Comparison of Normalized Difference Vegetation Index (NDVI) of Potato from Greenseeker and Landsat Satellite
Journal Title: Journal of Agricultural Machinery - Year 2018, Vol 8, Issue 2
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.
Authors and Affiliations
H. Mohamadi-Monavar,
Keywords
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