Description:Complete Specification:
The following specification describes and ascertains the nature of this invention and the manner in which it is to be performed.
Field of the invention
The present disclosure relates to a method for optimizing soil moisture content derived from satellite data in an area and a system thereof.

Background of the invention
Soil moisture content is an important parameter for many environmental phenomena including meteorological, agricultural and hydrological applications. Furthermore, soil moisture content can be considered as an indicator of droughts and environmental changes. Soil moisture content has a dynamic structure and thus, monitoring spatial and temporal variations in soil moisture content is of great importance for agriculture. Measuring accurate in situ soil moisture content is sometimes expensive and mostly time-consuming. There are many studies on estimating soil moisture content using both passive and active remote sensing satellites. Because of the remarkable penetrating capabilities of radar signal into the surface, active microwave remote sensing systems have been recently preferred in soil moisture studies.

There are many methodologies developed world wide for the retrieval of soil moisture content from satellite radar data i.e. Delta Index (DI), Change Detection Method (CD), Cumulative Density Function (CDF) transformation and look-up table (LUT) method. All the methods use backscattering coefficients to retrieve the soil moisture. However, these conventional methods for deriving soil moisture content from the satellite data lacks accuracy. The soil moisture content estimated using the conventional methods is errored by various other parameters of the area such as the crop water content, moisture of the area covered by the crop and likewise. There is a need of an accurate method for deriving soil moisture content from the satellite data of the area taking into account all the error factors.

The patent application US4015366 A titled “Highly Automated Agricultural Production System” discloses a highly automated agricultural production system which comprises a sensing subsystem comprising direct and indirect sensing means in an agricultural production area. The direct sensing means are generally on ground or plant mounted. The indirect sensing means are remote from the area being sensed. The direct and indirect sensing means are adapted to jointly generate data on all important parameters in the homogeneous agricultural production area. A field operations subsystem which accomplishes the functions of fruit harvesting, fruit conveying, fruit grading and fruit storage is the most preferred embodiment of the invention. The system does not focus on soil moisture content.

Brief description of the accompanying drawings
An embodiment of the invention is described with reference to the following accompanying drawings
Figure 1 depicts a system architecture (100) for optimizing soil moisture content derived from satellite data in an area (102).
Figure 2 illustrates the method steps (200) for optimizing soil moisture content derived from satellite data in an area (102).
Figure 3 illustrates the method steps for determining crop dynamics correction factor.
Figure 4 illustrates the method steps for determining soil dynamics correction factor.

Detailed description of the drawings

The present invention is further described below in combination with the accompanying drawings. The following embodiments are merely used for more clearly describing the technical solutions of the present invention but are not intended to limit the scope of protection of the present invention.

Figure 1 depicts a system architecture (100) for optimizing soil moisture content derived from satellite data in an area (102). The system (100) comprises a first sensing means (103), a second sensing means (104), an electronic control unit (105) and at least a display unit (108). The electronic control unit (105) has a processor (106) and at least a memory (107).

The first sensing means (103) comprises a group of one or more sensors which are configured to acquire data from at least a satellite (101). The data acquired by the first sensing means (103) comprises optical and radar data. Optical data comprises images of the Earth over relatively large area and are useful in the production of vegetation maps or to estimate specific vegetation parameters. The sensors acquiring optical data detect the solar radiation reflected from targets on the area. The sensors function in the optical part of wavelength spectrum, and include visible, near infrared and short-wave infrared wavelengths. Radar data is a type of active data where a sensor produces its own energy and then records the amount of that energy reflected back after interacting with Earth. While optical imagery is similar to interpreting a photograph, radar data is interpreting surface characteristics like structure and moisture. A person skilled in the art would appreciate the existence of one or more satellites so as to acquire optical and radar data.

The second sensing means (104) is an IOT based soil sensor system. Internet of Things (IOT) technology is suitable for the smart plant monitoring system. The IOT soil sensor system is suitable to check soil conditions, which may help to start a better growth of the plant. The IOT soil sensor system located in the ground comprises a group of sensors which can measure soil temperature, NPK (Nitrogen, Phosphorous, Potassium), volumetric water content, photosynthetic radiation, soil water potential and at least soil oxygen levels. The IOT soil sensor system can also measure the real time soil moisture content directly from the soil. The measurements from the IOT soil sensor system are more accurate as compared to the satellite measurements as it is directly measured form the ground and is not errored by any external factors.

The optical and radar data acquired by the first sensing means (103) and the real time data acquired by the second sensing means (104) are transmitted to the electronic control unit (105). The processor (106) in the electronic control unit (105) analyzes the data acquired by both the first sensing means (103) and second sensing means (104) to rectify the soil moisture content derived from the satellite data. A processor (106) is the logic circuitry that responds to and processes the basic instructions that drive an electronic control unit (105). The processor (106) performs arithmetical, logical, input/output (I/O) and other basic instructions that are passed from the electronic control unit (105).

The processor (106) determines the soil moisture content from the satellite data. The determination of soil moisture content from the satellite data is by predetermined methods known in the conventional arts. There are many methodologies developed world wide for the retrieval of soil moisture content from the satellite data. They include the Delta Index method (DI), Change Detection Method (CD), Cumulative Density Function (CDF) transformation and look-up table (LUT) method. All these methods use backscattering coefficients to retrieve the soil moisture content. However the soil moisture content obtained from these equations are not accurate as it may be errored by additional factors like the crop water content, moisture of that part of soil covered by the crop and likewise. Hence, the processor (106) in the present invention optimizes the soil moisture content derived from the satellite data using conventional methods by considering two correction factors. It is the processor (106) which rectifies the soil moisture content derived from the satellite data using the correction factors to derive an accurate soil moisture.

The correction factors comprise the crop dynamics correction factor and at least a soil dynamics correction factor. The crop dynamics correction factor is the factor to negate the error due to crop water content. Similarly the soil dynamics correction factor is the factor to negate the error in soil moisture content of that part of the soil covered by crop.

The data acquired by the first sensing means (103) and second sensing means (104) is stored in the memory (107) of the electronic control unit (105). It is from the memory (107), that the processor (106) fetches the historical data. A person skilled in the art would appreciate that the satellite (101) provides historical data as back as 20 years and thereby the system (100) can retrieve any previous data about the area (102) which is stored in the memory (107).

In one embodiment of the invention, the display unit (108) resides in the system (100). In another embodiment of the invention, the display unit (108) resides in an application in the user terminal. It can be either in a mobile application or web application in the side of the user.

Figure 2 illustrates the method steps (200) for optimizing soil moisture content derived from satellite data in an area (102). The method steps (200) comprise deriving (201) the soil moisture content from the satellite data, rectifying (202) the soil moisture content derived from the satellite data using a crop dynamics correction factor and a soil dynamics correction factor to derive an accurate soil moisture content, and displaying (203) the accurate soil moisture content. The system (100) for optimizing soil moisture content from satellite data in an area (102) is the one described in the preceding paragraphs in accordance with Figure 1. The system (100) comprises a first sensing means (103), second sensing means (104), electronic control unit (105), and at least a display unit (108).

In method step 201, soil moisture content is derived from the satellite data by means of the processor (106) in the electronic control unit (105). A person skilled in the art would appreciate the derivation of soil moisture content from the satellite data of the area (102) by conventional methods such as Delta Index method (DI), Change Detection Method (CD), Cumulative Density Function (CDF) transformation and look-up table (LUT) method. For instance, the soil moisture content from the change detection method (CD method) is derived by the equation:

〖SM〗_CD=(BCi,t-BCdry)/(BCdry-BCwet)

where, BCi,t wet is the backscatter coefficient representing the wet soil condition, BCwet and BCdry are the highest and lowest observed value of soil condition respectively. The unit of retrieved soil moisture content is percentage. Likewise, soil moisture content is derived from the satellite data of the area (102) from the other methods such as Delta Index method (DI), Cumulative Density Function (CDF) transformation and look-up table (LUT) method.

A person skilled in the art would appreciate that the soil moisture content derived from the conventional methods in method step 201 is prone to errors. This error may either due to the error caused by the crop water content or that part of soil covered by the crop.

In method step 202, the soil moisture content derived from the satellite data is rectified using the crop dynamics correction factor and the soil dynamics correction factor by means of the processor (106) in the electronic control unit (105) to derive the accurate soil moisture.

In method step 202 (a), the processor (106) in the electronic control unit (105) determines the crop dynamics correction factor. The crop dynamics correction factor is the factor to negate the error due to crop water content.

Figure 3 illustrates the method steps for determining crop dynamics correction factor. In method step 202(a)(i), the processor (106) in the electronic control unit (105) retrieves the historical optical data from the satellite (101). A person skilled in the art would appreciate that the satellite (101) provides historical data as back as 20 years and thereby the system (100) can retrieve any previous data about the area (102) which is stored in the memory (107). The processor (106) fetches historical data from the time period when the crop was implanted in the area (102) and analyzes the change in crop moisture content. The crop moisture content is determined from the Normalized Difference Moisture Index.

In method step 202(a)(ii), the processor (106) in the electronic control unit (105) determines the cumulative Normalized Difference Moisture Index (Nc) from the historical optical data. The Normalized Difference Moisture Index (NDMI) detects moisture levels in vegetation using a combination of near-infrared (NIR) and short-wave infrared (SWIR) spectral bands. It is a reliable indicator of water stress in crops. NDMI is derived from the equation:

NDMI = (NIR – SWIR) / (NIR + SWIR);

where NIR is the near-infrared spectral band and SWIR is the short-wave infrared spectral band.

In method step 202(a)(iii), the processor (106) in the electronic control unit (105) analyzes the cumulative NDMI (Nc) temporally to derive the crop water content (Wc). In method step 202(a)(iv), the processor (106) in the electronic control unit (105) receives the time stamp matched optical data from the first sensing means (103). The time stamp matched optical data is the real time optical data acquired by the first sensing means (103) from the satellite (101).

In method step 202(a)(v), the processor (106) in the electronic control unit (105) determines the time stamp matched NDMI (Nt) from the time stamp matched optical data.
NDMI = (NIR – SWIR) / (NIR + SWIR);

where NIR is the near-infrared spectral band and SWIR is the short-wave infrared spectral band.

In method step 202(a)(vi), the processor (106) in the electronic control unit (105) analyzes the time stamp matched NDMI (Nt) to derive the time stamp matched crop water content (Wt). The crop water content (Wc) is analyzed with the time stamp matched crop water content (Wt) to derive the crop dynamics correction factor, as in method 202(a).

In method step 202 (b), the processor (106) in the electronic control unit (105) determines the soil dynamics correction factor. The soil dynamics correction factor is the factor to negate the error in soil moisture content of that part of the soil covered by crop.

Figure 4 illustrates the method steps for determining soil dynamics correction factor. In method step 202(b)(i), the processor (106) in the electronic control unit (105) retrieves the historical radar data from the memory (107) and analyzes the radar data to derive the historical soil moisture content.

In method step 202(b)(ii), the processor (106) in the electronic control unit (105) receives the time stamp matched soil moisture content. The time stamp matched soil moisture content is measured by the IOT based sensor system located in the area (102). The IOT soil sensor system located in the ground comprises a group of sensors to measure the real time soil moisture content directly from the soil.

The historical soil moisture content is analyzed with the time stamp matched soil moisture content to determine the soil dynamics correction factor to derive the accurate soil moisture content as in method 202(b).

In method step 202, the soil moisture content derived from the satellite data of the area is rectified using the crop dynamics correction factor and soil dynamics correction factor to derive the accurate soil moisture content. For instance, if the soil moisture content is derived from the change detection method (CD method), then the accurate soil moisture content is determined as follows:

〖SM〗_CD=(BCi,t-BCdry )/(BCdry-BCwet) ±Crop dynamics correction factor±Soil dynamics correction factor

where, BCi,t wet is the backscatter coefficient representing the wet soil condition, BCwet and BCdry are the highest and lowest observed value of soil condition respectively.

Likewise, the accurate soil moisture content can be determined from the soil moisture content derived from the conventional methods such as Delta Index method (DI), Cumulative Density Function (CDF) transformation and look-up table (LUT) method.

In method step 203, the accurate soil moisture content is displayed in a display unit (108). In one embodiment of the invention, the display unit (108) resides in the system (100). In another embodiment of the invention, the display unit (108) resides in an application in the user terminal. At the user terminal, it can either be a mobile application or a web application.

The idea of optimizing the soil moisture content derived from the satellite data eliminates the problem with the conventional arts wherein the derived soil moisture content lacks accuracy. The present disclosure eliminates this problem with the conventional art by optimizing the soil moisture content derived from satellite data using the crop dynamics correction factor and soil dynamics correction factor.

It must be understood that the embodiments explained in the above detailed description are only illustrative and do not limit the scope of this invention. Any modification of a method (200) for optimizing soil moisture content derived from satellite data in an area (102) and a system (100) thereof are envisaged and form a part of this invention. The scope of this invention is limited only by the claims.
, Claims:We Claim:

1. A method (200) of optimizing soil moisture content derived from satellite data in an area (102), the method (200) using a first sensing means (103) for acquiring the data of the area (102) from at least a satellite (101), a second sensing means (104) for acquiring the real time data of the area (102), said first sensing means (103) and second sensing means (104) in communication with an electronic control unit (105), the electronic control unit (105) configured to store the data acquired by the first sensing means (103) and second sensing means (104), the method steps (200) comprising deriving (201) the soil moisture content from the satellite data, the method steps (200) characterized by:
rectifying (202) the soil moisture content derived from the satellite data using a crop dynamics correction factor and a soil dynamics correction factor to derive an accurate soil moisture content by means of an electronic control unit (105);
displaying (203) the accurate soil moisture content by means of a display unit (108).

2. A method (200) of optimizing soil moisture content derived from satellite data in an area (102) as claimed in claim 1, wherein the data acquired by the first sensing means (103) comprises optical data and radar data.

3. A method (200) of optimizing soil moisture content derived from satellite data in an area (102) as claimed in claim 1, wherein rectifying the soil moisture content through crop dynamics correction factor by the electronic control unit (105) further comprises:
retrieving (202(a)(i)) the historical optical data of the area (102) from the electronic control unit (105);
determining the cumulative Normalized Difference Moisture Index (NDMI) (Nc) (202(a)(ii)) from the historical optical data;
analyzing the cumulative NDMI (Nc) temporally (202(a)(iii)) to derive the crop water content (Wc);
determining a time stamp matched NDMI (Nt) (202(a)(v)) from a received time stamp matched optical data (202(a)(iv));
analyzing the time stamp matched NDMI (Nt) temporally (202(a)(vi)) to derive the time stamp matched crop water content (Wt);
analyzing the crop water content (Wc) with the time stamp matched crop water content (Wt) (202(a)) to derive the crop dynamics correction factor.

4. A method (200) of optimizing soil moisture content derived from satellite data in an area (102) as claimed in claim 1, wherein rectifying the soil moisture content through soil dynamics factor by the electronic control unit (105) further comprises:
determining the historical soil moisture content (202(b)(i)) from the radar data;
determining the time stamp matched soil moisture content (202(b)(ii)) from the second sensing means (104);
analyzing the historical soil moisture content and time stamp match soil moisture content (202(b)) to derive soil dynamics correction factor.

5. A system (100) for optimizing soil moisture content derived from satellite data in an area (102), the system (100) comprising a first sensing means (103) configured to acquire data of the area (102) from at least a satellite (101), a second sensing means (104) configured to acquire real time data of the area (102), the system (100) characterized by:
an electronic control unit (105), the electronic control unit (105) in communication with the first (103) and second sensing means (104), the electronic control unit (105) configured to:
store the data acquired by the first (103) and second sensing means (104);
derive the soil moisture content from the satellite data;
derive a crop dynamics correction factor and a soil dynamics correction factor;
rectify the soil moisture content derived from the satellite sensor using a crop dynamics correction factor and a soil dynamics correction factor to derive an accurate soil moisture content;
a display unit (108) in communication with the electronic control unit (105) to display the accurate soil moisture content.

6. A system (100) for optimizing soil moisture content derived from satellite data in an area (102) as claimed in claim 5, wherein the display unit (108) can reside either within the system (100) or remotely from the system (100) in an application in user terminal.