干旱区生态环境知识资源中心(Arid): Optical remote sensing of crop biophysical and biochemical parameters: An overview of advances in sensor technologies and machine learning algorithms for precision agriculture

Arid

DOI	10.1016/j.compag.2024.108730
	Optical remote sensing of crop biophysical and biochemical parameters: An overview of advances in sensor technologies and machine learning algorithms for precision agriculture
	Kganyago, Mahlatse; Adjorlolo, Clement; Mhangara, Paidamwoyo; Tsoeleng, Lesiba
通讯作者	Kganyago, M
来源期刊	COMPUTERS AND ELECTRONICS IN AGRICULTURE
ISSN	0168-1699
EISSN	1872-7107
出版年	2024
卷号	218
英文摘要	This paper provides an overview of the recent developments in remote sensing technology and machine learning algorithms for estimating important biophysical and biochemical parameters for precision farming. The objectives are (i) to provide an overview of recent advances in remotely sensed retrieval of biophysical and biochemical parameters brought by the developments in sensor technologies and robust machine learning algorithms and (ii) to identify the sources of uncertainty in retrieving biophysical and biochemical parameters and implications for precision agriculture. The review revealed that developments in crop biophysical and biochemical parameters retrieval techniques were mainly driven by announcements and the availability of new sensors. Two ground -breaking events can be identified, i.e., the availability of Sentinel -2 and the SuperDove constellation. The two provide high temporal -high spatial resolution data relevant for site -specific management and super -spectral configuration, enabling retrieval of crop growth and health parameters. The free availability of Sentinel -2 triggered the testing of its spectral configurations and upscaling of retrieval approaches using simulated data from field spectrometers and airborne hyperspectral sensors. SuperDoves will likely reduce the cost of very high -resolution data while providing unprecedented capabilities for detailed, accurate and frequent characterisation of field variability. Studies showed that the red -edge bands and hybrid models coupling Radiative Transfer Model (RTM) and machine learning regression algorithms (MLRA) are promising for operational and accurate monitoring of stress -related crop parameters to aid time -sensitive agronomic decisions. However, such models were tested in Mediterranean climates and performed poorly in African semi -arid areas and China's temperate continental semi -humid monsoon climates. Therefore, locally -calibrated RTM models incorporating crop -type maps and other spatio-temporal constraints may reduce uncertainties when adapted to data -scarce regions. Generally, permanent experimental sites and a lack of systematic calibration data on various crops are some limiting factors to using remote sensing technologies for PA in Sub-Saharan Africa. Other complexities arise from farm configurations, such as small field sizes and mixed cropping practices. Therefore, future studies should develop generic, scalable and transferable models, especially within under -studied areas.
英文关键词	Remote sensing Machine learning Precision agriculture Leaf area index Chlorophyll content
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:001184745600001
WOS关键词	LEAF-AREA INDEX ; HYPERSPECTRAL VEGETATION INDEXES ; CANOPY CHLOROPHYLL CONTENT ; RED-EDGE BANDS ; SUGAR-BEET ; NITROGEN CONCENTRATION ; ATMOSPHERIC CORRECTION ; LAI PRODUCTS ; GREEN LAI ; SENTINEL-2
WOS类目	Agriculture, Multidisciplinary ; Computer Science, Interdisciplinary Applications
WOS研究方向	Agriculture ; Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403249
推荐引用方式 GB/T 7714	Kganyago, Mahlatse,Adjorlolo, Clement,Mhangara, Paidamwoyo,et al. Optical remote sensing of crop biophysical and biochemical parameters: An overview of advances in sensor technologies and machine learning algorithms for precision agriculture[J],2024,218.
APA	Kganyago, Mahlatse,Adjorlolo, Clement,Mhangara, Paidamwoyo,&Tsoeleng, Lesiba.(2024).Optical remote sensing of crop biophysical and biochemical parameters: An overview of advances in sensor technologies and machine learning algorithms for precision agriculture.COMPUTERS AND ELECTRONICS IN AGRICULTURE,218.
MLA	Kganyago, Mahlatse,et al."Optical remote sensing of crop biophysical and biochemical parameters: An overview of advances in sensor technologies and machine learning algorithms for precision agriculture".COMPUTERS AND ELECTRONICS IN AGRICULTURE 218(2024).