Improving the Classification Accuracy of Annual Crops Using Time Series of Temperature and Vegetation Indices

Abstract

Accurate cropland classification is important for agricultural monitoring and related decision-making. The commonly used input spectral features for classification cannot be employed to effectively distinguish crops that have similar spectro-temporal features. This study attempted to improve the classification accuracy of crops using both the thermal feature, i.e., the land surface temperature (LST), and the spectral feature, i.e., the normalized difference vegetation index (NDVI), for classification. To amplify the temperature differences between the crops, a temperature index, namely, the modified land surface temperature index (mLSTI) was built using the LST. The mLSTI was calculated by subtracting the average LST of an image from the LST of each pixel. To study the adaptability of the proposed method to different areas, three study areas were selected. A comparison of the classification results obtained using the NDVI time series and NDVI + mLSTI time series showed that for long time series from June to November, the classification accuracy when using the mLSTI and NDVI time series was higher (85.6% for study area 1 in California, 96.3% for area 2 in Kansas, and 91.2% for area 3 in Texas) than that when using the NDVI time series alone (82.0% for area 1, 94.7% for area 2, and 90.9% for area 3); the same was true in most of the cases when using the shorter time series. With the addition of the mLSTI time series, the shorter time series achieved higher classification accuracy, which is beneficial for timely crop identification. The sorghum and soybean crops, which exhibit similar NDVI feature curves in this study, could be better distinguished by adding the mLSTI time series. The results demonstrated that the classification accuracy of crops can be improved by adding mLSTI long time series, particularly for distinguishing crops with similar NDVI characteristics in a given study area.

Improvement in the Accuracy of the Postclassification of Land Use and Land Cover Using Landsat 8 Data Based on the Majority of Segment-Based Filtering Approach

“…In this study, we have improved the accuracy of the LULC classification based on the mosaic cloud-free Landsat 8 satellite imagery that can be obtained from GEE, and its popular method for filling gaps in cloudy images using median metrics or the temporal aggregation method [ 36 ]. ML, RF, and SVM, which have been widely used for image classification [ 36 , 39 , 40 , 42 , 43 ], were employed as methods to classify LULC in the study area. We chose the Citarum, Ciliwung, and Cisadane watersheds as test case study areas to be able to understand and implement the proposed method; these areas are included in the 15 national priority watersheds in Indonesia.…”

“…Accuracy assessment was made to evaluate the results of the digital classifications generated from the ML, RF, and SVM classifiers, together with the optimisation results from using the MaSegFil approach for the postclassification stage as a spatial filter, as proposed in this study. A confusion matrix was used to evaluate the accuracy assessment procedure, which took into account user accuracy, producer accuracy, kappa, and overall accuracy [ 36 , 42 , 45 – 47 ].…”