A learning system of a precipitable water vapor estimation model includes a radio wave intensity acquisition part, a precipitable water vapor acquisition part, and a learning part. The radio wave intensity acquisition part acquires radio wave intensities of a plurality of frequencies among radio waves received by a microwave radiometer. The precipitable water vapor acquisition part acquires a precipitable water vapor calculated based on an atmospheric delay of a GNSS signal received by a GNSS receiver. Based on the radio wave intensities of the plurality of frequencies and the precipitable water vapor at a plurality of time points in a particular period, the learning part subjects an estimation model to machine learning such that an input data based on the radio wave intensities of the plurality of frequencies is taken as an input to output the precipitable water vapor.

A microwave coupling moisture content sensor and a method for deriving a linear regression correlation between signal data generated by a microwave moisture content sensor and moisture contents of objects are disclosed. The microwave coupling moisture content sensor includes a microwave resonator and a signal feeding member or a microwave emitting member. An object is placed outside the microwave resonator, and the signal feeding member or the microwave emitting member emits microwaves, which penetrate the object. The resonance frequency and the amplitude of the microwave resonator are measured to obtain the moisture content of the object. Several objects having known moisture contents are detected by the microwave coupling moisture content sensor to obtain corresponding resonance frequency and amplitude. Correlation coefficients of several combinations are calculated, and the combinations having low correlation coefficients are removed, whereby the best linear regression correlation is derived for the measurement of moisture contents of objects.

A microwave coupling moisture content sensor and a method for deriving a linear regression correlation between signal data generated by a microwave moisture content sensor and moisture contents of objects are disclosed. The microwave coupling moisture content sensor includes a microwave resonator and a signal feeding member or a microwave emitting member. An object is placed outside the microwave resonator, and the signal feeding member or the microwave emitting member emits microwaves, which penetrate the object. The resonance frequency and the amplitude of the microwave resonator are measured to obtain the moisture content of the object. Several objects having known moisture contents are detected by the microwave coupling moisture content sensor to obtain corresponding resonance frequency and amplitude. Correlation coefficients of several combinations are calculated, and the combinations having low correlation coefficients are removed, whereby the best linear regression correlation is derived for the measurement of moisture contents of objects.

A water vapor observation device includes a water vapor index acquisition module which acquires a water vapor index calculated based on radio wave intensities of at least two frequencies out of radio waves received by a microwave radiometer, a global navigation satellite system (GNSS) precipitable water vapor acquisition module which acquires a GNSS precipitable water amount calculated based on an atmospheric delay of a GNSS signal received by a GNSS receiver, a correlation data generation module which generates correlation data between the water vapor index and the GNSS precipitable water amount based on the water vapor index and the GNSS precipitable water amount at a plurality of time points during a predetermined period, and a precipitable water vapor calculation module which calculates a precipitable water amount based on the correlation data from the water vapor index obtained based on the microwave radiometer.

A water vapor observation device includes a water vapor index acquisition module which acquires a water vapor index calculated based on radio wave intensities of at least two frequencies out of radio waves received by a microwave radiometer, a global navigation satellite system (GNSS) precipitable water vapor acquisition module which acquires a GNSS precipitable water amount calculated based on an atmospheric delay of a GNSS signal received by a GNSS receiver, a correlation data generation module which generates correlation data between the water vapor index and the GNSS precipitable water amount based on the water vapor index and the GNSS precipitable water amount at a plurality of time points during a predetermined period, and a precipitable water vapor calculation module which calculates a precipitable water amount based on the correlation data from the water vapor index obtained based on the microwave radiometer.

A system and method for generating soil moisture data from satellite images of a geographical area using a deep learning model 108 is provided. The system includes one or more satellites 102A-C, a soil moisture data generator server 106. The method includes, (i) receiving, by a soil moisture data generator server, satellite images of the geographical area, (ii) pre-processing first set of satellite images, second set of satellite images, and third set of satellite images, (iii) interpolating, using spline interpolation, pre-processed first set of images, pre-processed second set of images, and pre-processed third set of images to generate high-resolution set of images, (iv) generating hydrological parameters from the high-resolution set of images, (v) training, a deep learning model, by providing historical hydrological parameters and historical soil moisture data associated with historical satellite images as training data to generate trained deep learning model, (v) generating soil moisture data on daily basis.

A system and method for generating soil moisture data from satellite images of a geographical area using a deep learning model 108 is provided. The system includes one or more satellites 102A-C, a soil moisture data generator server 106. The method includes, (i) receiving, by a soil moisture data generator server, satellite images of the geographical area, (ii) pre-processing first set of satellite images, second set of satellite images, and third set of satellite images, (iii) interpolating, using spline interpolation, pre-processed first set of images, pre-processed second set of images, and pre-processed third set of images to generate high-resolution set of images, (iv) generating hydrological parameters from the high-resolution set of images, (v) training, a deep learning model, by providing historical hydrological parameters and historical soil moisture data associated with historical satellite images as training data to generate trained deep learning model, (v) generating soil moisture data on daily basis.

The present exemplary embodiments provide a signal detection circuit and a sensor which improve a quality factor of a resonator by modeling an initial state of the resonator using an attenuator and a phase shifter which are modeling paths and significantly change a transmission coefficient of the resonator even with a small variation of an object to be measured.

A system and methods for measuring soil properties characteristics, the system comprising: at least one probe configured to be inserted into the soil, the probe comprising a plurality of antennas; a radio link characterization unit for transmitting a radio signal from at least one of the antennas and receiving a propagated radio signal from at least one of the antennas to yield at least one radio link; and a processor for converting the radio link characteristics into the soil properties characteristics.

A system and methods for measuring soil properties characteristics, the system comprising: at least one probe configured to be inserted into the soil, the probe comprising a plurality of antennas; a radio link characterization unit for transmitting a radio signal from at least one of the antennas and receiving a propagated radio signal from at least one of the antennas to yield at least one radio link; and a processor for converting the radio link characteristics into the soil properties characteristics.