Temporal mapping and analysis

A compositing process for selecting spatial data collected over a period of time, creating temporal data cubes from the spatial data, and processing and/or analyzing the data using temporal mapping algebra functions. In some embodiments, the temporal data cube is creating a masked cube using the data cubes, and computing a composite from the masked cube by using temporal mapping algebra

Using remote sensing and grid-based meteorological datasets for regional soybean crop yield prediction and crop monitoring

Regional crop yield estimations using crop models is a national priority due to its contributions to crop security assessment and food pricing policies. Many of these crop yield assessments are performed using time-consuming, intensive field surveys. This research was initiated to test the applicability of remote sensing and grid-based meteorological model data for providing improved and efficient predictive capabilities for crop bio-productivity. The soybean prediction model (Sinclair model) used in this research, requires daily data inputs to simulate yield which are temperature, precipitation, solar radiation, day length initialization of certain soil moisture parameters for each model run. The traditional meteorological datasets were compared with simulated South American Land Data Assimilation System (SALDAS) meteorological datasets for Sinclair model runs and for initializing soil moisture inputs. Considering the fact that grid-based meteorological data has the resolution of 1/8th of a degree, the estimations demonstrated a reasonable accuracy level and showed promise for increase in efficiency for regional level yield predictions. The research tested daily composited Normalized Difference Vegetation Index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor (both AQUA and TERRA platform) and simulated Visible/Infrared Imager Radiometer Suite (VIIRS) sensor product (a new sensor planned to be launched in the near future) for crop growth and development based on phenological events. The AQUA and TERRA fusion based daily MODIS NDVI was utilized to developed a planting date estimation method. The results have shown that daily MODIS composited NDVI values have the capability for enhanced monitoring of the soybean crop growth and development with respect to soybean growth and development. The method was able to predict planting date within ±3.4 days. A geoprocessing framework for extracting data from the grid data sources was developed. Overall, this study was able to demonstrate the utility of MODIS and VIIRS NDVI datasets and SALDAS meteorological data for providing effective inputs to crop yield models and the ability to provide an effective remote sensing-based regional crop monitoring. The utilization of these datasets helps in eliminating the ground-based data collection, which improves cost and time efficiency and also provides capability for regional crop monitoring

CONSIDERATION AND COMPARISON OF DIFFERENT REMOTE SENSING INPUTS FOR REGIONAL CROP YIELD PREDICTION MODEL

ABSTRACT Regional crop yield prediction methods can be enhanced by the use of remote sensing based inputs to obtain an efficient and timely prediction capability. Inputs from remote sensing usually include vegetation indices and climatic information such as temperature, precipitation, solar radiation etc. The crop selected for this study is soybean. This study focuses on investigating and comparing a combination of satellite sensor characteristics and data products derived from satellite data stream inputs, with crop modeling input data requirements. The factors to be considered include the spatial, spectral and temporal characteristics of sensor characteristics and derived data products to determine objective methods for selecting model inputs that offer the most promise to improve regional soybean yield prediction