All models of satellite-derived phenology are wrong, but some are useful: A case study from northern Australia

Younes, Nicolás; Joyce, Karen E.; Maier, Stefan W.

doi:10.1016/j.jag.2020.102285

Cited by 25 publications

(26 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Preliminary investigation of the Landsat 7 NDVI and yield at training field J showed that the relationships between integrated NDVI metrics and yield varied by year and location and was heavily influenced by missing data in the NDVI time sequences. This finding agrees with previous studies [37][38][39] and shows that, due to seasonal and spatial variability, any one metric has limited ability for yield estimation within a field. Waldner et al [40] quantified the positive relationship between temporal resolution of VI sequences and accuracy of empirical estimation on grain yield based on crop modelling.…”

Section: Discussionsupporting

confidence: 93%

The Potential of Landsat NDVI Sequences to Explain Wheat Yield Variation in Fields in Western Australia

Shen

Evans

2021

Remote Sensing

View full text Add to dashboard Cite

Long-term maps of within-field crop yield can help farmers understand how yield varies in time and space and optimise crop management. This study investigates the use of Landsat NDVI sequences for estimating wheat yields in fields in Western Australia (WA). By fitting statistical crop growth curves, identifying the timing and intensity of phenological events, the best single integrated NDVI metric in any year was used to estimate yield. The hypotheses were that: (1) yield estimation could be improved by incorporating additional information about sowing date or break of season in statistical curve fitting for phenology detection; (2) the integrated NDVI metrics derived from phenology detection can estimate yield with greater accuracy than the observed NDVI values at one or two time points only. We tested the hypotheses using one field (~235 ha) in the WA grain belt for training and another field (~143 ha) for testing. Integrated NDVI metrics were obtained using: (1) traditional curve fitting (SPD); (2) curve fitting that incorporates sowing date information (+SD); and (3) curve fitting that incorporates rainfall-based break of season information (+BOS). Yield estimation accuracy using integrated NDVI metrics was further compared to the results using a scalable crop yield mapper (SCYM) model. We found that: (1) relationships between integrated NDVI metrics using the three curve fitting models and yield varied from year to year; (2) overall, +SD marginally improved yield estimation (r = 0.81, RMSE = 0.56 tonnes/ha compared to r = 0.80, RMSE = 0.61 tonnes/ha using SPD), but +BOS did not show obvious improvement (r = 0.80, RMSE = 0.60 tonnes/ha); (3) use of integrated NDVI metrics was more accurate than SCYM (r = 0.70, RMSE = 0.62 tonnes/ha) on average and had higher spatial and yearly consistency with actual yield than using SCYM model. We conclude that sequences of Landsat NDVI have the potential for estimation of wheat yield variation in fields in WA but they need to be combined with additional sources of data to distinguish different relationships between integrated NDVI metrics and yield in different years and locations.

show abstract

Section: Discussionsupporting

confidence: 93%

The Potential of Landsat NDVI Sequences to Explain Wheat Yield Variation in Fields in Western Australia

Shen

Evans

2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…This aggregation often disassociates the response signal of the landscape from that of the individual species, yet is essential for representing landscape-scale processes (e.g., water, energy, and carbon fluxes) in biosphere-atmosphere interaction and other models (Reed et al, 2009 ). Due to the requirement of repeated observations to study LSP, most satellite-based phenology studies relied on medium to coarse spatial resolution satellite sensors, such as MODIS and Landsat, or on fusing MODIS and Landsat imagery to improve the temporal and spatial resolution (Younes et al, 2021 ). The same phenological events assessed by PhenoCams or UAVs are typically also analyzed via satellite images, such as SOS, PGS, and EOS (Zeng et al, 2020 ).…”

Section: Plant Phenology Monitoring Methodsmentioning

confidence: 99%

Deep Learning in Plant Phenological Research: A Systematic Literature Review

et al. 2022

View full text Add to dashboard Cite

Climate change represents one of the most critical threats to biodiversity with far-reaching consequences for species interactions, the functioning of ecosystems, or the assembly of biotic communities. Plant phenology research has gained increasing attention as the timing of periodic events in plants is strongly affected by seasonal and interannual climate variation. Recent technological development allowed us to gather invaluable data at a variety of spatial and ecological scales. The feasibility of phenological monitoring today and in the future depends heavily on developing tools capable of efficiently analyzing these enormous amounts of data. Deep Neural Networks learn representations from data with impressive accuracy and lead to significant breakthroughs in, e.g., image processing. This article is the first systematic literature review aiming to thoroughly analyze all primary studies on deep learning approaches in plant phenology research. In a multi-stage process, we selected 24 peer-reviewed studies published in the last five years (2016–2021). After carefully analyzing these studies, we describe the applied methods categorized according to the studied phenological stages, vegetation type, spatial scale, data acquisition- and deep learning methods. Furthermore, we identify and discuss research trends and highlight promising future directions. We present a systematic overview of previously applied methods on different tasks that can guide this emerging complex research field.

show abstract

“…As reported by the authors, the LAI (leaf area index) product is more suitable for analyses than the analogous phenological product in the area of evergreen forests and crop fields, whereas the phenological product provides better results than LAI in areas covered by low vegetation cover and meadows. Regional studies on plant phenology have also been conducted in East Africa [360], West Africa [58], and North and East Australia [42,327]. In Iran [354], researchers used data from the MODIS spectroradiometer to analyze the development phases in orchards surrounding Lake Urmia.…”

Section: Research Scalementioning

confidence: 99%

“…Plant analyses are mainly focused on the chlorophyll content [25] or substances contained in plants [91] and, consequently, the condition of plants [24,27]. Research on the growth and development of vegetation can be carried out in both strictly controlled laboratory conditions [29,79,92,93] and using satellite techniques [42,44,46,[49][50][51]56,59,61,62,[64][65][66]68,71,[75][76][77][78]80,88, or other means of transporting remote sensing devices (UAV, airships, airplanes) [43,47,54,55,65,84,[108][109][110]118,[140][141][142][143][144][145][146][147][148]…”

Section: Introductionmentioning

confidence: 99%

“…For many years, measurements were based on traditional sources of information (i.e., direct phenological observations combined with meteorological data). The dynamic development of technology in recent decades has allowed for the use of remote sensing data, mainly properly processed satellite data with different temporal and spatial resolutions [10,14,23,26,28,[30][31][32][33][34][35][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][58][59][60][61][62][63][64][65][66][67][68][69][70][71][72]94,95,[134][135][136][137]…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Remote Sensing in Studies of the Growing Season: A Bibliometric Analysis

2022

View full text Add to dashboard Cite

Analyses of climate change based on point observations indicate an extension of the plant growing season, which may have an impact on plant production and functioning of natural ecosystems. Analyses involving remote sensing methods, which have added more detail to results obtained in the traditional way, have been carried out only since the 1980s. The paper presents the results of a bibliometric analysis of papers related to the growing season published from 2000–2021 included in the Web of Science database. Through filtering, 285 publications were selected and subjected to statistical processing and analysis of their content. This resulted in the identification of author teams that mostly focused their research on vegetation growth and in the selection of the most common keywords describing the beginning, end, and duration of the growing season. It was found that most studies on the growing season were reported from Asia, Europe, and North America (i.e., 32%, 28%, and 28%, respectively). The analyzed articles show the advantage of satellite data over low-altitude and ground-based data in providing information on plant vegetation. Over three quarters of the analyzed publications focused on natural plant communities. In the case of crops, wheat and rice were the most frequently studied plants (i.e., they were analyzed in over 30% and over 20% of publications, respectively).

show abstract

All models of satellite-derived phenology are wrong, but some are useful: A case study from northern Australia

Cited by 25 publications

References 53 publications

The Potential of Landsat NDVI Sequences to Explain Wheat Yield Variation in Fields in Western Australia

The Potential of Landsat NDVI Sequences to Explain Wheat Yield Variation in Fields in Western Australia

Deep Learning in Plant Phenological Research: A Systematic Literature Review

Remote Sensing in Studies of the Growing Season: A Bibliometric Analysis

Contact Info

Product

Resources

About