Validation of sentinel-2 leaf area index (LAI) product derived from SNAP toolbox and its comparison with global LAI products in an African semi-arid agricultural landscape

Abstract

This study validated SNAP-derived LAI from Sentinel-2 and its consistency with existing global LAI products. The validation and intercomparison experiments were performed on two processing levels, i. e., Top-of-Atmosphere and Bottom-of-Atmosphere reflectances and two spatial resolutions, i.e., 10 m, and 20 m. These were chosen to determine their effect on retrieved LAI accuracy and consistency. The results showed moderate R 2 , i.e.,~0.6 to~0.7 between SNAPderived LAI and in-situ LAI, but with high errors, i.e., RMSE, BIAS, and MAE >2 m 2 m-2 with marked differences between processing levels and insignificant differences between spatial resolutions. In contrast, inter-comparison of SNAP-derived LAI with MODIS and Proba-V LAI products revealed moderate to high consistencies, i. e., R 2 of~0.55 and~0.8 respectively, and RMSE of~0.5 m 2 m-2 and~0.6 m 2 m-2 , respectively. The results in this study have implications for future use of SNAP-derived LAI from Sentinel-2 in agricultural landscapes, suggesting its global applicability that is essential for large-scale agricultural monitoring. However, enormous errors in characterizing field-level LAI variability indicate that SNAPderived LAI is not suitable for precision farming. In fact, from the study, the need for further improvement of LAI retrieval arises, especially to support farm-level agricultural management decisions.

Analyzing the Effects of Hyperspectral ZhuHai-1 Band Combinations on LAI Estimation Based on the PROSAIL Model

“…Negative values presented in the Sentinel-2 LAI can be considered outliers. Furthermore, some studies indicated that SNAP-derived LAI may significantly overestimate LAI values for different crops [ 65 , 66 ]; the comparison of the ZH-1 LAI map with the Sentinel-2 LAI map also confirmed this conclusion.…”

“…In addition, the ZH-1 hyperspectral satellite was the only commercial hyperspectral satellite that has been launched and networked in China. Kganyago et al compared the Sentinel-2 LAI product from the semi-arid agricultural landscape in Africa with global LAI products and showed that the R 2 values of Sentinel-2 LAI product and measured LAI could reach about 0.6–0.7, but the errors of RMSE and BIAS were relatively large, while the R 2 between theSentinel-2 LAI product and MODIS and Proba-V LAI products is about 0.55–0.8, and the RMSE error is small [ 65 ]. Hence, Sentinel-2 LAI product has some implications for characterizing LAI of agricultural crops.…”

Using sentinel-2 observations to assess the consequences of the COVID-19 lockdown on winter cropping in Bothaville and Harrismith, South Africa

“…Bothaville site, located at latitudes: 27°13ʹ0ʺS to 28°8ʹ0ʺS, and longitudes: 26°0ʹ0ʺE to 27° 05ʹ0ʺE, is characterized by warm and wet summers with average temperatures of ~18°C and an annual average rainfall of ~584 mm. Winters are cold, with average temperatures of about 5.1°C (Kganyago et al 2020a). On the other hand, the Harrismith site, located at latitudes: 28°0ʹ0ʺS to 29°0ʹ0ʺS and longitudes: 28°0ʹ0ʺE to 29°8ʹ0ʺE, is characterized by warm and wet summers with an average temperature of 19.2°C and an annual average rainfall of about 115 mm.…”

Comparativa multi e hiperespectral del pasto Cynodon nlemfuensis bajo condiciones tropicales y de pastoreo con ganado lechero

“…En este sentido el IAF predicho con información satelital, sobrestima el obtenido en campo. Dicho comportamiento también ha sido obtenido en otros estudios, por ejemplo, el llevado a cabo en cultivos como maíz y girasol, donde el IAF generado con el SNAP también sobrestima los valores de campo [42].…”