In this proposed study, for objective assessment of the GPM IMERG rainfall for crop yield forecast, we will use a crop simulation model (CSM). One of the main goals of CSM is to estimate agricultural production as a function of weather (e.g., rainfall and ambient temperature) and soil conditions as well as crop management. The CSM uses sets of differential equations, and calculate both rate and state variables over time, normally from planting until harvest maturity. The CSM will enable us to conduct robust analysis of the impact on crop yields due to GPM IMERG rainfall, GPM and SMAP synergy, interactions of GPM IMERG rainfall forcing with crop process dynamics (e.g., phenology, growth), and interactions among weather variables, and their overall influence on the crop.
We at Jet Propulsion Laboratory (JPL), for a NASA SERVIR project, have …show more content…
(Jones et al., 2003). The crop model is equipped with its own soil hydrology module that interacts with the crop component that simulates plant’s phenology, morphology and yield. Crop models are generally designed to run continuously from sowing until maturity or harvest – unless the crop fails due to extreme stress. However, to incorporate DSSAT in the modeling framework (Fig. 3), it is customized to stop when desired and restarts from that point in time (Ines et al., 2013; Das et al., 2011b). For this work, we will focus on three major crop modules (maize, wheat, rice) because of their economic values and being major crops for the world. These crops are cultivated in ~50% of the total cultivated land and 60% of total agricultural production (Leff et al., 2004; and Ramankutty and Foley, 1998). In particular, maize, wheat, and rice are dominant over more than two thirds of the croplands of the
We at Jet Propulsion Laboratory (JPL), for a NASA SERVIR project, have …show more content…
(Jones et al., 2003). The crop model is equipped with its own soil hydrology module that interacts with the crop component that simulates plant’s phenology, morphology and yield. Crop models are generally designed to run continuously from sowing until maturity or harvest – unless the crop fails due to extreme stress. However, to incorporate DSSAT in the modeling framework (Fig. 3), it is customized to stop when desired and restarts from that point in time (Ines et al., 2013; Das et al., 2011b). For this work, we will focus on three major crop modules (maize, wheat, rice) because of their economic values and being major crops for the world. These crops are cultivated in ~50% of the total cultivated land and 60% of total agricultural production (Leff et al., 2004; and Ramankutty and Foley, 1998). In particular, maize, wheat, and rice are dominant over more than two thirds of the croplands of the