Coupling Hyperspectral Remote Sensing Data with a Crop Model to Study Winter Wheat Water Demand

Abstract

Accurate information of crop growth conditions and water status can improve irrigation management. The objective of this study was to evaluate the performance of SAFYE (simple algorithm for yield and evapotranspiration estimation) crop model for simulating winter wheat growth and estimating water demand by assimilating leaf are index (LAI) derived from canopy reflectance measurements. A refined water stress function was used to account for high crop water stress. An experiment with nine irrigation scenarios corresponding to different levels of water supply was conducted over two consecutive winter wheat growing seasons (2013-2014 and 2014-2015). The calibration of four model parameters was based on the global optimization algorithms SCE-UA. Results showed that the estimated and retrieved LAI were in good agreement in most cases, with a minimum and maximum RMSE of 0.173 and 0.736, respectively. Good performance for accumulated biomass estimation was achieved under a moderate water stress condition while an underestimation occurred under a severe water stress condition. Grain yields were also well estimated for both years (R 2 = 0.83; RMSE = 0.48 t·ha −1 ; MRE = 8.4%). The dynamics of simulated soil moisture in the top 20 cm layer was consistent with field observations for all scenarios; whereas, a general underestimation was observed for total water storage in the 1 m layer, leading to an overestimation of the actual evapotranspiration. This research provides a scheme for estimating crop growth properties, grain yield and actual evapotranspiration by coupling crop model with remote sensing data.

Assimilation of LAI Derived from UAV Multispectral Data into the SAFY Model to Estimate Maize Yield

“…Compared with other crop models, SAFY is relatively simple and requires fewer parameters. However, SAFY can still simulate the physiological process of leaf growth and senescence [37,38]. The input parameters for SAFY include effective light utilization rate (ELUE), effective net radiation, water stress index, meteorological data, etc.…”

A Global Systematic Review of Improving Crop Model Estimations by Assimilating Remote Sensing Data: Implications for Small-Scale Agricultural Systems

“…About 44% of the studies reviewed applied data assimilation at the regional level, including the district and national levels [98,99], while 33% were conducted at the field level [100,101] (Tables A1-A3). A few studies (3%) were conducted at a sub-field scale, including plot and pixel levels [49,50]. In studies where data assimilation was applied at both the field and regional scales, two experiments were usually performed (20%) [66,102].…”

“…The studies reviewed have integrated RS data into many PBCMs to improve crop growth and yield (Tables A1-A3). These include WOrld FOod STudies (WOFOST) [45,46], Decision Support System for Agro-technology Transfer (DSSAT) [47,48], a Simple Algorithm For Yield (SAFY) [49,50], AquaCrop [51,52], and Soil Water Atmosphere Plant-WOrld FOod STudies (SWAP-WOFOST) [53,54]. Most of these studies used data assimilation to improve crop growth and yield estimates of staple crops (94%), consisting of maize, rice, soybeans, and wheat [55,56] (Tables A1-A3).…”

Modelagem de Variáveis Espectrais para Determinar as Condições Hídricas de Cafeeiros

“…Considerando estas contrariedades é necessário idealizar maneiras de determinar as condic ¸ões hídricas que sejam menos adversas. Estão presentes na literatura trabalhos que propõem mensurar as condic ¸ões hídricas da planta de maneira indireta, uma dessas formas é via assinatura espectral [1].…”