Improving the accuracy of rice fields identification using Landsat-8 Satellite Land surface temperature time series images and machine learning algorithms

Rice is an important crop and the main food of more than half of the world’s population, which needs water and heat to grow. Thus, mapping and monitoring rice fields with efficient means such as remote sensing technology is necessary for food security and the lack of water sources. The phenology extracted from the time series of vegetation indices is used for monitoring and mapping the area under rice cultivation. In addition to the phenological curve, the LST time series map, which is calculated from Landsat 8 images and is related to the phenomenon of evaporation and transpiration of irrigated crops, can cause the separation of rice cultivation from rainfed crops, summer crops, water, etc. Therefore, in this study, the effect of the LST time series map is investigated map for improving the accuracy of rice field identification.

Since the planting to harvest period of rice is from May to October, in this study, the time-series maps of LST and NDVI for the 3rd of April, 21st of May, 6th of John, 22nd of John, 8th of July, 24th of July, 9th of August, 12th of October, and 28th of October have been calculated after download the Landsat-8 time-series in 2020 The ground truth map of the study area has been obtained from the US Department of Agriculture. To identify rice fields and calculate the LST and NDVI using the Landsat-8 images, initial pre-processing including radiometric and geometric corrections has been applied to these images first. After initial corrections and the calculation of NDVI and LST maps, to identify rice fields in the study area, machine learning algorithms such as Support Vector Machine, K-Nearest Neighborhood, Multilayer Perspective, Logistic Regression, and Decision Tree, have been proposed.

The results of the proposed method at the state of California showed that using the time series map of  Land surface temperature (LST) with the time-series map of  Normalized Difference vegetation Index, improved the results of identifying rice fields (the average Overall Accuracy= + 3/572% and the average kappa coefficient= +7/112%). Visual results showed that some cultivation such as tomato, corn, cucumber, fallow, and water were removed from the rice final map when using the LST time-series map with the NDVI time-series map. According to the numerical results, the Support Vector Machine algorithm (Overall Accuracy 94/28 and Kappa Coefficient 88/29), the Multilayer Perceptron algorithm  (Overall Accuracy 94/26 and Kappa Coefficient 88/21), and the K-Nearest Neighborhood algorithm (Overall Accuracy 93/71 and Kappa Coefficient 87/08) showed the highest Overall Accuracy and Kappa Coefficient compared to the Logistic Regression algorithm (overall accuracy 91/96 and kappa coefficient 83/54) and the Decision Tree algorithm (Overall Accuracy 91/34 and Kappa Coefficient 81/97), respectively.

Although, many methods have been proposed to identify rice fields from satellite images. But, the similarity of rice class with other classes is one of the main challenges related to rice identification. In this research, the effect of LST time series maps to improve the identification accuracy of rice fields in Landsat-8 time-series images was investigated. In this study, the effect of the time series map of land surface temperature index extracted from Landsat-8 images on improving the accuracy of identifying rice fields from other rice fields due to the evapotranspiration process using machine learning algorithms was investigated. The results showed the effectiveness of the proposed index in improving the identification accuracy of rice fields. One of the reasons for improving the accuracy of identifying rice fields is to extract the characteristics of the thermal growing season from the Earth's surface temperature time series (LST) maps along with the rice phenology curve. The results showed that due to the flooding of rice fields when using the NDVI time series map, water class and fields summer crops were identified as rice class. But, water and summer crops classes were removed from the rice final map using a land surface temperature time-series map with the extraction of thermal growth season characteristics. Therefore, the results showed that there was a direct relationship between LST time-series maps and rice cultivation.