Performance analysis of regional AquaCrop (v6.1) biomass and surface soil moisture simulations using satellite and in situ observations

<p>The current intensive use of agricultural land is affecting the land quality and contributes to climate change. Feeding the world's growing population under changing climatic conditions demands a global transition to more sustainable agricultural systems. This requires efficient models...

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Autores principales:	S. de Roos, G. J. M. De Lannoy, D. Raes
Formato:	article
Lenguaje:	EN
Publicado:	Copernicus Publications 2021
Materias:	Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/6279ad74594847e38396eab3837bee54
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spelling	oai:doaj.org-article:6279ad74594847e38396eab3837bee542021-11-30T13:18:12ZPerformance analysis of regional AquaCrop (v6.1) biomass and surface soil moisture simulations using satellite and in situ observations10.5194/gmd-14-7309-20211991-959X1991-9603https://doaj.org/article/6279ad74594847e38396eab3837bee542021-11-01T00:00:00Zhttps://gmd.copernicus.org/articles/14/7309/2021/gmd-14-7309-2021.pdfhttps://doaj.org/toc/1991-959Xhttps://doaj.org/toc/1991-9603<p>The current intensive use of agricultural land is affecting the land quality and contributes to climate change. Feeding the world's growing population under changing climatic conditions demands a global transition to more sustainable agricultural systems. This requires efficient models and data to monitor land cultivation practices at the field to global scale.</p> <p>This study outlines a spatially distributed version of the field-scale crop model AquaCrop version 6.1 to simulate agricultural biomass production and soil moisture variability over Europe at a relatively fine resolution of 30 arcsec (<span class="inline-formula">∼1</span> <span class="inline-formula">km</span>). A highly efficient parallel processing system is implemented to run the model regionally with global meteorological input data from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), soil textural information from the Harmonized World Soil Database version 1.2 (HWSDv1.2), and generic crop information. The setup with a generic crop is chosen as a baseline for a future satellite-based data assimilation system. The relative temporal variability in daily crop biomass production is evaluated with the Copernicus Global Land Service dry matter productivity (CGLS-DMP) data. Surface soil moisture is compared against NASA Soil Moisture Active–Passive surface soil moisture (SMAP-SSM) retrievals, the Copernicus Global Land Service surface soil moisture (CGLS-SSM) product derived from Sentinel-1, and in situ data from the International Soil Moisture Network (ISMN). Over central Europe, the regional AquaCrop model is able to capture the temporal variability in both biomass production and soil moisture, with a spatial mean temporal correlation of 0.8 (CGLS-DMP), 0.74 (SMAP-SSM), and 0.52 (CGLS-SSM). The higher performance when evaluating with SMAP-SSM compared to Sentinel-1 CGLS-SSM is largely due to the lower quality of CGLS-SSM satellite retrievals under growing vegetation. The regional model further captures the short-term and inter-annual variability, with a mean anomaly correlation of 0.46 for daily biomass and mean anomaly correlations of 0.65 (SMAP-SSM) and 0.50 (CGLS-SSM) for soil moisture. It is shown that soil textural characteristics and irrigated areas influence the model performance. Overall, the regional AquaCrop model adequately simulates crop production and soil moisture and provides a suitable setup for subsequent satellite-based data assimilation.</p>S. de RoosG. J. M. De LannoyD. RaesCopernicus PublicationsarticleGeologyQE1-996.5ENGeoscientific Model Development, Vol 14, Pp 7309-7328 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Geology QE1-996.5
spellingShingle	Geology QE1-996.5 S. de Roos G. J. M. De Lannoy D. Raes Performance analysis of regional AquaCrop (v6.1) biomass and surface soil moisture simulations using satellite and in situ observations
description	<p>The current intensive use of agricultural land is affecting the land quality and contributes to climate change. Feeding the world's growing population under changing climatic conditions demands a global transition to more sustainable agricultural systems. This requires efficient models and data to monitor land cultivation practices at the field to global scale.</p> <p>This study outlines a spatially distributed version of the field-scale crop model AquaCrop version 6.1 to simulate agricultural biomass production and soil moisture variability over Europe at a relatively fine resolution of 30 arcsec (<span class="inline-formula">∼1</span> <span class="inline-formula">km</span>). A highly efficient parallel processing system is implemented to run the model regionally with global meteorological input data from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), soil textural information from the Harmonized World Soil Database version 1.2 (HWSDv1.2), and generic crop information. The setup with a generic crop is chosen as a baseline for a future satellite-based data assimilation system. The relative temporal variability in daily crop biomass production is evaluated with the Copernicus Global Land Service dry matter productivity (CGLS-DMP) data. Surface soil moisture is compared against NASA Soil Moisture Active–Passive surface soil moisture (SMAP-SSM) retrievals, the Copernicus Global Land Service surface soil moisture (CGLS-SSM) product derived from Sentinel-1, and in situ data from the International Soil Moisture Network (ISMN). Over central Europe, the regional AquaCrop model is able to capture the temporal variability in both biomass production and soil moisture, with a spatial mean temporal correlation of 0.8 (CGLS-DMP), 0.74 (SMAP-SSM), and 0.52 (CGLS-SSM). The higher performance when evaluating with SMAP-SSM compared to Sentinel-1 CGLS-SSM is largely due to the lower quality of CGLS-SSM satellite retrievals under growing vegetation. The regional model further captures the short-term and inter-annual variability, with a mean anomaly correlation of 0.46 for daily biomass and mean anomaly correlations of 0.65 (SMAP-SSM) and 0.50 (CGLS-SSM) for soil moisture. It is shown that soil textural characteristics and irrigated areas influence the model performance. Overall, the regional AquaCrop model adequately simulates crop production and soil moisture and provides a suitable setup for subsequent satellite-based data assimilation.</p>
format	article
author	S. de Roos G. J. M. De Lannoy D. Raes
author_facet	S. de Roos G. J. M. De Lannoy D. Raes
author_sort	S. de Roos
title	Performance analysis of regional AquaCrop (v6.1) biomass and surface soil moisture simulations using satellite and in situ observations
title_short	Performance analysis of regional AquaCrop (v6.1) biomass and surface soil moisture simulations using satellite and in situ observations
title_full	Performance analysis of regional AquaCrop (v6.1) biomass and surface soil moisture simulations using satellite and in situ observations
title_fullStr	Performance analysis of regional AquaCrop (v6.1) biomass and surface soil moisture simulations using satellite and in situ observations
title_full_unstemmed	Performance analysis of regional AquaCrop (v6.1) biomass and surface soil moisture simulations using satellite and in situ observations
title_sort	performance analysis of regional aquacrop (v6.1) biomass and surface soil moisture simulations using satellite and in situ observations
publisher	Copernicus Publications
publishDate	2021
url	https://doaj.org/article/6279ad74594847e38396eab3837bee54
work_keys_str_mv	AT sderoos performanceanalysisofregionalaquacropv61biomassandsurfacesoilmoisturesimulationsusingsatelliteandinsituobservations AT gjmdelannoy performanceanalysisofregionalaquacropv61biomassandsurfacesoilmoisturesimulationsusingsatelliteandinsituobservations AT draes performanceanalysisofregionalaquacropv61biomassandsurfacesoilmoisturesimulationsusingsatelliteandinsituobservations
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Performance analysis of regional AquaCrop (v6.1) biomass and surface soil moisture simulations using satellite and in situ observations

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