N. Loveimi; A. Akram; N. Bagheri; A. Hajiahmad
Abstract
Introduction Remote sensing and using satellite images have been widely considered due to the high speed of measurement and great area of coverage. Canola is a source of edible oil and its cultivation in Iran and the world is developing. Comparing with other crops, canola, because of its yellow flowers, ...
Read More
Introduction Remote sensing and using satellite images have been widely considered due to the high speed of measurement and great area of coverage. Canola is a source of edible oil and its cultivation in Iran and the world is developing. Comparing with other crops, canola, because of its yellow flowers, has a different canopy color, and only a few researches have been carried out in order to assess the spectral indices for prediction of its yield. Therefore, the main objective of this research is to evaluate some spectral vegetation indices to estimate the yield of canola in different growth stages. Materials and Methods The study was performed in 2016-2017 in Karaj, Iran. Three canola farms were chosen for the evaluation of the relationship between yield and some vegetation indices derived from the Sentinel-2 sensor. The sensor data were processed in five stages: before flowering, early flowering, peak of flowering, green and dry maturity, and the vegetation indices were extracted for each of them. This research was pixel-based and the pixels network of each studied farm was determined by RTKGPS. During harvesting time, for measurement of grain yield, five samples, four from the corners and one from the center of the pixel, were taken and their average was considered as the representative amount of the pixel. Totally, 112 pixels from three studied farms were used to calibrate the predictive models. By using Simple Linear Regression (SLR) models, ten new and conventional vegetation indices were assessed. Also, Multivariate Linear Regression (MLR) models and Artificial Neural Net (ANN) models with four bands, three visible bands and NIR band, as inputs, were used to predict the canola yield. In order to validate the SLR and MLR models, the "K-Fold" method of cross-validation was used, and for the validation of ANN models, 15% of data were used; 70% for the train, 15% for validation, and 15% for the test. Results and Discussion The results showed that, on the basis of SLR models, among the growth stages, the highest coefficient of determination (R2) in each of the vegetation indices belonged to one of the two stages: the peak of flowering and green maturity. According to SLR models, among the vegetation indices in different stages, the NDYI in the peak of the flowering stage had the highest correlation with yield (R2 = 73%). Also, the RVI with 29%, BNDVI with 52%, NDVI with 56%, and GNDVI with 35% had the highest R2 in the before flowering, early flowering, peak of flowering, green and dry maturity stages, respectively. MLR models resulted to the best yield predictive model at the peak of flowering stage (R2 = 76% for the calibration and R2 = 73% and RMSE = 0.641 for the validation). For ANN models, the strongest model achieved at peak of flowering stage (R2 = 92% for the calibration (train) and R2 = 77% and RMSE = 0.612 for the validation (test)). It seems that the results are affected by yellow flowers of canola, and absorption of blue light by their petals. Therefore, in the peak of the flowering stage, the reflection of the blue light is more likely to belong to green leaves and stems. Therefore, any index such as NDYI, which the blue reflection is subtracted in its equation, represents better the number of flowers, and since the density of flowers is directly related to the yield, the yield will be predicted with more precision. Conclusion The results of the analysis of the indices by SLR models showed that the correlation of each of the vegetation indices with the canola yield, in different stages of growth, has a considerable difference. Based on this model, the highest R2 in each of these indices happened in the peak of flowering or green maturity stage, and among these indices in different stages, the NDYI in the peak of the flowering stage had the highest R2. Finally, in both of the MLR and ANN models, with four bands, three visible bands and near-infrared band, as inputs, the best yield predictive model resulted in the peak of the flowering stage.
F. Alipour; M. H. Aghkhani; M. H. Abbaspour-Fard; A. Sepehr
Abstract
Application of satellite imagery and remote sensing techniques in agriculture and other natural resources has been approved by many studies. In this study two ETM+ imagery data for May and September 2012 of Astan Ghods Razavi Great Farm were acquired to identify the boundaries of lands cultivated with ...
Read More
Application of satellite imagery and remote sensing techniques in agriculture and other natural resources has been approved by many studies. In this study two ETM+ imagery data for May and September 2012 of Astan Ghods Razavi Great Farm were acquired to identify the boundaries of lands cultivated with different crops coverage and to create crops maps of that farm. . To classify the images, the supervised classification methods including Maximum Likelihood and artificial neural network were used. In order to compare the results of two applied classification methods, the same training and testing samples were used. To evaluate the accuracy of classification results, the produced map was compared with the ground control points extracted by GPS and local observations. Kappa coefficient and overall accuracy were estimated to be 82% and 85%, respectively by maximum likelihood method and these outputs were estimated to be 84% and 87%, respectively by neural network approach. The difference of cultivated area estimated by maximum likelihood and by neural network methods with actual measured area was 16.8% and 14.2%, respectively. The results of this study showed that satellite imagery has high capabilities to classify and estimate agricultural and cultivated areas. These data can be useful for strategic management to develop mechanization and cultivation plans.
