Addressing validation challenges for TROPOMI solar-induced chlorophyll fluorescence products using tower-based measurements and an NIRv-scaled approach

Du, Shanshan; Liu, Xinjie; Chen, Jidai; Duan, Weina; Liu, Liangyun

doi:10.1016/j.rse.2023.113547

Cited by 10 publications

(8 citation statements)

References 68 publications

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“…Therefore, future research may need to explore some semi-supervised or unsupervised DL methods to alleviate the need for annotated data [44,45]. In addition, we will also explore how to incorporate features such as vegetation parameters and phenological changes into our MAFANet model to further improve the richness of the model and its ability to UGS segmentation [46].…”

Section: Discussionmentioning

confidence: 99%

A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images

Wang,

Cheng,

Ren

et al. 2023

Remote Sensing

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The comprehensive use of high-resolution remote sensing (HRS) images and deep learning (DL) methods can be used to further accurate urban green space (UGS) mapping. However, in the process of UGS segmentation, most of the current DL methods focus on the improvement of the model structure and ignore the spectral information of HRS images. In this paper, a multiscale attention feature aggregation network (MAFANet) incorporating feature engineering was proposed to achieve segmentation of UGS from HRS images (GaoFen-2, GF-2). By constructing a new decoder block, a bilateral feature extraction module, and a multiscale pooling attention module, MAFANet enhanced the edge feature extraction of UGS and improved segmentation accuracy. By incorporating feature engineering, including false color image and the Normalized Difference Vegetation Index (NDVI), MAFANet further distinguished UGS boundaries. The UGS labeled datasets, i.e., UGS-1 and UGS-2, were built using GF-2. Meanwhile, comparison experiments with other DL methods are conducted on UGS-1 and UGS-2 to test the robustness of the MAFANet network. We found the mean Intersection over Union (MIOU) of the MAFANet network on the UGS-1 and UGS-2 datasets was 72.15% and 74.64%, respectively; outperforming other existing DL methods. In addition, by incorporating false color image in UGS-1, the MIOU of MAFANet was improved from 72.15% to 74.64%; by incorporating vegetation index (NDVI) in UGS-1, the MIOU of MAFANet was improved from 72.15% to 74.09%; and by incorporating false color image and the vegetation index (NDVI) in UGS-1, the MIOU of MAFANet was improved from 72.15% to 74.73%. Our experimental results demonstrated that the proposed MAFANet incorporating feature engineering (false color image and NDVI) outperforms the state-of-the-art (SOTA) methods in UGS segmentation, and the false color image feature is better than the vegetation index (NDVI) for enhancing green space information representation. This study provided a practical solution for UGS segmentation and promoted UGS mapping.

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Section: Discussionmentioning

confidence: 99%

A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images

Wang,

Cheng,

Ren

et al. 2023

Remote Sensing

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“…The basic idea of the upscaling function is that the ratio of the NIRv with different spatial scales can be regarded as the upscaling coefficients of SIF for the two spatial scales (Eq. ( 5)) [13].…”

Section: Quantification Of Uncertainty Caused By Geolocation Mismatchmentioning

confidence: 99%

“…where 𝑁𝐼𝑅𝑣 , and 𝑁𝐼𝑅𝑣 , represented the Sentinel-2 NIRv on the TROPOMI pixel scale at the exact and nominal locations of validation pixel, which was calculated by aggregating Sentinel-2 SR within the footprint of TROPOMI pixel as recommended by Du et al [13]. 𝑁𝐼𝑅𝑣 , is the Sentinel-2 pixel NIRv containing the tower-based location.…”

Section: Quantification Of Uncertainty Caused By Geolocation Mismatchmentioning

confidence: 99%

“…However, at the DM site, only maize was cultivated during the summer season for each year. By contrast, the HL site was featured by a combination of grass and several crabapple trees [13]. These sites were installed with the SIFSpec instrument, which consists of a QE 65Pro spectrometer, operating within a wavelength range of approximately 650 nm to 840 nm.…”

Section: Tower-based Measurementsmentioning

confidence: 99%

“…It has been well acknowledged that the direct validation of satellite SIF products based on in-situ SIF measurements is challenged by the uncertainties related to the in-situ measuring system, the spatial scale mismatch between in-situ and satellite-based SIF measurements, spectral difference, and retrieval algorithms. By addressing these challenges, Du et al [13] conducted a direct validation of the TROPOMI SIF product based on a sufficient in-situ SIF database. However, the influence of the geolocation mismatch between the validation pixel and reference data on validation results has not been recognized.…”

Section: Introductionmentioning

confidence: 99%

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Quantification of Uncertainty Caused by Geometric Location Mismatch in the Validation of TROPOMI Solar-Induced Chlorophyll Fluorescence Product

Zeng,

Wu,

Tang

et al. 2023

Preprint

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Validation of the TROPOspheric Monitoring Instrument (TROPOMI) SIF product is a necessity to evaluate its feasibility in various applications. A few validation works have been conducted through direct comparison with in-situ SIF retrievals or cross-comparison with similar satel-lite-based SIF or vegetation index (VI) products. Nevertheless, the influence of the geolocation mismatch between the validation pixel and reference data on validation results was never con-sidered. This study, for the first time, quantifies the geolocation shift of the TROPOMI validation pixel based on a geometric location matching method and then the uncertainty caused by the ge-olocation mismatch. The results indicate that the geolocation shift of the TROPOMI pixel shows large temporal variations, with a standard deviation of 2.45 km and 1.96 km in the across- and along-track directions. The mean shifts are 0.07 km and 0.25 km in the across- and along-track directions respectively, indicating eastward shifts in the across-track direction and northward shifts in the along-track direction in general. More than half (42/54) of the time periods showed relative uncertainty larger than 5%, with the maximum even reaching 55.7%. Hence, the uncertainty caused by geolocation mismatch should be fully considered in the validation of satellite SIF products, especially over heterogeneous surfaces.

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Global retrieval of the spectrum of terrestrial chlorophyll fluorescence: First results with TROPOMI

Zhao,

Ma,

Zhao

et al. 2024

Remote Sensing of Environment

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Addressing validation challenges for TROPOMI solar-induced chlorophyll fluorescence products using tower-based measurements and an NIRv-scaled approach

Cited by 10 publications

References 68 publications

A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images

A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images

Quantification of Uncertainty Caused by Geometric Location Mismatch in the Validation of TROPOMI Solar-Induced Chlorophyll Fluorescence Product

Global retrieval of the spectrum of terrestrial chlorophyll fluorescence: First results with TROPOMI

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