Comparison of Normalized Difference Vegetation Index (NDVI) of Potato from Greenseeker and Landsat Satellite

Document Type : Research Article

Author

• H. Mohamadi-Monavar

Mechanics of Biosystem Engineering, Agriculture Department, Buali Sina University, Hamedan, Iran

https://doi.org/10.22067/jam.v8i2.64486

Abstract

Introduction
Field management is a part of precision agriculture (PA) which has positive environmental and economic effects on quality of plant productions. Nitrogen needs of plant, depends on climate conditions and growing pattern. The optimum of nitrogen fertilizer is varied from fields to fields. Nitrogen management causes uniform shape and size of potatoes, on the other hand decreases the inward and outward damages (Stark and Brown, 2003). Between different herbal indices, NDVI is the most common for monitoring greenness of plants. NDVI was calculated from reflectance in red and NIR bands (equation 1). Greenseeker (GS) is a suitable optical sensor because it is not affected by light and temperature variation or wind intensity.
(1)
In addition to GS, satellite image was used to evaluate the NDVI of studied potato field. Landsat 8 is the last satellite of this family with new sensors (operational land imager (OLI) and thermal infrared sensor (TIRs)) and additional spectral bands (deep blue invisible (430-450 nm) and shortwave infrared (1360-1390 nm). At the end, support vector regression (SVR) and principal component regression (PCR) or multi-linear regression (MLR) was applied to estimate RMSE and R². The input of models was synoptic data, and NDVI extracted from GS or OLI.

Materials and Methods
The study was performed on marfona cultivar of potato field which located in Bahar city, Hamadan. The potato was planted early March and experiments were started after growing the first leaves. The soil texture in the experimented field was sandy loam soil to 75 cm depth. The territory (the southwest corner of the field) was fertigated by poultry manure with content 4.5% of N in order to put shortage of nitrogen down. Metrology station of Bahar city reported the maximum, minimum and average temperature, relative humidity, precipitation and wind velocity which were effective on NDVI variation. The GS was put at a height of 60 cm above the plant and the average of NDVI was obtained by three times measurement. This sensor has red and NIR diodes which reflect and absorb the spectra in 660±15nm and 770±15nm regions, respectively. GS and OLI were applied for measurement every 8 and 16 days, respectively. Satellite images were analyzed two times (30cm height of plant and hilling stage) during the growing. Although, climate changing were effective on NDVI then some image corrections were necessary. Geometric and atmospheric corrections were applied for removing the absorption and distribution error with dark object subtraction and FLAASH algorithm in ENVI 5.3 Software. In addition, GS is a nondestructive and contactless optic sensor which helps farmers to manage nitrogen because using laboratory method is not easy way for them. As well as, OLI provided accurate NDVI which support the accuracy of GS.

Results and Discussion
In order to correlate NDVI-GS and NDVI-OLI, the third parameter (INSEY) was explained. In season estimation of yield (INSEY) was estimated by dividing NDVI by days after planting (DAP). INSEY index is suitable to predict product potential performance. PCR and SVR methods in Matlab 2011b was used to calculated the relationship of INSEY and NDVI. Also, Red and NIR bands extracted from spectrometer (AvaSpec-ULS 2048- UV-VIS) in the 300-1100 nm region were used in order to support comparison of those sensors. Results showed that the reflectance spectra changed through the growing stage, which is logic because the size and number of leaves were increased and as a result the greenness was enhanced. NDVI calculated with spectra showed more accurate R2 for NDVI-GS (0.94) than NDVI-OLI (0.81). In addition, correlation coefficients of the SVR model between INSEY and NDVI were predicted 0.947 and 0.947 for the GS and OLI, respectively.

Conclusion
The result of the study confirmed the useful Greanseeker as an accurate and fast technology for prediction of NDVI. Among different regression methods, SVR showed the perfect results. Since the farm is a commercial one and not belong to the university, it would not possible to test different nitrogen fertilizer treatments. It is obvious that evaluation of field in different consecutive years helps us to codify manual fertilization.

Keywords

• Greenseeker
• Normalized Difference Vegetation Index
• Sattelite images

20.1001.1.22286829.1397.8.2.7.1

References

1. Brereton, R. G., and G. R. Lloyd. 2010. Support Vector Machines for classification and regression. Analyst Journal 135: 230-267.

2. Chavez, P. S. 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote sensing of environment 24 (3): 459-479.

3. Cheret, V., and J. Denux. 2011. Analysis of MODIS NDVI time series to calculate indicators of Mediterranean forest fire susceptibility. GIScience and Remote Sensing 48: 171-194.

4. Deng, F., J. M. Chen, S. Plummer, M. Chen, and J. Pisek. 2006. Algorithm for global leaf area index retrieval using satellite imagery. IEEE Transactions on Geoscience and Remote Sensing 44 (8): 2219.

5. Durbha, S. S., R. L. King, and N. H. Younan. 2007. Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer. Remote Sensing of Environment 107 (1-2): 348-361.

6. Feng, M., C. Huang, S. Channan, E. F. Vermote, J. G. Masek, and J. R. Townshend. 2012. Quality assessment of Landsat surface reflectance products using MODIS data. Computers & Geosciences 38 (1): 9-22.

7. Gavier-Pizarroa, G. I., T. Kuemmerle, L. E. Hoyos, S. I. Stewart, C. D. Huebner, N. S. Keuler, and V. C. Radelof. 2012. Monitoring the invasion of an exotic tree (Ligustrum lucidum) from 1983 to 2006 with Landsat TM/ETM + satellite data and Support Vector Machines in Cordoba, Argentina. Remote Sensing of Environment 122: 134-145.

8. Hadjimitsis, D. G., G. Papadavid, A. Agapiou, K. Themistocleous, M. G. Hadjimitsis, and A. Retalis. 2010. Atmospheric correction for satellite remotely sensed data intended for agricultural applications: Impact on vegetation indices. Natural Hazards and Earth System Sciences 10 (1): 89-95.

9. Hong, S. H., J. M. Hendrickx, and B. Borchers. 2009. Up-scaling of SEBAL derived evapotranspiration maps from Landsat (30 m) to MODIS (250 m) scale. Hydrology 370 (1): 122-138.

10. Im, J., Z. Lu, J. Rhee, and L. J. Quackenbush. 2012. Impervious surface quantification using a synthesis of artificial immune networks and decision/regression trees from multisensor data. Remote Sensing of Environment 117: 102-113.

11. Ke, Y., J. Im, J. Lee, H. Gong, and Y. Ryu. 2015. Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observations, Remote Sensing of Environment 164: 298-313.

12. Kitchen, N. R., K. A. Sudduth, S. T. Drummond, P. C. Scharf, H. L. Palm, D. F. Roberts, and E. D. Vories. 2010. Ground-based canopy reflectance sensing for variable-rate nitrogen corn fertilization. Agronomy 102: 71-84.

13. Li, P., L. Jiang, and Z. Feng. 2014. Cross-comparison of vegetation indices derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) sensors. Remote Sensing 6: 310-329.

14. Maiersperger, T. K., P. L. Scaramuzza, L. Leigh, S. Shrestha, K. P. Gallo, and C. B. Jenkerson 2013. Characterizing LEDAPS surface reflectance products by comparisons with AERONET, field spectrometer, and MODIS data. Remote Sensing of Environment 136: 1-13.

15. Meng, Q., W. Cooke, and J. Rodgers. 2013. Derivation of 16-day time-series NDVI data for environmental studies using a data assimilation approach. GIScience and Remote Sensing 50: 500-514.

16. MohammadZamani, D., A. Taghavi, M. Gholami-parshokouhi, and J. Massah. 2014. Design, implementation and evaluation of a monitoring system of potato yield. Journal of Agricultural Machinery 4 (1): 50-56. (In Farsi).

17. Olfs, H. W. 2009. Improved precision of arable nitrogen applications: requirements, technologies and implementation. The International FertiliserSociety, Proceeding 662: 35.

18. Pastor-Guzman, J., P. M. Atkinson, J. Dash, and R. Rioja-Nieto. 2015. Spatotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8. Remote Sensing 7: 14530-14558.

19. Raun, W. R., J. B. Solie, G. V. Johnson, M. L. Stone, R. W. Mullen, K. W. Freeman, W. E. Thomason, and E.V. Lukina. 2002. Improving nitrogen use efficiency in cerealgrain production with optical sensing and variable rate application. Agronomy 94: 815-820.

20. Roberts, D. F., V. I. Adamchuk, J. F. Shanahan, R. B. Ferguson, and J. S. Schepers. 2009. Optimization of crop canopy sensor placement for measuring nitrogen statusin corn. Agronomy 101: 140-149.

21. Rostami, M. A., M. H. Raofat, A. Jafari, M. Loghavi, M. Kasraee, and M. J. Nazem-Alsadat. 2014. Monitoring of protection tillage and intensity of tillage using satellite and terrestrial images. Journal of Agricultural Machinery 4 (2): 255-265. (In Farsi).

22. Samborski, M., D. Gozdowski, M. Stepie´na, O. S. Walshc, and E. Leszczy´nska. 2016. On-farm evaluation of an active optical sensor performance forvariable nitrogen application in winter wheatStanisław. Agronomy 74: 56-67.

23. Schepers, J. S., and J. F. Shanahan. 2009. Managing nitrogen with active sensors. 13th Annual Symposium on Precision Agriculture in Australia.

24. Sharma, L. K., H. Bu, A. Denton, and D. W. Franzen. 2015. Active-Optical Sensors Using Red NDVI Compared to Red Edge NDVI for Prediction of Corn Grain Yield in North Dakota USA. Sensors 15: 27832-27853.

25. Stark, J. C., and B. D. Brown. 2003. Nutrient Management. In L.D. Robertson and J.C. Stark (Eds) Idaho Spring Barley Production Guide. pp. 22-26.

26. Teillet, P., and X. Ren. 2008. Spectral band difference effects on vegetation infices derived from multiple satellite sensor data. Canadian Journal of Remote Sensing 34: 159-173.

27. Tremblay, N., E. Fallon, and N. Ziadi. 2011. Sensing of crop nitrogen status: Opportunities, tools, limitations, and supporting information requirements. Horticulture Technology 21: 274-281.

28. Yuan, F., C. Wang, and M. Mitchell. 2014. Spatial patterns of land surface phenology relative to monthly climate variations: US Great Plains. GIScience and Remote Sensing 51: 30-50.