Comparisons of global land surface seasonality and phenology derived from AVHRR, MODIS, and VIIRS data

Zhang, Xiaoyang; Liu, Lingling; Yan, Dong

doi:10.1002/2017jg003811

Cited by 51 publications

(29 citation statements)

References 67 publications

(101 reference statements)

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“…Thus, it is vital that phenological products are derived from high quality time series data, such as those described in in this manuscript. While phenology metrics have been routinely generated from coarse-resolution data [40], data derived solely from moderate-resolution imagery (e.g., Landsat and Sentinel-2) are beginning to emerge [41]. Given the declines in MODIS performance and forthcoming decommission with no equivalent replacement planned, data fusion approaches that integrate observations from multispectral sensors (e.g., Landsat, Sentinel-2A and -2B) will be needed to effectively monitor dryland ecosystems.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Analysis of Landsat-8 and Sentinel-2 Data to Support Monitoring of Dryland Ecosystems

Pastick

Wylie

2018

Remote Sensing

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Drylands are the habitat and source of livelihood for about two fifths of the world's population and are highly susceptible to climate and anthropogenic change. To understand the vulnerability of drylands to changing environmental conditions, land managers need to effectively monitor rates of past change and remote sensing offers a cost-effective means to assess and manage these vast landscapes. Here, we present a novel approach to accurately monitor land-surface phenology in drylands of the Western United States using a regression tree modeling framework that combined information collected by the Operational Land Imager (OLI) onboard Landsat 8 and the Multispectral Instrument (MSI) onboard Sentinel-2. This highly-automatable approach allowed us to precisely characterize seasonal variations in spectral vegetation indices with substantial agreement between observed and predicted values (R 2 = 0.98; Mean Absolute Error = 0.01). Derived phenology curves agreed with independent eMODIS phenological signatures of major land cover types (average r-value = 0.86), cheatgrass cover (average r-value = 0.96), and growing season proxies for vegetation productivity (R 2 = 0.88), although a systematic bias towards earlier maturity and senescence indicates enhanced monitoring capabilities associated with the use of harmonized Landsat-8 Sentinel-2 data. Overall, our results demonstrate that observations made by the MSI and OLI can be used in conjunction to accurately characterize land-surface phenology and exclusion of imagery from either sensor drastically reduces our ability to monitor dryland environments. Given the declines in MODIS performance and forthcoming decommission with no equivalent replacement planned, data fusion approaches that integrate observations from multispectral sensors will be needed to effectively monitor dryland ecosystems. While the synthetic image stacks are expected to be locally useful, the technical approach can serve a wide variety of applications such as invasive species and drought monitoring, habitat mapping, production of phenology metrics, and land-cover change modeling.

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Section: Discussionmentioning

confidence: 99%

Spatiotemporal Analysis of Landsat-8 and Sentinel-2 Data to Support Monitoring of Dryland Ecosystems

Pastick

Wylie

2018

Remote Sensing

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“…The daily EVI2 and LST were further aggregated to 3-day composite data by selecting maximum EVI2 and averaging LST if more than one cloud-free observations existed within a 3-day window. It is worth noting that the climatology of MODIS EVI2 values was calibrated to be comparable with VIIRS EVI2 due to the difference in spectral bands between MODIS and VIIRS [49,50]. To do this, we first reconstructed the temporal trajectories of MODIS EVI2 (from MCD43A4, Collection 6) and VIIRS EVI2 in 2014 using the HPLM method [43].…”

Section: Real-time Phenological Monitoring Algorithmmentioning

confidence: 99%

Real-Time Monitoring of Crop Phenology in the Midwestern United States Using VIIRS Observations

Liu

Zhang

et al. 2018

Remote Sensing

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Real-time monitoring of crop phenology is critical for assisting farmers managing crop growth and yield estimation. In this study, we presented an approach to monitor in real time crop phenology using timely available daily Visible Infrared Imaging Radiometer Suite (VIIRS) observations and historical Moderate Resolution Imaging Spectroradiometer (MODIS) datasets in the Midwestern United States. MODIS data at a spatial resolution of 500 m from 2003 to 2012 were used to generate the climatology of vegetation phenology. By integrating climatological phenology and timely available VIIRS observations in 2014 and 2015, a set of temporal trajectories of crop growth development at a given time for each pixel were then simulated using a logistic model. The simulated temporal trajectories were used to identify spring green leaf development and predict the occurrences of greenup onset, mid-greenup phase, and maximum greenness onset using curvature change rate. Finally, the accuracy of real-time monitoring from VIIRS observations was evaluated by comparing with summary crop progress (CP) reports of ground observations from the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA). The results suggest that real-time monitoring of crop phenology from VIIRS observations is a robust tool in tracing the crop progress across regional areas. In particular, the date of mid-greenup phase from VIIRS was significantly correlated to the planting dates reported in NASS CP for both corn and soybean with a consistent lag of 37 days and 27 days on average (p < 0.01), as well as the emergence dates in CP with a lag of 24 days and 16 days on average (p < 0.01), respectively. The real-time monitoring of maximum greenness onset from VIIRS was able to predict the corn silking dates with an advance of 9 days (p < 0.01) and the soybean blooming dates with a lag of 7 days on average (p < 0.01). These findings demonstrate the capability of VIIRS observations to effectively monitor temporal dynamics of crop progress in real time at a regional scale.

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“…Land-surface phenology, reflecting the seasonality of vegetated land surface detected by remotely sensed imagery, has attracted increasing attention in recent decades, as it provides an independent, long-term, globally sensed measure for assessing ecosystem responses to climate change [1][2][3]. Although significant progress has been made to detect phenology metrics, particularly the green-up date (GUD), based on vegetation index (VI) time-series, there is still considerable inconsistency in the detected land-surface phenology among different sensors [3][4][5], thus posing challenges in the precise quantification of vegetation phenological changes and their responses to climate change at a large scale. The inconsistency may be caused by differences in imaging condition, spectral response functions of sensors and geometric registration.…”

Section: Introductionmentioning

confidence: 99%

How Does Scale Effect Influence Spring Vegetation Phenology Estimated from Satellite-Derived Vegetation Indexes?

et al. 2019

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As an important land-surface parameter, vegetation phenology has been estimated from observations by various satellite-borne sensors with substantially different spatial resolutions, ranging from tens of meters to several kilometers. The inconsistency of satellite-derived phenological metrics (e.g., green-up date, GUD, also known as the land-surface spring phenology) among different spatial resolutions, which is referred to as the “scale effect” on GUD, has been recognized in previous studies, but it still needs further efforts to explore the cause of the scale effect on GUD and to quantify the scale effect mechanistically. To address these issues, we performed mathematical analyses and designed up-scaling experiments. We found that the scale effect on GUD is not only related to the heterogeneity of GUD among fine pixels within a coarse pixel, but it is also greatly affected by the covariation between the GUD and vegetation growth speed of fine pixels. GUD of a coarse pixel tends to be closer to that of fine pixels with earlier green-up and higher vegetation growth speed. Therefore, GUD of the coarse pixel is earlier than the average of GUD of fine pixels, if the growth speed is a constant. However, GUD of the coarse pixel could be later than the average from fine pixels, depending on the proportion of fine pixels with later GUD and higher growth speed. Based on those mechanisms, we proposed a model that accounted for the effects of heterogeneity of GUD and its co-variation with growth speed, which explained about 60% of the scale effect, suggesting that the model can help convert GUD estimated at different spatial scales. Our study provides new mechanistic explanations of the scale effect on GUD.

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Comparisons of global land surface seasonality and phenology derived from AVHRR, MODIS, and VIIRS data

Cited by 51 publications

References 67 publications

Spatiotemporal Analysis of Landsat-8 and Sentinel-2 Data to Support Monitoring of Dryland Ecosystems

Spatiotemporal Analysis of Landsat-8 and Sentinel-2 Data to Support Monitoring of Dryland Ecosystems

Real-Time Monitoring of Crop Phenology in the Midwestern United States Using VIIRS Observations

How Does Scale Effect Influence Spring Vegetation Phenology Estimated from Satellite-Derived Vegetation Indexes?

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