Mapping The Temporal and Spatial Variability of Soil Moisture Content Using Proximal Soil Sensing

2nd International Conference on Agricultural Engineering for Sustainable Agricultural Production

Virgawati, S.^1 ; Mawardi, M.^2 ; Sutiarso, L.^2 ; Shibusawa, S.^3 ; Segah, H.^4 ; Kodaira, M.^3

Dept. of Agrotechnology, University of Pembangunan Nasional veteran, Yogyakarta, Indonesia^1

Dept. of Agricultural and Biosystem Engineering, University of Gadjah Mada, Yogyakarta, Indonesia^2

Dept. of Environmental and Agricultural Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan^3

Dept. of Forestry, Faculty of Agriculture, University of Palangka Raya, Central Kalimantan, Indonesia^4

关键词: Data analysis techniques;    Different growth stages;    Partial least square regression;    Proximal soil sensing;    Repeated measurements;    Spectroscopy technology;    Temporal and spatial variability;    Vis-NIR spectroscopy;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/147/1/012038/pdf
DOI  :  10.1088/1755-1315/147/1/012038

来源: IOP

【 摘 要 】

In studies related to soil optical properties, it has been proven that visual and NIR soil spectral response can predict soil moisture content (SMC) using proper data analysis techniques. SMC is one of the most important soil properties influencing most physical, chemical, and biological soil processes. The problem is how to provide reliable, fast and inexpensive information of SMC in the subsurface from numerous soil samples and repeated measurement. The use of spectroscopy technology has emerged as a rapid and low-cost tool for extensive investigation of soil properties. The objective of this research was to develop calibration models based on laboratory Vis-NIR spectroscopy to estimate the SMC at four different growth stages of the soybean crop in Yogyakarta Province. An ASD Field-spectrophotoradiometer was used to measure the reflectance of soil samples. The partial least square regression (PLSR) was performed to establish the relationship between the SMC with Vis-NIR soil reflectance spectra. The selected calibration model was used to predict the new samples of SMC. The temporal and spatial variability of SMC was performed in digital maps. The results revealed that the calibration model was excellent for SMC prediction. Vis-NIR spectroscopy was a reliable tool for the prediction of SMC.