Search

Remote sensing technique gained more attention recently in precision agricultural management for its advantages as efficient, economical, undamaged, and continuous. This paper introduced an agricultural service system, PIE-LANDSCAPE, developed based on remote sensing and big-data techniques. Functions of field management, growth monitoring, precision meteorology service, and planting guidance are integrated and achieved in this system. Based on PIE-LANDSCAPE system, we monitored the pear growth and relevant environmental factors during the whole growth stage and the results can support the planting management optimizing. First, soil quality in pear orchard was assessed with soil organic matter, nutrient indictors, pH, structural indicators, and geographical indictors. Soil quality was in the high level, but addition of soil fertility especially of organic matter was required. Second, the whole pear growth stages were monitored by remote sensing technique, and the result showed the pear trees in most plots grew better. Appropriate irrigation and training are required to improve the pear growing. Moreover, meteorological remote sensing technique was applied for early warning of weather disaster and precision weather forecast. Freezing injury in April florescence could be withstood based on the meteorological data analyses.

The Ocean Salinity (OS) Satellite mission of the State Oceanic Administration of China dedicates to an “all-weather” estimate of high-quality global SSS and to reduce geophysical errors due to surface roughness and sea surface temperature from space. The payload of this mission has the capability of L/C/K multi-frequency passive and L-band active measurement, and can implement the simultaneously remote sensing of SSS, SST, WS and atmospheric parameters. The OS Satellite data processing prototype software has been developed to simulate the instrument and to generate Level 0 to Level 2 data. Based on the prototype software, the end to end study of the OS Satellite mission is performed and the errors of simultaneous retrieval of multiparameters are analyzed and noises level and stability requirement of instruments are estimated.

The wind vector inversion from space-borne microwave radiometer is mainly done on the ground, and need to transfer large amount of data block to the ground when crossing ground stations, which delays the real-time inversion and usage of satellite data. We propose a rapid wind vector retrieval algorithm for the onboard processing of microwave polarimetric radiometer brightness temperature (TB). The rapid wind vector retrieval algorithm is validated using WindSat polarimetric TB. With this rapid onboard inversion algorithm, only the inversion result will be transfer down to ground stations in order to reduce onboard storage space and amount of downlink data transmission, and to improve real-time inversion.