ChinaSpec: A Network for Long‐Term Ground‐Based Measurements of Solar‐Induced Fluorescence in China

Zhang, Yongguang; Zhang, Qian; Liu, Liangyun; Zhang, Yangjian; Wang, Shaoqiang; Ju, Weimin; Zhou, Guangsheng; Zhou, Li; Tang, Jianwu; Zhu, Xudong; Wang, Feng; Huang, Ying; Zhang, Zhaoying; Qiu, Bo; Zhang, Xiaokang; Wang, Songhan; Huang, Changping; Tang, Xuguang; Zhang, Jinsong

doi:10.1029/2020jg006042

Cited by 27 publications

(12 citation statements)

References 85 publications

(161 reference statements)

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“…The study site, located in Shangqiu, Henan Province, China (Figure 1), is a part of the ChinaSpec network (https://chinaspec.nju.edu.cn/(accessed on 6 December 2022)) managed by Nanjing University and Institute of Farmland Irrigation of the Chinese Academy of Agricultural Sciences (CN-SQ, National Agro-Ecological Observation and Research Station of Shangqiu: 34.5203°N, 115.5894°E) [46][47][48]. This region in the North China Plain is renowned for its annual crop rotation, mainly involving winter wheat (Triticum aestivum) and summer maize (Zea mays).…”

Section: Study Sitementioning

confidence: 99%

Identifying Crop Growth Stages from Solar-Induced Chlorophyll Fluorescence Data in Maize and Winter Wheat from Ground and Satellite Measurements

Hou,

Wu,

et al. 2023

Remote Sensing

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Crop growth stages are integral components of plant phenology and are of significant ecological and agricultural importance. While the use of remote sensing methods for phenology identification in cropland ecosystems has been extensively explored in previous studies, the focus has often been on land surface phenology, primarily related to the start and end of the growing season. In contrast, the monitoring of crop growth within an agronomic framework has been limited, particularly in the context of recently developed solar-induced chlorophyll fluorescence (SIF) data. Additionally, some critical growth stages have not received adequate attention or evaluation. This study aims to assess the utility of SIF data, collected from both ground and satellite measurements, for identifying critical crop growth stages within the realm of remote sensing phenological estimation. A comparative analysis was conducted using enhanced vegetation index (EVI) data at the Shangqiu site in the North China Plain from 2018 to 2022. Both SIF and EVI time-series data, obtained from ground and satellite sources, undergo a comprehensive phenological estimation framework encompassing pre-processing, modeling, and transition characterization. This approach involves reconciling time-series phenological patterns with crop growth stages, revealing the necessity of redefining the mapping relationship between these two fundamental concepts. After preprocessing the time-series data, the framework incorporates the phenological modeling process employing two double logistic models and a spline model for comparison. Additionally, it includes phenological transition characterization using four different methods. Consequently, each input dataset undergoes an assessment, resulting in 12 sets of estimations, which are compared to select the ideal estimation portfolio for identifying the growth stages of maize and winter wheat. Our findings highlight the efficacy of SIF data in accurately identifying the growth stages of maize and winter wheat, achieving remarkable results with an R-square exceeding 0.9 and an RMSE of less than 1 week for key growth stages (KGSs). Notably, SIF data demonstrate superior accuracy, robustness, and sensitivity to phenological events when compared to EVI data. This study establishes an estimation portfolio utilizing SIF data, involving the Gu model, a double logistic model, as the preferred phenological modelling method together with various compositing methods and transition characterization methods, suitable for most KGSs. These findings create opportunities for future research aimed at enhancing and standardizing crop growth stage identification using remote sensing data for a wide range of KGSs.

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Section: Study Sitementioning

confidence: 99%

Identifying Crop Growth Stages from Solar-Induced Chlorophyll Fluorescence Data in Maize and Winter Wheat from Ground and Satellite Measurements

Hou,

Wu,

et al. 2023

Remote Sensing

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“…The tower-based SIF validation datasets were collected from five ChinaSpec (http://chinaspec.nju.edu.cn) network sites (i.e., XTS, DM, GC, HL, and AR) [25]. The main emphasis of ChinaSpec is to gather ground-based SIF measurements from the flux sites that are part of the ChinaFLUX network (http://www.chinaflux.org/) [26].…”

Section: Tower-based Measurementsmentioning

confidence: 99%

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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“…For example, an optical network, EUROSPEC, was used to conduct long-term ground-based optical measurements at the representative EC towers in the European Union (EU) [32,33]. In addition, Zhang et al also established a network (ChinaSpec) for long-term ground-based measurements of solar-induced fluorescence in China [34], which offers long-term spectral observation data for various ecosystem types.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Leaf Area Index Estimation for Maize with Tower-Based Multi-Angular Spectral Observations

Yan,

Liu,

Jing

et al. 2023

Sensors

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The leaf area index (LAI) played a crucial role in ecological, hydrological, and climate models. The normalized difference vegetation index (NDVI) has been a widely used tool for LAI estimation. However, the NDVI quickly saturates in dense vegetation and is susceptible to soil background interference in sparse vegetation. We proposed a multi-angular NDVI (MAVI) to enhance LAI estimation using tower-based multi-angular observations, aiming to minimize the interference of soil background and saturation effects. Our methodology involved collecting continuous tower-based multi-angular reflectance and the LAI over a three-year period in maize cropland. Then we proposed the MAVI based on an analysis of how canopy reflectance varies with solar zenith angle (SZA). Finally, we quantitatively evaluated the MAVI’s performance in LAI retrieval by comparing it to eight other vegetation indices (VIs). Statistical tests revealed that the MAVI exhibited an improved curvilinear relationship with the LAI when the NDVI is corrected using multi-angular observations (R2 = 0.945, RMSE = 0.345, rRMSE = 0.147). Furthermore, the MAVI-based model effectively mitigated soil background effects in sparse vegetation (R2 = 0.934, RMSE = 0.155, rRMSE = 0.157). Our findings demonstrated the utility of tower-based multi-angular spectral observations in LAI retrieval, having the potential to provide continuous data for validating space-borne LAI products. This research significantly expanded the potential applications of multi-angular observations.

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ChinaSpec: A Network for Long‐Term Ground‐Based Measurements of Solar‐Induced Fluorescence in China

Cited by 27 publications

References 85 publications

Identifying Crop Growth Stages from Solar-Induced Chlorophyll Fluorescence Data in Maize and Winter Wheat from Ground and Satellite Measurements

Identifying Crop Growth Stages from Solar-Induced Chlorophyll Fluorescence Data in Maize and Winter Wheat from Ground and Satellite Measurements

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

Enhancing Leaf Area Index Estimation for Maize with Tower-Based Multi-Angular Spectral Observations

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