Evaluating Sentinel-2 based phenology and productivity data for crop monitoring in Swedish agricultural fields
(2022) In Student thesis series INES NGEM01 20221Dept of Physical Geography and Ecosystem Science
- Abstract
- Sentinel-2 based data provides interesting opportunities for land monitoring and applications in agriculture. High-Resolution Vegetation Phenology and Productivity (HR-VPP) is derived from Sentinel-2 data and offers up to 13 metrics. These metrics have a high spatial resolution (10x10m) and enables the possibility for in-field agricultural monitoring. The aim of this study is to evaluate four of these metrics namely, start of season, end of season, green-up and seasonal productivity in regard to variation between fields and within. Additionally, the parameters correlation with monthly weather data on radiation, temperature and precipitation is computed. The parameters correlation with the resulting yield is also computed on both a county... (More)
- Sentinel-2 based data provides interesting opportunities for land monitoring and applications in agriculture. High-Resolution Vegetation Phenology and Productivity (HR-VPP) is derived from Sentinel-2 data and offers up to 13 metrics. These metrics have a high spatial resolution (10x10m) and enables the possibility for in-field agricultural monitoring. The aim of this study is to evaluate four of these metrics namely, start of season, end of season, green-up and seasonal productivity in regard to variation between fields and within. Additionally, the parameters correlation with monthly weather data on radiation, temperature and precipitation is computed. The parameters correlation with the resulting yield is also computed on both a county level and a field level. Two counties are studied, Skåne county and Västra Götaland county. Several crops with different characteristics are included in the analysis. These include winter wheat, winter rape, oats, spring barley, field bean and potato. A large variation in start of season is seen for the winter crops while the spring crops are quite stable from year to year. The highest variability between and within-fields is found in the parameters green-up and seasonal productivity. Monthly weather variables have a weak but significant correlation for most parameters and months for both spring barley and winter wheat. The correlation between yield and HR-VPP parameters indicate that seasonal productivity has a strong correlation with the resulting yield. Green-up has a stronger correlation with the yield of spring crops than winter crops. There are no large regional differences between the two counties. The higher variability in winter crops is partly accredited to less influence of agricultural practices but also to difficulties in determining the season start. Green-up’s correlation with the yield for spring crops is stronger and a possible reason could be green-up’s dependence on a correct identification of the season start. Weak correlations with weather data are expected as not one single factor can explain the spatial variability of the parameters. It is likely a combination of several weather factors and the practices of the farmer. The resulting yield has a strong correlation with seasonal productivity for all the crops in the study, which could mean that the seasonal productivity could be used in agricultural monitoring. However, the usefulness of the HR-VPP would probably be increased if finetuned for agricultural fields performed instead of being calibrated for all vegetation types. (Less)
- Popular Abstract (Swedish)
- Högupplöst satellitdata erbjuder intressanta möjligheter för jordbrukssektorn och för precisionsodling. Sentinel-2 är ett jordobservationssystem som med sin höga rumsliga upplösning (10x10m) möjliggör analyser även inom jordbruksfält. En produkt framställd från Sentinel-2 är fenologi och produktivitetsdata från High-Resolution Vegetation Phenology and Productivity (HR-VPP). HR-VPP är en produkt med 13 olika parametrar såsom säsongstart, säsongslut men även säsongsproduktivitet. Fenologi är term för återkommande händelser i naturen såsom växters tillväxt men även djurs reproduktion och migration.
Syftet med denna studie är att utvärdera fyra av dessa parametrar från HR-VPP, nämligen säsongsstart (SOS), säsongsslut (EOS), lutning på... (More) - Högupplöst satellitdata erbjuder intressanta möjligheter för jordbrukssektorn och för precisionsodling. Sentinel-2 är ett jordobservationssystem som med sin höga rumsliga upplösning (10x10m) möjliggör analyser även inom jordbruksfält. En produkt framställd från Sentinel-2 är fenologi och produktivitetsdata från High-Resolution Vegetation Phenology and Productivity (HR-VPP). HR-VPP är en produkt med 13 olika parametrar såsom säsongstart, säsongslut men även säsongsproduktivitet. Fenologi är term för återkommande händelser i naturen såsom växters tillväxt men även djurs reproduktion och migration.
Syftet med denna studie är att utvärdera fyra av dessa parametrar från HR-VPP, nämligen säsongsstart (SOS), säsongsslut (EOS), lutning på grönhet (GRUP) och säsongsproduktivitet (SPRO) med avseende på variation mellan fält och inom. Dessutom beräknas parametrarnas korrelation med månatliga värden på strålning, temperatur och nederbörd. Parametrarnas korrelation med den resulterande avkastningen tas också fram både på länsnivå och fältnivå. Flera grödor ingick i analysen. Dessa inkluderar höstvete, höstraps, havre, vårkorn, åkerböna och potatis.
En stor variation i SOS ses för vintergrödorna medan vårgrödorna är ganska stabila från år till år. Den största variationen mellan och inom fält finns i parametrarna GRUP och SPRO. Månatliga vädervariabler har en svag men signifikant korrelation för de flesta parametrar och månader för både vårkorn och höstvete. Korrelationen mellan avkastning och HR-VPP parametrar indikerar att SPRO har en stark korrelation med den resulterande avkastningen. GRUP har en starkare korrelation med vårgrödor än vintergrödor. Det finns inga stora regionala skillnader mellan Skåne län och Västra Götaland län. Högre variation i SOS vintergrödor beror delvis på mindre påverkan från jordbruksmetoder men också på grund av svårigheter med att bestämma SOS. GRUPs korrelation med avkastningen för vårgrödor är starkare och en möjlig orsak kan vara GRUPs beroende av en korrekt identifiering av SOS. Svaga korrelationer med väderdata förväntas eftersom inte en enskild faktor kan förklara parametrarnas rumsliga variabilitet. Det är sannolikt en kombination av flera faktorer och jordbrukarens metoder. Den resulterande avkastningen har ett starkt samband med säsongsproduktiviteten för alla grödor i studien, vilket kan innebära att säsongsproduktiviteten skulle kunna användas i jordbruksövervakning. Användbarheten av HR-VPP skulle dock troligen öka om den finjusteras för grödor i stället för att justeras för alla vegetationstyper. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9094408
- author
- Erlström, Daniel LU
- supervisor
-
- Lars Eklundh LU
- Per-Ola Olsson LU
- organization
- course
- NGEM01 20221
- year
- 2022
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Geography, Phenology, Agriculture, Yield, Remote Sensing, Sentinel-2, Productivity
- publication/series
- Student thesis series INES
- report number
- 578
- language
- English
- id
- 9094408
- date added to LUP
- 2022-06-29 13:07:39
- date last changed
- 2022-06-29 13:07:39
@misc{9094408,
abstract = {{Sentinel-2 based data provides interesting opportunities for land monitoring and applications in agriculture. High-Resolution Vegetation Phenology and Productivity (HR-VPP) is derived from Sentinel-2 data and offers up to 13 metrics. These metrics have a high spatial resolution (10x10m) and enables the possibility for in-field agricultural monitoring. The aim of this study is to evaluate four of these metrics namely, start of season, end of season, green-up and seasonal productivity in regard to variation between fields and within. Additionally, the parameters correlation with monthly weather data on radiation, temperature and precipitation is computed. The parameters correlation with the resulting yield is also computed on both a county level and a field level. Two counties are studied, Skåne county and Västra Götaland county. Several crops with different characteristics are included in the analysis. These include winter wheat, winter rape, oats, spring barley, field bean and potato. A large variation in start of season is seen for the winter crops while the spring crops are quite stable from year to year. The highest variability between and within-fields is found in the parameters green-up and seasonal productivity. Monthly weather variables have a weak but significant correlation for most parameters and months for both spring barley and winter wheat. The correlation between yield and HR-VPP parameters indicate that seasonal productivity has a strong correlation with the resulting yield. Green-up has a stronger correlation with the yield of spring crops than winter crops. There are no large regional differences between the two counties. The higher variability in winter crops is partly accredited to less influence of agricultural practices but also to difficulties in determining the season start. Green-up’s correlation with the yield for spring crops is stronger and a possible reason could be green-up’s dependence on a correct identification of the season start. Weak correlations with weather data are expected as not one single factor can explain the spatial variability of the parameters. It is likely a combination of several weather factors and the practices of the farmer. The resulting yield has a strong correlation with seasonal productivity for all the crops in the study, which could mean that the seasonal productivity could be used in agricultural monitoring. However, the usefulness of the HR-VPP would probably be increased if finetuned for agricultural fields performed instead of being calibrated for all vegetation types.}},
author = {{Erlström, Daniel}},
language = {{eng}},
note = {{Student Paper}},
series = {{Student thesis series INES}},
title = {{Evaluating Sentinel-2 based phenology and productivity data for crop monitoring in Swedish agricultural fields}},
year = {{2022}},
}