Soil nutrient modelling for precision farming by downscaling of remote sensing data | |
| Author | Kolasani, Sai Krisha |
| Call Number | AIT Thesis no.RS-16-17 |
| Subject(s) | Remote sensing Geographic information systems Soils |
| Note | A thesis submitted in partial fulfilment of the requirements for the degree of Master of Engineering in Remote Sensing and Geographic Information Systems |
| Publisher | Asian Institute of Technology |
| Series Statement | Thesis ; no. RS-16-17 |
| Abstract | The soil nutrients are the main essential components for a better crop growth. In each and every field, there will be a great variability in the soil properties. In general identification of amount of soil nutrient properties is done by laboratory tests which is quite expensive. In this study, soil nutrient properties are identified by using a simple and rapid soil testing kit which is developed for use in the field. By the use of this soil testing kit, the whole process of soil analysis has been simplified and the amount of soil nutrients present is identified in colorimetric determination of Nitrogen, Phosphorous and Potassium with the color chart. This study was done in the Lopburi province of Central Thailand from which 40 soil samples were collected to conduct colorimetric tests and each sample is georeferenced by using GPS. The main objective of this study is to determine the spatial variability of the soil nutrients (NPK) by using geostatistical techniques and remote sensing data. The spectral reflectance of the soil samples is also found by using ASD spectrometer (Narrowband) and are compared with the GeoEye-1 and Landsat-8 satellite data (Broadband). Regression Analysis is done to find the relation between the in-situ experiments to the Visible Near Infrared (VIS-NIR) bands of the satellite data. The analysis showed that the R2value for GeoEye-1 is found to be at 0.6-0.8 than with the Landsat-8 where the R2value is very poor at 0.2-0.28 after removing the outliers. After identification of soil nutrient variability of the site, the Landsat-8 data is downscaled to 2m to study the different soil spectral indices like Brightness Index (BI), Coloration Index (CI) and Saturation Index (SI). The downscaling is done in three different methods (i.e. Bilinear, Cubic Convolution and Nearest neighbor) and a better technique is identified. A good correlation is found between the Narrowband and Broadband Spectral Indices. The overall study shows that, identification of soil nutrients by using colorimetric tests is a better and cost effective solution in determining where the nutrient levels are “Low” or “High”. This study also shows that there is a higher correlation between the spectrometer and GeoEye-1 than with the Landsat-8. By the use of this downscaling technique, we can easily know the spatial variability of the field. |
| Year | 2016 |
| Corresponding Series Added Entry | Asian Institute of Technology. Thesis ; no. RS-16-17 |
| Type | Thesis |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Remote Sensing (RS) |
| Chairperson(s) | Tripathi, Nitin Kumar |
| Examination Committee(s) | Nakamura, Shinichi;Soni, Peeyush |
| Scholarship Donor(s) | Asian Institute of Technology Fellowship |
| Degree | Thesis (M. Eng.) - Asian Institute of Technology, 2016 |