Mapping soybean planting area in midwest Brazil with remotely sensed images and phenologybased algorithm using the Google Earth Engine platform Abstract   Soybean is the main crop of the Brazilian agribusiness. The nearrealtime monitoring of this crop is important in the production estimate, identification of the progress, and location of the crops. It is also crucial for governmental surveillance institutions regarding sanitary break. Thus, this study aimed to estimate and map soybean areas in almost real time using temporal series multispectral images and vegetation indices (nearinfrared and red) in the Google Earth Engine system in the state of Mato Grosso, Brazil. A multitemporal algorithm of the Perpendicular Vegetation Index (PVI) of MODIS, OLI, and MSI images of the 20162017 crop yrxe2x88x921 was created from the identification of soybean areas using the Perpendicular Crop Enhancement Index (PCEI). The use of the MODIS images for the monitoring of soybean areas using the Google Earth Engine platform was a viable and promising automated alternative for largescale soybean area estimates.

Remote Sensing Data Assimilation in Dynamic Crop Models Using Particle Swarm Optimization A growing world population, increasing prosperity in emerging countries, and shifts in energy and food demands necessitate a continuous increase in global agricultural production. Simultaneously, risks of extreme weather events and a slowing productivity growth in recent years has caused concerns about meeting the demands in the future. Crop monitoring and timely yield predictions are an important tool to mitigate risk and ensure food security. A common approach is to combine the temporal simulation of dynamic crop models with a geospatial component by assimilating remote sensing data. To ensure reliable assimilation, handling of uncertainties in both models and the assimilated input data is crucial. Here, we present a new approach for data assimilation using particle swarm optimization (PSO) in combination with statistical distance metrics that allow for flexible handling of model and input uncertainties. We explored the potential of the newly proposed method in a case study by assimilating canopy cover (CC) information, obtained from Sentinel2 data, into the AquaCropOS model to improve winter wheat yield estimation on the pixel and fieldlevel and compared the performance with two other methods (simple updating and extended Kalman filter). Our results indicate that the performance of the new method is superior to simple updating and similar or better than the extended Kalman filter updating. Furthermore, it was particularly successful in reducing bias in yield estimation.