Validation of atmospheric correction approaches for Sentinel-2 under partly-cloudy conditions in an African agricultural landscape

Mahlatse Kganyago; Georgios Ovakoglou; Paidamwoyo Mhangara; Thomas Alexandridis; John Odindi; Clement Adjorlolo; Nosiseko Mashiyi

doi:10.1117/12.2572293

Validation of atmospheric correction approaches for Sentinel-2 under partly-cloudy conditions in an African agricultural landscape

Mahlatse Kganyago
, Georgios Ovakoglou
, Paidamwoyo Mhangara
, Thomas Alexandridis
, John Odindi
, Clement Adjorlolo
, Nosiseko Mashiyi

South African National Space Agency
University of the Witwatersrand
Aristotle University of Thessaloniki
University of KwaZulu-Natal
African Union Development Agency

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Citations (Scopus)

Abstract

Globally, remotely sensed agricultural monitoring is impeded by cloudy conditions which render the acquired images useless. In semi-arid landscapes, rainfed croplands dominate agricultural production; thus, the majority of planting occurs in the rainy season, characterized by erratic cloud cover. The clear-sky pixels in partly-cloudy images can be used to increase the number of useful observations for quantitative analysis. This is achieved by cloud screening and atmospheric correction (AC) processes. However, the effectiveness of various AC approaches under partly-cloudy conditions is still unknown. Many studies validate surface reflectance (SR) under clear-sky conditions, with only a few focusing on the validation of SR under partly-cloudy conditions. This study sought to validate Sentinel-2 SR products derived from various AC approaches, i.e., MAJA, Sen2Cor, iCor, and FORCE, using in-situ spectral measurements. A partly-cloudy image with >60% cloud cover, acquired over a semi-arid agricultural landscape and ±3 days of the field measurements, was used as a test case. The results showed R² of <0.2 with in-situ SR in the VIS and SWIR, and R² of ~0.6 in the Red-edge and NIR regions, attributable to low aerosol scattering effect in the NIR. However, RMSE, MAE, and BIAS error metrics showed consistently higher errors across all AC approaches and crop types, with varying magnitude per spectral band. This finding can be attributed to the high presence of clouds in the image, which enhanced the apparent path radiance, causing an overestimation of the aerosol optical thickness, hence an underestimation of SR. The need for further validation of SR under various cloud conditions emanates from this study, to ascertain the utility of AC approaches under such conditions. Overall, the results have implications for the utility of remotely sensed images for agricultural crop monitoring in partly-cloudy conditions.

Original language	English
Title of host publication	Remote Sensing of Clouds and the Atmosphere XXV
Editors	Adolfo Comeron, Evgueni I. Kassianov, Klaus Schafer, Richard H. Picard, Konradin Weber, Upendra N. Singh
Publisher	SPIE
ISBN (Electronic)	9781510638754
DOIs	https://doi.org/10.1117/12.2572293
Publication status	Published - 2020
Externally published	Yes
Event	Remote Sensing of Clouds and the Atmosphere XXV 2020 - Virtual, Online, United Kingdom Duration: 21 Sept 2020 → 25 Sept 2020

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	11531
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	Remote Sensing of Clouds and the Atmosphere XXV 2020
Country/Territory	United Kingdom
City	Virtual, Online
Period	21/09/20 → 25/09/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 2 Zero Hunger

Keywords

Analysis-Ready-Data
Atmospheric correction
FORCE
ICor
MAJA
Sen2Cor
Sentinel-2
Surface Reflectance

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Instrumentation
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

Access to Document

10.1117/12.2572293

Cite this

Kganyago, M., Ovakoglou, G., Mhangara, P., Alexandridis, T., Odindi, J., Adjorlolo, C., & Mashiyi, N. (2020). Validation of atmospheric correction approaches for Sentinel-2 under partly-cloudy conditions in an African agricultural landscape. In A. Comeron, E. I. Kassianov, K. Schafer, R. H. Picard, K. Weber, & U. N. Singh (Eds.), Remote Sensing of Clouds and the Atmosphere XXV Article 115310B (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11531). SPIE. https://doi.org/10.1117/12.2572293

Kganyago, Mahlatse ; Ovakoglou, Georgios ; Mhangara, Paidamwoyo et al. / Validation of atmospheric correction approaches for Sentinel-2 under partly-cloudy conditions in an African agricultural landscape. Remote Sensing of Clouds and the Atmosphere XXV. editor / Adolfo Comeron ; Evgueni I. Kassianov ; Klaus Schafer ; Richard H. Picard ; Konradin Weber ; Upendra N. Singh. SPIE, 2020. (Proceedings of SPIE - The International Society for Optical Engineering).

@inproceedings{7ce575f79fd64024860e8f17a06f25e7,

title = "Validation of atmospheric correction approaches for Sentinel-2 under partly-cloudy conditions in an African agricultural landscape",

abstract = "Globally, remotely sensed agricultural monitoring is impeded by cloudy conditions which render the acquired images useless. In semi-arid landscapes, rainfed croplands dominate agricultural production; thus, the majority of planting occurs in the rainy season, characterized by erratic cloud cover. The clear-sky pixels in partly-cloudy images can be used to increase the number of useful observations for quantitative analysis. This is achieved by cloud screening and atmospheric correction (AC) processes. However, the effectiveness of various AC approaches under partly-cloudy conditions is still unknown. Many studies validate surface reflectance (SR) under clear-sky conditions, with only a few focusing on the validation of SR under partly-cloudy conditions. This study sought to validate Sentinel-2 SR products derived from various AC approaches, i.e., MAJA, Sen2Cor, iCor, and FORCE, using in-situ spectral measurements. A partly-cloudy image with >60\% cloud cover, acquired over a semi-arid agricultural landscape and ±3 days of the field measurements, was used as a test case. The results showed R2 of <0.2 with in-situ SR in the VIS and SWIR, and R2 of \textasciitilde{}0.6 in the Red-edge and NIR regions, attributable to low aerosol scattering effect in the NIR. However, RMSE, MAE, and BIAS error metrics showed consistently higher errors across all AC approaches and crop types, with varying magnitude per spectral band. This finding can be attributed to the high presence of clouds in the image, which enhanced the apparent path radiance, causing an overestimation of the aerosol optical thickness, hence an underestimation of SR. The need for further validation of SR under various cloud conditions emanates from this study, to ascertain the utility of AC approaches under such conditions. Overall, the results have implications for the utility of remotely sensed images for agricultural crop monitoring in partly-cloudy conditions.",

keywords = "Analysis-Ready-Data, Atmospheric correction, FORCE, ICor, MAJA, Sen2Cor, Sentinel-2, Surface Reflectance",

author = "Mahlatse Kganyago and Georgios Ovakoglou and Paidamwoyo Mhangara and Thomas Alexandridis and John Odindi and Clement Adjorlolo and Nosiseko Mashiyi",

note = "Publisher Copyright: {\textcopyright} 2020 SPIE.; Remote Sensing of Clouds and the Atmosphere XXV 2020 ; Conference date: 21-09-2020 Through 25-09-2020",

year = "2020",

doi = "10.1117/12.2572293",

language = "English",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

publisher = "SPIE",

editor = "Adolfo Comeron and Kassianov, \{Evgueni I.\} and Klaus Schafer and Picard, \{Richard H.\} and Konradin Weber and Singh, \{Upendra N.\}",

booktitle = "Remote Sensing of Clouds and the Atmosphere XXV",

address = "United States",

}

Kganyago, M, Ovakoglou, G, Mhangara, P, Alexandridis, T, Odindi, J, Adjorlolo, C & Mashiyi, N 2020, Validation of atmospheric correction approaches for Sentinel-2 under partly-cloudy conditions in an African agricultural landscape. in A Comeron, EI Kassianov, K Schafer, RH Picard, K Weber & UN Singh (eds), Remote Sensing of Clouds and the Atmosphere XXV., 115310B, Proceedings of SPIE - The International Society for Optical Engineering, vol. 11531, SPIE, Remote Sensing of Clouds and the Atmosphere XXV 2020, Virtual, Online, United Kingdom, 21/09/20. https://doi.org/10.1117/12.2572293

Validation of atmospheric correction approaches for Sentinel-2 under partly-cloudy conditions in an African agricultural landscape. / Kganyago, Mahlatse; Ovakoglou, Georgios; Mhangara, Paidamwoyo et al.
Remote Sensing of Clouds and the Atmosphere XXV. ed. / Adolfo Comeron; Evgueni I. Kassianov; Klaus Schafer; Richard H. Picard; Konradin Weber; Upendra N. Singh. SPIE, 2020. 115310B (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11531).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Validation of atmospheric correction approaches for Sentinel-2 under partly-cloudy conditions in an African agricultural landscape

AU - Kganyago, Mahlatse

AU - Ovakoglou, Georgios

AU - Mhangara, Paidamwoyo

AU - Alexandridis, Thomas

AU - Odindi, John

AU - Adjorlolo, Clement

AU - Mashiyi, Nosiseko

PY - 2020

Y1 - 2020

N2 - Globally, remotely sensed agricultural monitoring is impeded by cloudy conditions which render the acquired images useless. In semi-arid landscapes, rainfed croplands dominate agricultural production; thus, the majority of planting occurs in the rainy season, characterized by erratic cloud cover. The clear-sky pixels in partly-cloudy images can be used to increase the number of useful observations for quantitative analysis. This is achieved by cloud screening and atmospheric correction (AC) processes. However, the effectiveness of various AC approaches under partly-cloudy conditions is still unknown. Many studies validate surface reflectance (SR) under clear-sky conditions, with only a few focusing on the validation of SR under partly-cloudy conditions. This study sought to validate Sentinel-2 SR products derived from various AC approaches, i.e., MAJA, Sen2Cor, iCor, and FORCE, using in-situ spectral measurements. A partly-cloudy image with >60% cloud cover, acquired over a semi-arid agricultural landscape and ±3 days of the field measurements, was used as a test case. The results showed R2 of <0.2 with in-situ SR in the VIS and SWIR, and R2 of ~0.6 in the Red-edge and NIR regions, attributable to low aerosol scattering effect in the NIR. However, RMSE, MAE, and BIAS error metrics showed consistently higher errors across all AC approaches and crop types, with varying magnitude per spectral band. This finding can be attributed to the high presence of clouds in the image, which enhanced the apparent path radiance, causing an overestimation of the aerosol optical thickness, hence an underestimation of SR. The need for further validation of SR under various cloud conditions emanates from this study, to ascertain the utility of AC approaches under such conditions. Overall, the results have implications for the utility of remotely sensed images for agricultural crop monitoring in partly-cloudy conditions.

AB - Globally, remotely sensed agricultural monitoring is impeded by cloudy conditions which render the acquired images useless. In semi-arid landscapes, rainfed croplands dominate agricultural production; thus, the majority of planting occurs in the rainy season, characterized by erratic cloud cover. The clear-sky pixels in partly-cloudy images can be used to increase the number of useful observations for quantitative analysis. This is achieved by cloud screening and atmospheric correction (AC) processes. However, the effectiveness of various AC approaches under partly-cloudy conditions is still unknown. Many studies validate surface reflectance (SR) under clear-sky conditions, with only a few focusing on the validation of SR under partly-cloudy conditions. This study sought to validate Sentinel-2 SR products derived from various AC approaches, i.e., MAJA, Sen2Cor, iCor, and FORCE, using in-situ spectral measurements. A partly-cloudy image with >60% cloud cover, acquired over a semi-arid agricultural landscape and ±3 days of the field measurements, was used as a test case. The results showed R2 of <0.2 with in-situ SR in the VIS and SWIR, and R2 of ~0.6 in the Red-edge and NIR regions, attributable to low aerosol scattering effect in the NIR. However, RMSE, MAE, and BIAS error metrics showed consistently higher errors across all AC approaches and crop types, with varying magnitude per spectral band. This finding can be attributed to the high presence of clouds in the image, which enhanced the apparent path radiance, causing an overestimation of the aerosol optical thickness, hence an underestimation of SR. The need for further validation of SR under various cloud conditions emanates from this study, to ascertain the utility of AC approaches under such conditions. Overall, the results have implications for the utility of remotely sensed images for agricultural crop monitoring in partly-cloudy conditions.

KW - Analysis-Ready-Data

KW - Atmospheric correction

KW - FORCE

KW - ICor

KW - MAJA

KW - Sen2Cor

KW - Sentinel-2

KW - Surface Reflectance

UR - https://www.scopus.com/pages/publications/85093682180

U2 - 10.1117/12.2572293

DO - 10.1117/12.2572293

M3 - Conference contribution

AN - SCOPUS:85093682180

T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - Remote Sensing of Clouds and the Atmosphere XXV

A2 - Comeron, Adolfo

A2 - Kassianov, Evgueni I.

A2 - Schafer, Klaus

A2 - Picard, Richard H.

A2 - Weber, Konradin

A2 - Singh, Upendra N.

PB - SPIE

T2 - Remote Sensing of Clouds and the Atmosphere XXV 2020

Y2 - 21 September 2020 through 25 September 2020

ER -

Kganyago M, Ovakoglou G, Mhangara P, Alexandridis T, Odindi J, Adjorlolo C et al. Validation of atmospheric correction approaches for Sentinel-2 under partly-cloudy conditions in an African agricultural landscape. In Comeron A, Kassianov EI, Schafer K, Picard RH, Weber K, Singh UN, editors, Remote Sensing of Clouds and the Atmosphere XXV. SPIE. 2020. 115310B. (Proceedings of SPIE - The International Society for Optical Engineering). doi: 10.1117/12.2572293

Validation of atmospheric correction approaches for Sentinel-2 under partly-cloudy conditions in an African agricultural landscape

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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