SBIR Phase I: Dynamic OneSource Geospatial Information System for Maximizing Agricultural Yields

这个小企业创新研究（SBIR）第一阶段项目的更广泛/商业影响是创建一个动态的地理空间数据库，可以挖掘，以进一步开发土壤水分和农业生产力的精确模型，这将有助于中小型农场。这些农场覆盖了美国近75%的运营农田，农业行业的大型公司受益于数字农场数据的收集，然而，小型农场由于无法获得农业数字创新系统而处于不利地位。提供及时的数字信息作为对田间条件的投入，将有助于中小型农民优化产量，并有可能创造更大的收入。拟议的数据库将把多尺度和多变量成像和非成像地理空间和气象数据纳入单一来源。将卫星地球空间数据与航空地球空间数据合并是一项具有挑战性的任务，将提高地球科学数据的准确性。该技术的潜在应用包括中长期粮食安全规划、干旱缓解、土壤保护、多样化以及扩大气候适应性和可持续农业。SBIR第一阶段项目的重点是开发一个综合的、单一来源的动态地理空间数据库以及精确的土壤湿度和农业生产力预测模型，以造福中小型农场。数据输入包括：㈠全球导航卫星系统在新设计的大地测量/气象网络上进行观测，以大大提高精度; ㈡固定翼平台提供的机载数字地理空间数据，以捕捉高精度和分辨率的天底多光谱和彩色倾斜图像、激光雷达点云数据，以及无人驾驶航空系统提供的数据，以捕捉季节性高光谱图像;商业卫星高分辨率图象的卫星遥感数据;与全球降水测量使命的多卫星检索相结合的每小时全球降水测量的气象卫星数据;和（v）该奖项反映了NSF的法定使命，通过使用基金会的知识价值和更广泛的影响审查标准进行评估，

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is the creation of a dynamic geospatial database that can be mined to further develop precision models of soil moisture and farm productivity that will aid small- to medium-sized farms. These farms cover almost 75% of the operating farmland in the U.S. Large companies in the agriculture industry benefit from the collection of digital farm data however, smaller farms are disadvantaged by the inaccessibility of agricultural digital innovation systems. The availability of timely digital information as inputs to field conditions will help small- to mid-sized farmers optimize their yields and potentially generate greater revenue. The proposed database will integrate multiscale and multivariate imaging and non-imaging geospatial and meteorological data into a single source. The merging of satellite-based geospatial data with airborne-geospatial data, a challenging task, will improve the accuracy of earth science data. Potential applications of the technology include medium- to long-term food security planning, drought mitigation, soil conservation, diversification, and expansion of climate-resilient and sustainable farming.This SBIR Phase I project focuses on developing an integrated, one-source, dynamic geospatial database as well as precision soil moisture and farm productivity forecasting models to benefit small- and medium-size farms. The data inputs include (i) global navigation satellite system observations on a newly designed geodetic/meteorological network for significantly increased accuracy; (ii) airborne digital geospatial data from a fixed-wing platform to capture high precision and resolution nadir multispectral and color oblique imageries, lidar point cloud data, and data from un-manned aerial systems to capture seasonal hyperspectral imageries; (iii) satellite remote sensing data from commercial satellite high-resolution imageries; (iv) weather satellite data for hourly global precipitation measurements (GPM) fused with multi-satellite retrievals for global precipitation measurement mission (GPM); and (v) terrestrial data from existing geographic information systems and meteorological stations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.