosphere Transfer Scheme (BATS) for the NCAR CCM,NCAR/TN-275-STR.1986,NCAR Research,Boulder,CO. [10] S.W. Running, J. C. A. Coughlan, general model of forest ecosystem processes for regional applications. Ecol. Model.1988,42,124-154. [11] P. J. Sellers, D. A. Randall, G. J.Collatz, J. A. Berry, C. B. Field, D. A. Dazlich, C.Zhang, G. D.Collelo, L. A.Bounoua, Revised land surface parameterization (SIB2) for atmospheric GCMs:Part I. Model formulation. J.Clim. 1996, 9, 676-705. [12] J. Q. Ren , X. R. Liu , Z. X. Chen, Q. B. Zhou, H. J. Tang, Prediction of winter wheat yield based on crop biomass estimation at regional scale. Chinese Journal of App lied Ecology,2009,20 (4):872-878. (in Chinese) [13] J. Q. Ren, Z. X. Chen, H. J.Tang, R. X. Shi,Regional yield estimation for win ter wheat based on net primary production model.Transactions of the CSAE, 2006,22(5):111-117. (in Chinese) [14] H. L. Long, X. B. Li, H. Wang, D. D. Wei, C. Zhang, Net primary productivity (NPP) of grassland ecosystem and its relationship with climate in Inner Mongolia. Acta Ecologica Sinica, 2010,30(5):1367-1378. (in Chinese) [15] B. L. David, P. A. Gregory, J. I. Oritiz-Monasterio, Remote sensing of regional crop production in the Yaqui Valley,Mexico:etimates and uncertainties .Agriculture, Ecosystems and Environment,2003,94:205-220. [16] G. M. W. Bastiaanssen, S. Ali. A new crop yield forecasting model based on satellite measurements applied across the Indus Basin,Pak istan.A gricuture .Ecosystems and Environment,2003,94:321-340. [17] F. L. Tao, M.Yokozaw, Z. Zhang. Remote sensing of crop production in China by production efficiency models:models comparisons,estimates and uncertainties. Ecological Modelling, 2005, 183: 385-396. /ColorImageDict > /JPEG2000ColorACSImageDict > /JPEG2000ColorImageDict > /AntiAliasGrayImages false /CropGrayImages true /GrayImageMinResolution 200 /GrayImageMinResolutionPolicy /OK /DownsampleGrayImages true /GrayImageDownsampleType /Bicubic /GrayImageResolution 300 /GrayImageDepth -1 /GrayImageMinDownsampleDepth 2 /GrayImageDownsampleThreshold 2.00333 /EncodeGrayImages true /GrayImageFilter /DCTEncode /AutoFilterGrayImages true /GrayImageAutoFilterStrategy /JPEG /GrayACSImageDict > /GrayImageDict > /JPEG2000GrayACSImageDict > /JPEG2000GrayImageDict > /AntiAliasMonoImages false /CropMonoImages true /MonoImageMinResolution 400 /MonoImageMinResolutionPolicy /OK /DownsampleMonoImages true /MonoImageDownsampleType /Bicubic /MonoImageResolution 600 /MonoImageDepth -1 /MonoImageDownsampleThreshold 1.00167 /EncodeMonoImages true /MonoImageFilter /CCITTFaxEncode /MonoImageDict > /AllowPSXObjects false /CheckCompliance [ /None ] /PDFX1aCheck false /PDFX3Check false /PDFXCompliantPDFOnly false /PDFXNoTrimBoxError true /PDFXTrimBoxToMediaBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXSetBleedBoxToMediaBox true /PDFXBleedBoxToTrimBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXOutputIntentProfile (None) /PDFXOutputConditionIdentifier () /PDFXOutputCondition () /PDFXRegistryName () /PDFXTrapped /False /CreateJDFFile false /Description > /Namespace [ (Adobe) (Common) (1.0) ] /OtherNamespaces [ > /FormElements false /GenerateStructure false /IncludeBookmarks false /IncludeHyperlinks false /IncludeInteractive false /IncludeLayers false /IncludeProfiles true /MultimediaHandling /UseObjectSettings /Namespace [ (Adobe) (CreativeSuite) (2.0) ] /PDFXOutputIntentProfileSelector /NA /PreserveEditing false /UntaggedCMYKHandling /UseDocumentProfile /UntaggedRGBHandling /UseDocumentProfile /UseDocumentBleed false >> ]>> setdistillerparams> setpagedevice

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