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

小型企业创新研究(SBIR)第一阶段项目的更广泛/商业影响是创建一个动态地理空间数据库，可以挖掘该数据库以进一步开发土壤湿度和农场生产力的精确模型，以帮助中小型农场。这些农场覆盖了美国近75%的经营农田。农业行业的大公司受益于数字农场数据的收集，但较小的农场因无法访问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田间条件的投入，将有助于中小型农民优化产量，并有可能产生更多的收入。拟议的数据库将把多比例尺和多变量成像和非成像地理空间和气象数据整合为一个单一来源。星载地理空间数据与机载地理空间数据的融合是一项具有挑战性的任务，将提高地球科学数据的精度。该技术的潜在应用包括中长期粮食安全规划、抗旱减灾、土壤保持、多样化，以及气候适应性和可持续农业的推广。这项SBIR第一阶段项目的重点是开发一个集成的、单一来源的动态地理空间数据库以及精确的土壤水分和农场生产力预测模型，以使中小型农场受益。数据输入包括：(1)全球导航卫星系统在新设计的大地测量/气象网络上的观测，以大大提高精度；(2)来自固定翼平台的机载数字地理空间数据，以获取高精度和分辨率的最低点多光谱和彩色斜面图像、激光雷达点云数据和来自无人驾驶航空系统的数据，以获取季节性高光谱图像；(3)来自商业卫星高分辨率图像的卫星遥感数据；(4)用于每小时全球降水测量的气象卫星数据，与用于全球降水测量任务的多卫星反演相融合；和(V)来自现有地理信息系统和气象站的地面数据。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.