Application of Fractional Order Differential Technology in the Estimation of Soil Moisture Content Using UAV-Based Hyperspectral Data
WANG Jin-jie1, 2, 3, 4, 5, DING Jian-li1, 4, 5*, GE Xiang-yu1, 4, 5, ZHANG Zhe1, 4, 5, HAN Li-jing1, 4, 5
1. College of Geography and Remote Sensing Science，Xinjiang University, Urumqi 830017, China
2. MOE Engineering Research Center of Desertification and Blown-Sand Control，Beijing Normal University, Beijing 100875，China
3. Key Laboratory of Digital Earth Science, Chinese Academy of Sciences, Beijing 100094, China
4. Xinjiang Key Laboratory of Oasis Ecology，Xinjiang University, Urumqi 830017, China
5. Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi 830017, China
Abstract：UAV-based remote sensing technique provides a new perspective and platform for precision agriculture and agricultural information monitoring. The hyperspectral sensor has centimeter-level spatial and fine spectral resolution, allowing for the acquisition of high-quality hyperspectral data. However, hyperspectral data often bring question on noise, data redundancy and inefficient use of hyperspectral information, whereas conventional preprocessing is difficult to estimate withhigh-precision. Therefore, data mining for UAV-based hyperspectral images is essential to solve the above problems. Here we used fractional order differential (FOD) to process UAV-based hyperspectral data (with a step length of 0.1). The optimal FOD order is explored at the spectral level by comparing the ability of the FOD technique with the integer order technique to improve the hyperspectral data. Soil moisture content (SMC) estimation models were constructed under the Gradient-Boosted Regression Tree (GBRT) algorithm, and the spatial distribution of SMC was finally evaluated under the best model. The results found that the correlation between the spectrum and SMC alsowas increased (absolute maximum correlation coefficient, rmax=0.768). Compared with the original image and processed images via first and second order derivatives, rmax increased by 0.168, 0.157 and 0.158, respectively. The main reason for the FOD technique to enhance the accuracy of model estimation is to highlight the role of effective spectral information, especially chlorophyll, plant structure and water response bands closely sensitive to drought stress. (430，460，640，660 and 970 nm). By comparison, the low-order FOD (order＜1) is more effective in the image quality, correlation and model accuracy than high-order FOD (order>1). The higher order FOD adds a certain amount of noise to the image, though the FOD technology achieves the desired result. Estimated model achieved the best results in the 0.4-order model (R2p=0.874，RMSEP=1.458，RPIQ=3.029). In addition, the SMC estimation models of 0.1—0.9 order and 1.6—1.9 order outperformed the integer-order models (R2p improvement of 0.8%～13.8%), but the lower-order FOD models were found to be stronger in terms of model predictive power based on the RPIQ of the models. The spatial distribution of inverse farmland soil moisture under the 0.4 order model indicated significant spatial heterogeneity of farmland SMC in the arid regions. In conclusion, the low-order FOD technique effectively enables the mining of hyperspectral data to accurately estimate agricultural SMC. This study proposes a new approach to airborne hyperspectral image processing that provides a new strategy for precision agriculture implementation and management in arid regions.
王瑾杰，丁建丽，葛翔宇，张 喆，韩礼敬. 分数阶微分技术在机载高光谱数据估算土壤含水量中的应用[J]. 光谱学与光谱分析, 2022, 42(11): 3559-3567.
WANG Jin-jie, DING Jian-li, GE Xiang-yu, ZHANG Zhe, HAN Li-jing. Application of Fractional Order Differential Technology in the Estimation of Soil Moisture Content Using UAV-Based Hyperspectral Data. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3559-3567.
[1] Martínez-Fernández J, González-Zamora A, Sánchez N, et al. Remote Sensing of Environment，2016, 177: 277.
[2] Sankey T T, McVay J, Swetnam T L, et al. Remote Sensing in Ecology and Conservation，2018, 4(1): 20.
[3] Yan W, Zhong Y，Shangguan Z. Agricultural and Forest Meteorology, 2017, 247: 311.
[4] Maimaitiyiming M, Ghulam A, Bozzolo A, et al. Remote Sensing，2017, 9(7): 745.
[5] Jay S, Baret F, Dutartre D, et al. Remote Sensing of Environment，2019, 231: 110898.
[6] SHI Zhou, XU Dong-yun, TENG Hong-fen, et al(史 舟, 徐冬云, 滕洪芬, 等). Progress in Geography(地理科学进展), 2018, 37(1): 79.
[7] Gago J, Douthe C, Coopman R E, et al. Agricultural Water Management, 2015, 153: 9.
[8] Liu H, Zhu H，Wang P. International Journal of Remote Sensing，2017, 38(8-10): 2117.
[9] Steidle Neto A J, Lopes D C, Pinto F A C, et al. Biosystems Engineering, 2017, 155: 124.
[10] Nawar S, Buddenbaum H, Hill J, et al. Soil and Tillage Research，2016, 155: 510.
[11] YUAN Jie, ZHANG Fei, GE Xiang-yu, et al(袁 婕, 张 飞, 葛翔宇, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2019, 35(10): 115.
[12] Wang X, Zhang F, Kung H T, et al. Ecological Indicators, 2017, 81: 222.
[13] Wei G, Li Y, Zhang Z, et al. PeerJ, 2020，(8): e9087.
[14] Wang F, Jay G, Yong Z. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 136: 73.
[15] PU Yi-fei, WANG Wei-xing, ZHOU Ji-liu, et al(蒲亦非, 王卫星, 周激流, 等). Science in China (Series E): Information Sciences(中国科学·E辑: 信息科学), 2008, 38(12): 2252.
[16] Wang X, Zhang F, Kung H T, et al. Remote Sensing of Environment, 2018, 218: 104.
[17] ZHANG Zhi-tao, LAO Cong-cong, WANG Hai-feng, et al(张智韬, 劳聪聪, 王海峰, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2020, 51(1): 156.
[18] Galvão R KH, Araujo M C U, José G E, et al. Talanta, 2005, 67(4): 736.
[19] Yang L, Zhang X, Liang S, et al. Remote Sensing, 2018, 10(2): 185.
[20] Chen Di, Chang Naijie, Xiao Jingfeng, et al. Science of the Total Environment, 2019, 669: 844.
[21] Nawar S, Abdul M M. Catena, 2017, 155: 118.
[22] Pascucci S, Casa R, Belviso C, et al. European Journal of Soil Science, 2014, 65(6): 865.
[23] GE Xiang-yu, DING Jian-li, WANG Jing-zhe, et al(葛翔宇, 丁建丽, 王敬哲, 等), Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2020, 40(2): 602.
[24] XIAO Yan-fang, ZHOU De-min, GONG Hui-li, et al(肖艳芳, 周德民, 宫辉力, 等). Journal of Remote Sensing(遥感学报), 2015, 19(3): 368.
[25] ZHU Ye-qing, QU Yong-hua, LIU Su-hong, et al(朱叶青, 屈永华, 刘素红, 等). Journal of Remote Sensing(遥感学报), 2014, 18(2): 335.
[26] Jin X, Kumar L, Li Z, et al. European Journal of Agronomy，2018, 92: 141.
