Assessing the Impact of LAI Data Assimilation on Simulations of the Soil Water Balance and Maize Development Using MOHID-Land

Abstract

Hydrological modeling at the catchment scale requires the upscaling of many input parameters for better characterizing landscape heterogeneity, including soil, land use and climate variability. In this sense, remote sensing is often considered as a practical solution. This study aimed to access the impact of assimilation of leaf area index (LAI) data derived from Landsat 8 imagery on MOHID-Land’s simulations of the soil water balance and maize state variables (LAI, canopy height, aboveground dry biomass and yield). Data assimilation impacts on final model results were first assessed by comparing distinct modeling approaches to measured data. Then, the uncertainty related to assimilated LAI values was quantified on final model results using a Monte Carlo method. While LAI assimilation improved MOHID-Land’s estimates of the soil water balance and simulations of crop state variables during early stages, it was never sufficient to overcome the absence of a local calibrated crop dataset. Final model estimates further showed great uncertainty for LAI assimilated values during earlier crop stages, decreasing then with season reaching its end. Thus, while model simulations can be improved using LAI data assimilation, additional data sources should be considered for complementing crop parameterization.

Landsat and Sentinel-2 images as a tool for the effective estimation of winter and spring cultivar growth and yield prediction in the Czech Republic

“…Yield is the most complex and important target in field crops, and challenges still remain in precisely describing this and improving accurate predictions from seasonal data Nearing et al, 2010;Thorp et al, 2001). Further research is required to define the most suitable sensor platform which will improve soil and crop management in different geographical conditions (Flynn et al, 2020).…”

Innovation Issues in Water, Agriculture and Food

“…The model separately characterizes evaporation fluxes from the soil and transpiration from the trees and its practical application requires adequate parametrization of both sources of fluxes, the soil and the trees, thus the stomatal resistance and leaf area index of olive orchards under different management practices. Ramos et al [9] describe the impact of assimilation of leaf area index (LAI) data derived from Landsat 8 imagery on MOHID-Land's simulations of the soil water balance and maize state variables using data collected in southern Portugal. The main conclusion is that the implementation of the MOHID-Land model at the regional scale cannot depend solely on inputs from the LAI data assimilation because estimates may diverge substantially from the reality, thus confirming the need to use a proper data set for calibration.…”

Modelação da rega deficitária em vinha com o MOHID-Land

“…Para o período de validação (2018), os erros das estimativas mantiveram-se na mesma ordem de grandeza dos obtidos durante a calibração do modelo (RMSE = 0.029 m 3 m -3 ; NRMSE = 0.012), o modelo subestimou os dados observados em 3.25%, enquanto que a EF aumentou consideravelmente (EF = 0.706). Considerou-se, assim, que o modelo MOHID-Land conseguiu reproduzir com sucesso os valores do teor de água no solo observados na rega da vinha durante dois anos, apresentando indicadores estatísticos semelhantes aos obtidos noutras aplicações do mesmo género [4,20,21].…”

“…PBIAS foi de 7.098% revelando alguma subestimação dos valores obtidos por deteção remota e a EF foi de 0.602. O ajustamento do modelo foi, portanto, aceitável, embora os indicadores estatísticos sejam inferiores ao obtidos por Ramos et al[4,20] para a cultura do milho. Neste caso, os valores de LAI foram medidos diretamente no campo e não derivados das imagens de satélite, o que poderá justificar o pior ajustamento aqui conseguido.…”