code into LIS has not yet been officially released, but the AquaCrop source code can be found on the FAO website, https://www.fao.org/aquacrop/en/. The following repository includes the generic crop file and management file; https://doi.org/10.1002/2014MS000330. The water cloud model (WCM) calibration scripts can be found here: https://github.com/KUL‐RSDA/obs_operator_calibration. All data that were used for model input and evaluation are freely available online. Please visit the following links for data access. MERRA‐2 variables: https://disc.gsfc.nasa.gov/datasets?project=MERRA‐2(last access: 1 Jan 2022, Global Modeling and Assimilation office, 2015a, https://doi.org/10.5067/VJAFPLI1CSIV,
Open resource ↗KUL‐RSDA/obs_operator_calibration · pdf-layout-page:26 lines:1-26Unverified paper record
Assimilation of Sentinel‐1 Backscatter to Update AquaCrop Estimates of Soil Moisture and Crop Biomass
Journal of Geophysical Research: Biogeosciences · 1 Oct 2024 · 10.1029/2024jg008231
Abstract
Abstract This study assesses the potential of regional microwave backscatter data assimilation (DA) in AquaCrop for the first time, using NASA's Land Information System. The objective is to assess whether the assimilation setup can improve surface soil moisture (SSM) and crop biomass estimates. SSM and crop biomass simulations from AquaCrop were updated using Sentinel‐1 synthetic aperture radar observations, over three regions in Europe in two separate DA experiments. The first experiment concerned updating SSM using VV‐polarized backscatter and the corrections were propagated via the model to the biomass. In the second experiment, the DA setup was extended by also updating the biomass with VH‐polarized backscatter. SSM was evaluated with local in situ data and with downscaled Soil Moisture Active Passive (SMAP) retrievals for all cropland grid cells, whereas crop biomass was compared to SMAP vegetation optical depth and the Copernicus dry matter productivity. The assimilation showed mixed results for root mean square error and Pearson's correlation, with slight overall improvements in the (anomaly) correlations of updated SSM relative to independent in situ and satellite data. By contrast, the biomass estimates obtained with backscatter DA did not agree better with reference data sets. Overall, the SSM evaluation showed that there is potential in using Sentinel‐1 backscatter for assimilation in AquaCrop, but the present setup was not able to improve crop biomass estimates. Our study reveals how the complex interaction between SSM, crop biomass and backscatter affect the impact and performance of DA, offering insight into ways to optimize DA for crop growth estimation.
Plant phenotyping relevance
Sentinel-1後方散乱をAquaCropへ同化し、作物バイオマスを推定・独立データで評価する手法が研究の中心であり、植物形質の取得・推定方法を実質的に検証している。
abstractThe objective is to assess whether the assimilation setup can improve surface soil moisture (SSM) and crop biomass estimates.
abstractSSM and crop biomass simulations from AquaCrop were updated using Sentinel‐1 synthetic aperture radar observations
abstractcrop biomass was compared to SMAP vegetation optical depth and the Copernicus dry matter productivity.
Code and data availability
The Data Availability Statement points to the authors' public GitHub repository containing the water cloud model (WCM) calibration scripts used in this paper's Sentinel-1 backscatter forward-operator analysis. Other listed resources (AquaCrop source, MERRA-2, HWSD, CORINE, SMAP, VODsmap) are generic model code or third
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