Phenology of short vegetation cycles in a Kenyan rangeland from PlanetScope and Sentinel-2
The short revisit times afforded by recently-deployed optical satellite sensors that acquire 3–30 m resolution imagery provide new opportunities to study seasonal vegetation dynamics. Previous studies demonstrated a successful retrieval of phenology with Sentinel-2 for relatively stable annual growi...
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| Published in | Remote sensing of environment Vol. 248; p. 112004 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Elsevier Inc
01.10.2020
Elsevier BV |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0034-4257 1879-0704 |
| DOI | 10.1016/j.rse.2020.112004 |
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| Abstract | The short revisit times afforded by recently-deployed optical satellite sensors that acquire 3–30 m resolution imagery provide new opportunities to study seasonal vegetation dynamics. Previous studies demonstrated a successful retrieval of phenology with Sentinel-2 for relatively stable annual growing seasons. In semi-arid East Africa however, vegetation responds rapidly to a concentration of rainfall over short periods and consequently is subject to strong interannual variability. Obtaining a sufficient density of cloud-free acquisitions to accurately describe these short vegetation cycles is therefore challenging. The objective of this study is to evaluate if data from two satellite constellations, i.e., PlanetScope (3 m resolution) and Sentinel-2 (10 m resolution), each independently allow for accurate mapping of vegetation phenology under these challenging conditions. The study area is a rangeland with bimodal seasonality located at the 128-km2 Kapiti Farm in Machakos County, Kenya. Using all the available PlanetScope and Sentinel-2 imagery between March 2017 and February 2019, we derived temporal NDVI profiles and fitted double hyperbolic tangent models (equivalent to commonly-used logistic functions), separately for the two rainy seasons locally referred to as the short and long rains. We estimated start- and end-of-season for the series using a 50% threshold between minimum and maximum levels of the modelled time series (SOS50/EOS50). We compared our estimates against those obtained from vegetation index series from two alternative sources, i.e. a) greenness chromatic coordinate (GCC) series obtained from digital repeat photography, and b) MODIS NDVI. We found that both PlanetScope and Sentinel-2 series resulted in acceptable retrievals of phenology (RMSD of ~8 days for SOS50 and ~15 days for EOS50 when compared against GCC series) suggesting that the sensors individually provide sufficient temporal detail. However, when applying the model to the entire study area, fewer spatial artefacts occurred in the PlanetScope results. This could be explained by the higher observation frequency of PlanetScope, which becomes critical during periods of persistent cloud cover. We further illustrated that PlanetScope series could differentiate the phenology of individual trees from grassland surroundings, whereby tree green-up was found to be both earlier and later than for grass, depending on location. The spatially-detailed phenology retrievals, as achieved in this study, are expected to help in better understanding climate and degradation impacts on rangeland vegetation, particularly for heterogeneous rangeland systems with large interannual variability in phenology and productivity.
•Phenology was retrieved for multiple rainfall seasons from PlanetScope and Sentinel-2.•Retrievals were compared with greenness series from RGB-cameras and MODIS imagery.•PlanetScope resulted in phenology maps with fewer spatial artefacts than Sentinel-2.•Phenology patterns suggest that vegetation response to rainfall differs per season. |
|---|---|
| AbstractList | The short revisit times afforded by recently-deployed optical satellite sensors that acquire 3–30 m resolution imagery provide new opportunities to study seasonal vegetation dynamics. Previous studies demonstrated a successful retrieval of phenology with Sentinel-2 for relatively stable annual growing seasons. In semi-arid East Africa however, vegetation responds rapidly to a concentration of rainfall over short periods and consequently is subject to strong interannual variability. Obtaining a sufficient density of cloud-free acquisitions to accurately describe these short vegetation cycles is therefore challenging. The objective of this study is to evaluate if data from two satellite constellations, i.e., PlanetScope (3 m resolution) and Sentinel-2 (10 m resolution), each independently allow for accurate mapping of vegetation phenology under these challenging conditions. The study area is a rangeland with bimodal seasonality located at the 128-km² Kapiti Farm in Machakos County, Kenya. Using all the available PlanetScope and Sentinel-2 imagery between March 2017 and February 2019, we derived temporal NDVI profiles and fitted double hyperbolic tangent models (equivalent to commonly-used logistic functions), separately for the two rainy seasons locally referred to as the short and long rains. We estimated start- and end-of-season for the series using a 50% threshold between minimum and maximum levels of the modelled time series (SOS₅₀/EOS₅₀). We compared our estimates against those obtained from vegetation index series from two alternative sources, i.e. a) greenness chromatic coordinate (GCC) series obtained from digital repeat photography, and b) MODIS NDVI. We found that both PlanetScope and Sentinel-2 series resulted in acceptable retrievals of phenology (RMSD of ~8 days for SOS₅₀ and ~15 days for EOS₅₀ when compared against GCC series) suggesting that the sensors individually provide sufficient temporal detail. However, when applying the model to the entire study area, fewer spatial artefacts occurred in the PlanetScope results. This could be explained by the higher observation frequency of PlanetScope, which becomes critical during periods of persistent cloud cover. We further illustrated that PlanetScope series could differentiate the phenology of individual trees from grassland surroundings, whereby tree green-up was found to be both earlier and later than for grass, depending on location. The spatially-detailed phenology retrievals, as achieved in this study, are expected to help in better understanding climate and degradation impacts on rangeland vegetation, particularly for heterogeneous rangeland systems with large interannual variability in phenology and productivity. The short revisit times afforded by recently-deployed optical satellite sensors that acquire 3–30 m resolution imagery provide new opportunities to study seasonal vegetation dynamics. Previous studies demonstrated a successful retrieval of phenology with Sentinel-2 for relatively stable annual growing seasons. In semi-arid East Africa however, vegetation responds rapidly to a concentration of rainfall over short periods and consequently is subject to strong interannual variability. Obtaining a sufficient density of cloud-free acquisitions to accurately describe these short vegetation cycles is therefore challenging. The objective of this study is to evaluate if data from two satellite constellations, i.e., PlanetScope (3 m resolution) and Sentinel-2 (10 m resolution), each independently allow for accurate mapping of vegetation phenology under these challenging conditions. The study area is a rangeland with bimodal seasonality located at the 128-km2 Kapiti Farm in Machakos County, Kenya. Using all the available PlanetScope and Sentinel-2 imagery between March 2017 and February 2019, we derived temporal NDVI profiles and fitted double hyperbolic tangent models (equivalent to commonly-used logistic functions), separately for the two rainy seasons locally referred to as the short and long rains. We estimated start- and end-of-season for the series using a 50% threshold between minimum and maximum levels of the modelled time series (SOS50/EOS50). We compared our estimates against those obtained from vegetation index series from two alternative sources, i.e. a) greenness chromatic coordinate (GCC) series obtained from digital repeat photography, and b) MODIS NDVI. We found that both PlanetScope and Sentinel-2 series resulted in acceptable retrievals of phenology (RMSD of ~8 days for SOS50 and ~15 days for EOS50 when compared against GCC series) suggesting that the sensors individually provide sufficient temporal detail. However, when applying the model to the entire study area, fewer spatial artefacts occurred in the PlanetScope results. This could be explained by the higher observation frequency of PlanetScope, which becomes critical during periods of persistent cloud cover. We further illustrated that PlanetScope series could differentiate the phenology of individual trees from grassland surroundings, whereby tree green-up was found to be both earlier and later than for grass, depending on location. The spatially-detailed phenology retrievals, as achieved in this study, are expected to help in better understanding climate and degradation impacts on rangeland vegetation, particularly for heterogeneous rangeland systems with large interannual variability in phenology and productivity. The short revisit times afforded by recently-deployed optical satellite sensors that acquire 3–30 m resolution imagery provide new opportunities to study seasonal vegetation dynamics. Previous studies demonstrated a successful retrieval of phenology with Sentinel-2 for relatively stable annual growing seasons. In semi-arid East Africa however, vegetation responds rapidly to a concentration of rainfall over short periods and consequently is subject to strong interannual variability. Obtaining a sufficient density of cloud-free acquisitions to accurately describe these short vegetation cycles is therefore challenging. The objective of this study is to evaluate if data from two satellite constellations, i.e., PlanetScope (3 m resolution) and Sentinel-2 (10 m resolution), each independently allow for accurate mapping of vegetation phenology under these challenging conditions. The study area is a rangeland with bimodal seasonality located at the 128-km2 Kapiti Farm in Machakos County, Kenya. Using all the available PlanetScope and Sentinel-2 imagery between March 2017 and February 2019, we derived temporal NDVI profiles and fitted double hyperbolic tangent models (equivalent to commonly-used logistic functions), separately for the two rainy seasons locally referred to as the short and long rains. We estimated start- and end-of-season for the series using a 50% threshold between minimum and maximum levels of the modelled time series (SOS50/EOS50). We compared our estimates against those obtained from vegetation index series from two alternative sources, i.e. a) greenness chromatic coordinate (GCC) series obtained from digital repeat photography, and b) MODIS NDVI. We found that both PlanetScope and Sentinel-2 series resulted in acceptable retrievals of phenology (RMSD of ~8 days for SOS50 and ~15 days for EOS50 when compared against GCC series) suggesting that the sensors individually provide sufficient temporal detail. However, when applying the model to the entire study area, fewer spatial artefacts occurred in the PlanetScope results. This could be explained by the higher observation frequency of PlanetScope, which becomes critical during periods of persistent cloud cover. We further illustrated that PlanetScope series could differentiate the phenology of individual trees from grassland surroundings, whereby tree green-up was found to be both earlier and later than for grass, depending on location. The spatially-detailed phenology retrievals, as achieved in this study, are expected to help in better understanding climate and degradation impacts on rangeland vegetation, particularly for heterogeneous rangeland systems with large interannual variability in phenology and productivity. •Phenology was retrieved for multiple rainfall seasons from PlanetScope and Sentinel-2.•Retrievals were compared with greenness series from RGB-cameras and MODIS imagery.•PlanetScope resulted in phenology maps with fewer spatial artefacts than Sentinel-2.•Phenology patterns suggest that vegetation response to rainfall differs per season. |
| ArticleNumber | 112004 |
| Author | Fava, Francesco Gachoki, Stella Cheng, Yan Vrieling, Anton Marshall, Michael Meroni, Michele |
| Author_xml | – sequence: 1 givenname: Yan orcidid: 0000-0001-8658-4673 surname: Cheng fullname: Cheng, Yan organization: University of Twente, Faculty of Geo-information Science and Earth Observation, P.O. Box 217, 7500 AE Enschede, the Netherlands – sequence: 2 givenname: Anton orcidid: 0000-0002-7979-1540 surname: Vrieling fullname: Vrieling, Anton email: a.vrieling@utwente.nl organization: University of Twente, Faculty of Geo-information Science and Earth Observation, P.O. Box 217, 7500 AE Enschede, the Netherlands – sequence: 3 givenname: Francesco surname: Fava fullname: Fava, Francesco organization: International Livestock Research Institute (ILRI), P.O. Box 30709, Nairobi 00100, Kenya – sequence: 4 givenname: Michele surname: Meroni fullname: Meroni, Michele organization: European Commission, Joint Research Centre (JRC), Via E. Fermi 2749, I-21027 Ispra, VA, Italy – sequence: 5 givenname: Michael orcidid: 0000-0002-9738-5036 surname: Marshall fullname: Marshall, Michael organization: University of Twente, Faculty of Geo-information Science and Earth Observation, P.O. Box 217, 7500 AE Enschede, the Netherlands – sequence: 6 givenname: Stella surname: Gachoki fullname: Gachoki, Stella organization: University of Twente, Faculty of Geo-information Science and Earth Observation, P.O. Box 217, 7500 AE Enschede, the Netherlands |
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| Keywords | Digital repeat photography NDVI time series Phenology Multi-temporal analysis PlanetScope Sentinel-2 Landscape variability Semi-arid rangelands Spatial resolution |
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| ParticipantIDs | proquest_miscellaneous_2552038864 proquest_journals_2451172031 crossref_primary_10_1016_j_rse_2020_112004 crossref_citationtrail_10_1016_j_rse_2020_112004 elsevier_sciencedirect_doi_10_1016_j_rse_2020_112004 |
| PublicationCentury | 2000 |
| PublicationDate | October 2020 2020-10-00 20201001 |
| PublicationDateYYYYMMDD | 2020-10-01 |
| PublicationDate_xml | – month: 10 year: 2020 text: October 2020 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | Remote sensing of environment |
| PublicationYear | 2020 |
| Publisher | Elsevier Inc Elsevier BV |
| Publisher_xml | – name: Elsevier Inc – name: Elsevier BV |
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| SubjectTerms | Aridity climate Climate and vegetation Cloud cover Digital repeat photography environment farms grasses Grasslands Growing season Herbivores Image acquisition Interannual variability Kenya Landscape variability Mapping Multi-temporal analysis NDVI time series Phenology Photography PlanetScope rain Rainfall Rainy season Rangelands Satellite constellations satellites Seasonal variations Semi-arid rangelands Sensors Sentinel-2 Spatial resolution time series analysis Vegetation Vegetation index Vegetation surveys |
| Title | Phenology of short vegetation cycles in a Kenyan rangeland from PlanetScope and Sentinel-2 |
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