干旱区生态环境知识资源中心(Arid): Evaluating the contribution of Sentinel-2 view and illumination geometry to the accuracy of retrieving essential crop parameters

Arid

DOI	10.1080/15481603.2022.2163046
	Evaluating the contribution of Sentinel-2 view and illumination geometry to the accuracy of retrieving essential crop parameters
	Kganyago, Mahlatse; Ovakoglou, Georgios; Mhangara, Paidamwoyo; Adjorlolo, Clement; Alexandridis, Thomas; Laneve, Giovanni; Beltran, Juan Suarez
通讯作者	Kganyago, M
来源期刊	GISCIENCE & REMOTE SENSING
ISSN	1548-1603
EISSN	1943-7226
出版年	2023
卷号	60 期号:1
英文摘要	Wide field-of-view (FOV) sensors such as Sentinel-2 exhibit per-pixel view and illumination geometry variation that may influence the retrieval accuracy of essential crop biophysical and biochemical variables (BVs) for precision agriculture. However, this aspect is rarely studied in the existing literature. Hence, the current study aimed to evaluate the contribution of view and illumination geometries to the accuracy of retrieving Leaf Chlorophyll a and b (LC ab ), Canopy Chlorophyll Content (CCC), and Leaf Area Index (LAI) using the Random Forest (RF). The experiments were performed on various input variable scenarios where per-pixel geometric covariates, i.e. View and Sun Zenith Angles (VZA and SZA, respectively), and Relative Azimuth Angle (RAA), are excluded and included in spectral bands (SB) and spectral vegetation indices (SVIs), respectively, in two semi-arid areas. The results showed that spectral bands or vegetation indices combined with geometric covariates improved the R (2) by 10-15% for LAI and 3-5% for CCC. In contrast, negligible improvements of 1-2% were achieved for LC ab with cross-validation test data and independent held-out dataset, respectively. In agreement with previous studies, VZA and SZA were among the topmost influential variables in the RF models for estimating LAI, LC ab , and CCC. Collectively, per-pixel geometric variables explained more than 30% of the variability in surface reflectance for all Sentinel-2 spectral bands (p < 2.2e-16). Overall, the results showed that incorporating geometric covariates improved the accuracy of retrieving BVs; thus, it provided additional information that improves the predictive power of SB and SVIs. The significant benefits of the geometric variables were mainly realized for canopy-level BVs (i.e. LAI and CCC) than for LC ab . Therefore, it is recommended to incorporate per-pixel view and illumination geometry in estimating LAI and CCC, especially when using wide-view sensors such as Sentinel-2. However, further testing in different phenology, site and acquisition conditions is needed to confirm the contribution of the geometric covariates to facilitate reliable retrieval of BVs from remotely sensed data and aid better agronomic decisions.
英文关键词	Chlorophyll Content Leaf Area Index Sentinel-2 Random Forest red-edge vegetation indices sun-sensor geometry
类型	Article
语种	英语
开放获取类型	gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:000906920200001
WOS关键词	LEAF-AREA INDEX ; CANOPY CHLOROPHYLL CONTENT ; RED-EDGE BANDS ; VEGETATION INDEXES ; GREEN LAI ; BIOPHYSICAL VARIABLES ; REMOTE ESTIMATION ; SPECTRAL INDEX ; BIDIRECTIONAL REFLECTANCE ; BIOCHEMICAL VARIABLES
WOS类目	Geography, Physical ; Remote Sensing
WOS研究方向	Physical Geography ; Remote Sensing
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396757
推荐引用方式 GB/T 7714	Kganyago, Mahlatse,Ovakoglou, Georgios,Mhangara, Paidamwoyo,et al. Evaluating the contribution of Sentinel-2 view and illumination geometry to the accuracy of retrieving essential crop parameters[J],2023,60(1).
APA	Kganyago, Mahlatse.,Ovakoglou, Georgios.,Mhangara, Paidamwoyo.,Adjorlolo, Clement.,Alexandridis, Thomas.,...&Beltran, Juan Suarez.(2023).Evaluating the contribution of Sentinel-2 view and illumination geometry to the accuracy of retrieving essential crop parameters.GISCIENCE & REMOTE SENSING,60(1).
MLA	Kganyago, Mahlatse,et al."Evaluating the contribution of Sentinel-2 view and illumination geometry to the accuracy of retrieving essential crop parameters".GISCIENCE & REMOTE SENSING 60.1(2023).