当前位置: X-MOL 学术Remote Sens. Lett. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
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
Remote Sensing Letters ( IF 1.4 ) Pub Date : 2020-07-09 , DOI: 10.1080/2150704x.2020.1767823
Mahlatse Kganyago 1, 2 , Paidamwoyo Mhangara 2 , Thomas Alexandridis 3 , Giovanni Laneve 4 , Georgios Ovakoglou 3 , Nosiseko Mashiyi 1
Affiliation  

ABSTRACT

This study validated SNAP-derived LAI from Sentinel-2 and its consistency with existing global LAI products. The validation and inter-comparison 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 SNAP-derived LAI and in-situ LAI, but with high errors, i.e., RMSE, BIAS, and MAE >2 m2 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 m2 m–2 and ~0.6 m2 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 SNAP-derived 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.



中文翻译:

来自SNAP工具箱的前哨2叶面积指数(LAI)产品的验证及其与非洲半干旱农业景观中全球LAI产品的比较

摘要

这项研究验证了Sentinel-2的SNAP衍生LAI及其与现有全球LAI产品的一致性。验证和比对实验是在两个处理级别上进行的,即大气顶部和大气底部的反射率以及两个空间分辨率,即10 m和20 m。选择这些参数以确定它们对检索到的LAI准确性和一致性的影响。结果显示,在SNAP派生的LAI和原位 LAI之间,R 2为中等 ,即〜0.6至 〜0.7,但具有较高的误差,即RMSE,BIAS和MAE> 2 m 2  m –2 在处理级别之间存在明显差异,而在空间分辨率之间则没有明显差异。相比之下,SNAP衍生的LAI与MODIS和Proba-V LAI产品的相互比较显示出中等至高度的一致性,即 R 2 分别为〜0.55和〜0.8,RMSE为〜0.5 m 2  m –2 和〜0.6 m 2  m –2, 分别。这项研究的结果对Sentinel-2的SNAP衍生的LAI在农业景观中的未来使用具有影响,表明其全球适用性对于大规模农业监测至关重要。但是,在表征田间水平LAI变异性方面存在巨大错误,这表明SNAP衍生的LAI不适合精确耕作。实际上,根据该研究,出现了进一步改善LAI检索的需求,特别是为了支持农场一级的农业管理决策。

更新日期:2020-07-09
down
wechat
bug