In this paper, novel geospatial services are presented which are able to process on the server-side numerous remote sensing data based on big data frameworks like Hadoop and Rasdaman. The developed system itself features several software modules that orchestrate the different image processing algorithms responsible for the production of consistent value-added maps like canopy greenness and leaf area index. Through distributed multitemporal analysis, the entire crop growth cycle can be continuously monitored through the analysis of time-series observations. These observations cover multiple crop growth cycles, offering invaluable information by linking weather statistical data with the start, the end and the duration of each growth cycle enabling critical decisions by direct comparison with the current crop growth state.

Weed distribution maps can be developed from remotely sensed reflectance data if collected at appropriate times during the growing season. The research objectives were to determine if and when reflectance could be used to distinguish between weed-free and weed-infested (mixed species) areas in soybean and to determine the most useful wavebands to separate crop, weed, and soil reflectance differences. Treatments included no vegetation (bare soil), weed-free soybean, and weed-infested soybean and, in 1 yr, 80% corn residue cover. Reflectance was measured at several sampling times from May through September in 2001 and 2002 using a handheld multispectral radiometer equipped with band-limited optical interference filters (460 to 1,650 nm). The spatial resolution was 0.8 m2. The reflectance in the visible spectral range (460 to 700 nm) generally was similar among treatments. In the near-infrared (NIR) range (> 700 to 1,650 nm), differences among treatments were observed from soybean growth stage V-3 (about 4 wk after planting) until mid-July to early August depending on crop vigor and canopy closure (76-cm row spacing in 2001 and 19-cm row spacing in 2002). Reflectance rankings in the NIR range when treatments could be differentiated were consistent between years and, from lowest to highest reflectance, were soil < weed-free < weed-infested areas. Increased reflectance from weed-infested areas was most likely due to increased biomass and canopy cover. Residue masked differences between weed-free and weed-infested areas during the early stages of growth due to high reflectance from the residue and reduced weed numbers in these areas. These results suggest that NIR spectral reflectance collected before canopy closure can be used to distinguish weed-infested from weed-free areas.