Title
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.
Year
DOI
Venue
2020
10.3390/rs12193202
REMOTE SENSING
Keywords
DocType
Volume
mLSTI,NDVI,time series,crop classification,Landsat
Journal
12
Issue
Citations 
PageRank 
19
0
0.34
References 
Authors
0
9
Name
Order
Citations
PageRank
Xinran Chen101.35
Yulin Zhan201.35
Yan Liu319730.85
Xingfa Gu45436.00
Tao Yu52715.23
Dakang Wang601.01
Qixin Liu701.35
Yin Zhang800.68
Yunzhou Zhang900.34