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Advancing Land Surface Phenological Analysis with High Spatial Resolution Imagery

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 22191

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Special Issue Editors

Dr. Chunyuan Diao

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Guest Editor

Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, IL, USA
Interests: land surface phenology; time series remote sensing; computational remote sensing; large-scale agricultural and forest dynamic monitoring; invasive species and biodiversity

Dr. Xiaoyang Zhang

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Guest Editor

Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007, USA
Interests: biomass burning emissions; burned area; fire seasonality; climate change; real-time monitoring; remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Liang Liang

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Guest Editor

Department of Geography, University of Kentucky, Lexington, KY 40506, USA
Interests: plant/vegetation phenology; bioclimatology; remote sensing
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Dr. Rasmus Houborg

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Guest Editor

Planet Labs, 645 Harrison St, San Francisco, CA 94107, USA
Interests: multi-sensor data fusion; radiometric harmonization; machine learning; precision agriculture; satellite-based retrieval of vegetation biophysical properties and functional traits; satellite-based water use and productivity estimation
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Special Issue Information

Dear Colleagues,

Land surface phenology (LSP) plays a crucial role in characterizing ecosystem structures and functions, and is an integrative indicator of terrestrial ecosystems in response to climatic and anthropogenic changes. LSP regulates terrestrial gross primary productivity, water-energy-carbon fluxes, and ecological processes, as well as providing critical information for detecting vegetation types and land cover/land use changes. Time series of earth observation data from coarse resolution sensors (e.g., AVHRR, SPOT VGT, and MODIS) set the stage for LSP operational monitoring at regional to global scales. Recently, a new generation of time series studies using high spatial resolution imagery (sub 100m) has opened up opportunities to advance our scientific understanding of patterns, trends, drivers, and consequences of LSP dynamics across diverse ecosystems in unparalleled detail. In particular, the harmonization of Landsat and Sentinel-2, together with the fusion of coarse resolution satellite imagery (e.g., MODIS and VIIRS), has been increasingly explored to improve LSP monitoring and modeling, especially at heterogeneous landscapes. Besides, the constellation of CubeSats (e.g., PlanetScope) imagery provides enhanced capabilities for land surface characterization. The near-surface remote sensing (e.g., drone, pheno-cam, and smartphone) has gained increasing popularity with its potential to connect satellite- and ground-based phenological measures, as well as to conduct more comprehensive phenological validation. The unprecedented wealth of information provided by higher temporal frequency, improved spatial resolution, and sheer data volume calls for innovative data analysis algorithms and monitoring strategies.

In this Special Issue, we are inviting submissions including, but not limited to, the following

Novel approaches for high spatial resolution (sub 100m) LSP monitoring
Characterize spatio-temporal patterns and trends of LSP at high spatial resolutions
Multi-source data fusion for LSP characterization
Improved understanding of mechanisms and drivers underlying LSP dynamics
Multi-scale phenological monitoring from species to landscape levels
LSP applications across ecosystems (e.g., forest, cropland, and grassland)

Dr. Chunyuan Diao
Dr. Xiaoyang Zhang
Dr. Liang Liang
Dr. Rasmus Houborg
Guest Editors

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Keywords

Land surface phenology
Data fusion
Near-surface remote sensing
Phenology validation
High spatial resolution
Time series analysis

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Published Papers (5 papers)

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Research

20 pages, 4942 KiB

Open AccessArticle

Characterising the Land Surface Phenology of Middle Eastern Countries Using Moderate Resolution Landsat Data

by Sarchil Hama Qader, Rhorom Priyatikanto, Nabaz R. Khwarahm, Andrew J. Tatem and Jadunandan Dash

Remote Sens. 2022, 14(9), 2136; https://doi.org/10.3390/rs14092136 - 29 Apr 2022

Cited by 4 | Viewed by 3236

Abstract

Global change impacts including climate change, increased CO₂ and nitrogen deposition can be determined through a more precise characterisation of Land Surface Phenology (LSP) parameters. In addition, accurate estimation of LSP dates is being increasingly used in applications such as mapping vegetation types, yield forecasting, and irrigation management. However, there has not been any attempt to characterise Middle East vegetation phenology at the fine spatial resolution appropriate for such applications. Remote-sensing based approaches have proved to be a useful tool in such regions since access is restricted in some areas due to security issues and their inter-annual vegetation phenology parameters vary considerably because of high uncertainty in rainfall. This study aims to establish for the first time a comprehensive characterisation of the vegetation phenological characteristics of the major vegetation types in the Middle East at a fine spatial resolution of 30 m using Landsat Normalized Difference Vegetation Index (NDVI) time series data over a temporal range of 20 years (2000–2020). Overall, a progressive pattern in phenophases was observed from low to high latitude. The earliest start of the season was concentrated in the central and east of the region associated mainly with grassland and cultivated land, while the significantly delayed end of the season was mainly distributed in northern Turkey and Iran corresponding to the forest, resulting in the prolonged length of the season in the study area. There was a significant positive correlation between LSP parameters and latitude, which indicates a delay in the start of the season of 4.83 days (R² = 0.86, p < 0.001) and a delay in the end of the season of 6.54 days (R² = 0.83, p < 0.001) per degree of latitude increase. In addition, we have discussed the advantages of fine resolution LSP parameters over the available coarse datasets and showed how such outputs can improve many applications in the region. This study shows the potential of Landsat data to quantify the LSP of major land cover types in heterogeneous landscapes of the Middle East which enhances our understanding of the spatial-temporal dynamics of vegetation dynamics in arid and semi-arid settings in the world. Full article

(This article belongs to the Special Issue Advancing Land Surface Phenological Analysis with High Spatial Resolution Imagery)

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25 pages, 5375 KiB

Open AccessArticle

Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology

by Chunyuan Diao and Geyang Li

Remote Sens. 2022, 14(9), 1957; https://doi.org/10.3390/rs14091957 - 19 Apr 2022

Cited by 19 | Viewed by 3594

Abstract

Detecting crop phenology with satellite time series is important to characterize agroecosystem energy-water-carbon fluxes, manage farming practices, and predict crop yields. Despite the advances in satellite-based crop phenological retrievals, interpreting those retrieval characteristics in the context of on-the-ground crop phenological events remains a long-standing hurdle. Over the recent years, the emergence of near-surface phenology cameras (e.g., PhenoCams), along with the satellite imagery of both high spatial and temporal resolutions (e.g., PlanetScope imagery), has largely facilitated direct comparisons of retrieved characteristics to visually observed crop stages for phenological interpretation and validation. The goal of this study is to systematically assess near-surface PhenoCams and high-resolution PlanetScope time series in reconciling sensor- and ground-based crop phenological characterizations. With two critical crop stages (i.e., crop emergence and maturity stages) as an example, we retrieved diverse phenological characteristics from both PhenoCam and PlanetScope imagery for a range of agricultural sites across the United States. The results showed that the curvature-based Greenup and Gu-based Upturn estimates showed good congruence with the visually observed crop emergence stage (RMSE about 1 week, bias about 0–9 days, and R square about 0.65–0.75). The threshold- and derivative-based End of greenness falling Season (i.e., EOS) estimates reconciled well with visual crop maturity observations (RMSE about 5–10 days, bias about 0–8 days, and R square about 0.6–0.75). The concordance among PlanetScope, PhenoCam, and visual phenology demonstrated the potential to interpret the fine-scale sensor-derived phenological characteristics in the context of physiologically well-characterized crop phenological events, which paved the way to develop formal protocols for bridging ground-satellite phenological characterization. Full article

(This article belongs to the Special Issue Advancing Land Surface Phenological Analysis with High Spatial Resolution Imagery)

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23 pages, 19486 KiB

Open AccessArticle

Multi-Season Phenology Mapping of Nile Delta Croplands Using Time Series of Sentinel-2 and Landsat 8 Green LAI

by Eatidal Amin, Santiago Belda, Luca Pipia, Zoltan Szantoi, Ahmed El Baroudy, José Moreno and Jochem Verrelst

Remote Sens. 2022, 14(8), 1812; https://doi.org/10.3390/rs14081812 - 9 Apr 2022

Cited by 12 | Viewed by 5031

Abstract

Space-based cropland phenology monitoring substantially assists agricultural managing practices and plays an important role in crop yield predictions. Multitemporal satellite observations allow analyzing vegetation seasonal dynamics over large areas by using vegetation indices or by deriving biophysical variables. The Nile Delta represents about half of all agricultural lands of Egypt. In this region, intensifying farming systems are predominant and multi-cropping rotations schemes are increasing, requiring a high temporal and spatial resolution monitoring for capturing successive crop growth cycles. This study presents a workflow for cropland phenology characterization and mapping based on time series of green Leaf Area Index (LAI) generated from NASA’s Harmonized Landsat 8 (L8) and Sentinel-2 (S2) surface reflectance dataset from 2016 to 2019. LAI time series were processed for each satellite dataset, which were used separately and combined to identify seasonal dynamics for a selection of crop types (wheat, clover, maize and rice). For the combination of L8 with S2 LAI products, we proposed two time series smoothing and fitting methods: (1) the Savitzky–Golay (SG) filter and (2) the Gaussian Processes Regression (GPR) fitting function. Single-sensor and L8-S2 combined LAI time series were used for the calculation of key crop Land Surface Phenology (LSP) metrics (start of season, end of season, length of season), whereby the detection of cropland growing seasons was based on two established threshold methods, i.e., a seasonal or a relative amplitude value. Overall, the developed phenology extraction scheme enabled identifying up to two successive crop cycles within a year, with a superior performance observed for the seasonal than for the relative threshold method, in terms of consistency and cropland season detection capability. Differences between the time series collections were analyzed by comparing the phenology metrics per crop type and year. Results suggest that L8-S2 combined LAI data streams with GPR led to a more precise detection of the start and end of growing seasons for most crop types, reaching an overall detection of 74% over the total planted crops versus 69% with S2 and 63% with L8 alone. Finally, the phenology mapping allowed us to evaluate the spatial and temporal evolution of the croplands over the agroecosystem in the Nile Delta. Full article

(This article belongs to the Special Issue Advancing Land Surface Phenological Analysis with High Spatial Resolution Imagery)

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27 pages, 11387 KiB

Open AccessArticle

Towards Routine Mapping of Crop Emergence within the Season Using the Harmonized Landsat and Sentinel-2 Dataset

by Feng Gao, Martha C. Anderson, David M. Johnson, Robert Seffrin, Brian Wardlow, Andy Suyker, Chunyuan Diao and Dawn M. Browning

Remote Sens. 2021, 13(24), 5074; https://doi.org/10.3390/rs13245074 - 14 Dec 2021

Cited by 15 | Viewed by 5048

Abstract

Crop emergence is a critical stage for crop development modeling, crop condition monitoring, and biomass accumulation estimation. Green-up dates (or the start of the season) detected from remote sensing time series are related to, but generally lag, crop emergence dates. In this paper, we refine the within-season emergence (WISE) algorithm and extend application to five Corn Belt states (Iowa, Illinois, Indiana, Minnesota, and Nebraska) using routine harmonized Landsat and Sentinel-2 (HLS) data from 2018 to 2020. Green-up dates detected from the HLS time series were assessed using field observations and near-surface measurements from PhenoCams. Statistical descriptions of green-up dates for corn and soybeans were generated and compared to county-level planting dates and district- to state-level crop emergence dates reported by the National Agricultural Statistics Service (NASS). Results show that emergence dates for corn and soybean can be reliably detected within the season using the HLS time series acquired during the early growing season. Compared to observed crop emergence dates, green-up dates from HLS using WISE were ~3 days later at the field scale (30-m). The mean absolute difference (MAD) was ~7 days and the root mean square error (RMSE) was ~9 days. At the state level, the mean differences between median HLS green-up date and median crop emergence date were within 2 days for 2018–2020. At this scale, MAD was within 4 days, and RMSE was less than 5 days for both corn and soybeans. The R-squares were 0.73 and 0.87 for corn and soybean, respectively. The 2019 late emergence of crops in Corn Belt states (1–4 weeks to five-year average) was captured by HLS green-up date retrievals. This study demonstrates that routine within-season mapping of crop emergence/green-up at the field scale is practicable over large regions using operational satellite data. The green-up map derived from HLS during the growing season provides valuable information on spatial and temporal variability in crop emergence that can be used for crop monitoring and refining agricultural statistics used in broad-scale modeling efforts. Full article

(This article belongs to the Special Issue Advancing Land Surface Phenological Analysis with High Spatial Resolution Imagery)

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19 pages, 13488 KiB

Open AccessArticle

Fusing Geostationary Satellite Observations with Harmonized Landsat-8 and Sentinel-2 Time Series for Monitoring Field-Scale Land Surface Phenology

by Yu Shen, Xiaoyang Zhang, Weile Wang, Ramakrishna Nemani, Yongchang Ye and Jianmin Wang

Remote Sens. 2021, 13(21), 4465; https://doi.org/10.3390/rs13214465 - 6 Nov 2021

Cited by 16 | Viewed by 3814

Abstract

Accurate and timely land surface phenology (LSP) provides essential information for investigating the responses of terrestrial ecosystems to climate changes and quantifying carbon and surface energy cycles on the Earth. LSP has been widely investigated using daily Visible Infrared Imaging Radiometer Suite (VIIRS) or Moderate Resolution Imaging Spectroradiometer (MODIS) observations, but the resultant phenometrics are frequently influenced by surface heterogeneity and persistent cloud contamination in the time series observations. Recently, LSP has been derived from Landsat-8 and Sentinel-2 time series providing detailed spatial pattern, but the results are of high uncertainties because of poor temporal resolution. With the availability of data from Advanced Baseline Imager (ABI) onboard a new generation of geostationary satellites that observe the earth every 10–15 min, daily cloud-free time series could be obtained with high opportunities. Therefore, this study investigates the generation of synthetic high spatiotemporal resolution time series by fusing the harmonized Landsat-8 and Sentinel-2 (HLS) time series with the temporal shape of ABI data for monitoring field-scale (30 m) LSP. The algorithm is verified by detecting the timings of greenup and senescence onsets around north Wisconsin/Michigan states, United States, where cloud cover is frequent during spring rainy season. The LSP detections from HLS-ABI are compared with those from HLS or ABI alone and are further evaluated using PhenoCam observations. The result indicates that (1) ABI could provide ~3 times more high-quality observations than HLS around spring greenup onset; (2) the greenup and senescence onsets derived from ABI and HLS-ABI are spatially consistent and statistically comparable with a median difference less than 1 and 10-days, respectively; (3) greenup and senescence onsets derived from HLS data show sharp boundaries around the orbit-overlapped areas and shifts of ~13 days delay and ~15 days ahead, respectively, relative to HLS-ABI detections; and (4) HLS-ABI greenup and senescence onsets align closely to PhenoCam observations with an absolute average difference of less than 2 days and 5 days, respectively, which are much better than phenology detections from ABI or HLS alone. The result suggests that the proposed approach could be implemented the monitor of 30 m LSP over regions with persistent cloud cover. Full article

(This article belongs to the Special Issue Advancing Land Surface Phenological Analysis with High Spatial Resolution Imagery)

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