Remote sensing of temperate and boreal forest phenology: A review of progress, challenges and opportunities in the intercomparison of in-situ and satellite phenological metrics

Berra, Elias Fernando; Gaulton, Rachel

doi:10.1016/j.foreco.2020.118663

Cited by 60 publications

(58 citation statements)

References 223 publications

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“…The after-season phenology mapping approaches and LSP data products have been reviewed in several publications [40,43,44]. Misra et al summarized general phenological research using Sentinel-2 and discussed future improvements [40].…”

Section: Introductionmentioning

confidence: 99%

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Mapping Crop Phenology in Near Real-Time Using Satellite Remote Sensing: Challenges and Opportunities

Gao

Zhang

2021

J Remote Sens

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Crop phenology is critical for agricultural management, crop yield estimation, and agroecosystem assessment. Traditionally, crop growth stages are observed from the ground, which is time-consuming and lacks spatial variability. Remote sensing Vegetation Index (VI) time series has been used to map land surface phenology (LSP) and relate to crop growth stages mostly after the growing season. In recent years, high temporal and spatial resolution remote sensing data have allowed near-real-time mapping of crop phenology within the growing season. This paper summarizes two classes of near-real-time mapping methods, i.e., curve-based and trend-based approaches. The curve-based approaches combine the time series VIs and crop growth stages from historical years with the current observations to estimate crop growth stages. The curve-based approaches are capable of a short-term prediction. The trend-based approaches detect upward or downward trends from time series and confirm the trends using the increasing or decreasing momentum and VI thresholds. The trend-based approaches only use current observations. Both curve-based and trend-based approaches are promising in mapping crop growth stages timely. Nevertheless, mapping crop phenology near real-time is challenging since remote sensing observations are not always sensitive to crop growth stages. The accuracy of crop phenology detection depends on the frequency and availability of cloud-free observations within the growing season. Recent satellite datasets such as the harmonized Landsat and Sentinel-2 (HLS) are promising for mapping crop phenology within the season over large areas. Operational applications in the near future are feasible.

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Section: Introductionmentioning

confidence: 99%

“…Zeng et al summarized methods for LSP detection and focused on the after-season approaches [43]. Berra and Gaulton examined LSP studies related to temperate and boreal forests [44]. The objective of this paper is to present the latest advancement in crop phenology mapping in near real-time using multisource satellite images.…”

Section: Introductionmentioning

confidence: 99%

Mapping Crop Phenology in Near Real-Time Using Satellite Remote Sensing: Challenges and Opportunities

Gao

Zhang

2021

J Remote Sens

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“…In terms of the imaging mechanism, the main data types are optical data, microwave data, LIDAR and hyperspectral data. Spatial resolution can be divided into different levels, such as WorldView-3 (0.3 m resolution) belongs to the high-resolution satellites, Landsat-8 satellites (15 m) belongs to the mid-resolution satellites and MODIS (Moderate-resolution Imaging Spectroradiometer) (250 m) belongs to the low-resolution satellites (Ma et al 2014;Liu 2015;Alkathiri, Jhummarwala, and Potdar 2019;Chen, Dkuo, and Chen 2020;Berra and Gaulton 2021).…”

Section: The Rs 'Big Data' Overviewmentioning

confidence: 99%

“…Firstly, in the data phase, we can choose any kind of remote sensing data as Big Data sources that are based on previous experiences and knowledge (as for CBR) as well as incorporated with forest investigation data for processing. As many studies show that satellite imagery combined with forest inventories has become one of the critical evaluation techniques for carbon sink, carbon stocks and sequestration (Muukkonen and Heiskanen 2005;Maselli et al 2006;Lechner, Foody, and Boyd 2020;Berra and Gaulton 2021), the technique of RS has many advantages, such as great area, immediacy, diverse resolution, multi-frequency, extensive manipulation, and unceasing progress of new technology; there is no doubt that it has become the mainstream measures of solution. In Taiwan, the RS data have multiple sources, except for commercial imagery such as IKONOS, QuickBird, WorldView1-4, GeoEye-1 and other high-resolution imagery or aerial photography.…”

Section: Rs-kbdss Frameworkmentioning

confidence: 99%

A decision support system for Taiwan’s forest resource management using Remote Sensing Big Data

Wang

Lin

Chu

et al. 2021

Enterprise Information Systems

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This study aims to incorporate the application of RS technology with management strategies and proposes the Remote Sensing Knowledge-Based Decision Support System (RS-KBDSS) framework. It is not only focusing on considering RS technology for discussing the accuracy of estimation but also conducting strategic thinking that is needed for keeping the inventory and monitoring task running by obtaining knowledge and decision support. This study offers more flexible and accurate estimation models for different resolution images and different kinds of models. The results obtained by the study is satisfied with the practical application.

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“…The satellite-based methods, which are based on the analysis of the seasonal dynamics of VI time series, has offered a continuous and robust way to monitor plant phenology at large spatial scales (Reed et al, 2009;Zhang et al, 2006). Generally, three primary procedures were conducted in the satellite-based methods to estimate the EOS: first, cleaning and flagging the noisy data in original VI products; second, smoothing and reconstructing VI time series curves using curve fitting methods (e.g., logistic, Gaussian-midpoint, harmonic analysis of time series (HANTS) and polyfit maximum); third, determining the EOS dates by identifying the inflection of the transition point from the reconstructed VI curve or predefined threshold of the VI (Berra & Gaulton, 2021;Zeng et al, 2020). For example, Zhang et al (2003) developed a piecewise logistic function to fit time series normalized difference vegetation index (NDVI) data and identified the key phenological transition dates through the changes in curvature.…”

Section: Introductionmentioning

confidence: 99%

Regional evaluation of satellite‐based methods for identifying end of vegetation growing season

Shen

Yuan

et al. 2021

Remote Sens Ecol Conserv

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Autumn phenology plays an important role in regulating ecosystem carbon and water cycling, but it has received less attention than spring phenology. Satellitebased methods have been widely applied in monitoring autumn phenology at large spatial scales. However, few studies have evaluated and compared the performance of different satellite-based methods in autumn phenology identification. Here, we compared the spatiotemporal variations of end of vegetation growing season dates (EOS) as determined from eight prevailing satellite-based methods against long-term field observations at 31 sites in China. We found that field-based observations in forest and grassland sites, respectively, had rates of EOS delay of 2.11 and 3.85 days per 1°C increase in mean annual temperature (MAT) during 2001-2014. However, nearly all the eight satellite-based methods underestimated these delay rates compared with the ground observations over all sites. We also found that the eight methods weakly agreed with the field-observed interannual variations of EOS. At the regional scale, the identified average EOS differed up to 38 and 40 days among the investigated satellite-based methods in forest and grassland ecosystems respectively. The delayed rate of identified EOS with the increase of MAT ranged from 0.77 to 3.51 days°C −1 for forests and from 0.41 to 2.95 days°C −1 for grasslands. The identified EOS by most of the eight methods had delayed temporal trends in forests during 2001-2014 while we found advanced trends in grassland ecosystems. The large discrepancy in EOS identification among the prevailing satellite-based methods highlight the need for more accurate satellite-based methods in data gap-filling and phenometrics detection, and more extensive, multi-species based field observations that can be used to constrain and validate the satellite-based methods.

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Remote sensing of temperate and boreal forest phenology: A review of progress, challenges and opportunities in the intercomparison of in-situ and satellite phenological metrics

Cited by 60 publications

References 223 publications

Mapping Crop Phenology in Near Real-Time Using Satellite Remote Sensing: Challenges and Opportunities

Mapping Crop Phenology in Near Real-Time Using Satellite Remote Sensing: Challenges and Opportunities

A decision support system for Taiwan’s forest resource management using Remote Sensing Big Data

Regional evaluation of satellite‐based methods for identifying end of vegetation growing season

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