Estimating Soil Moisture Distributions across Small Farm Fields with ALOS/PALSAR
Author(s) -
Yuki Kojima,
Kazuo Oki,
Kosuke Noborio,
Masaru Mizoguchi
Publication year - 2016
Publication title -
international scholarly research notices
Language(s) - English
Resource type - Journals
ISSN - 2356-7872
DOI - 10.1155/2016/4203783
Subject(s) - algorithm , correlation coefficient , remote sensing , moisture , satellite , water content , synthetic aperture radar , environmental science , geology , computer science , meteorology , physics , machine learning , geotechnical engineering , astronomy
The ALOS (advanced land observing satellite) has an active microwave sensor, PALSAR (phased array L-band synthetic aperture radar), which has a fine resolution of 6.5 m. Because of the fine resolution, PALSAR provides the possibility of estimating soil moisture distributions in small farmlands. Making such small-scale estimates has not been available with traditional satellite remote sensing techniques. In this study, the relationship between microwave backscattering coefficient ( σ ) measured with PALSAR and ground-based soil moisture was determined to investigate the performance of PALSAR for estimating soil moisture distribution in a small-scale farmland. On the ground at a cabbage field in Japan in 2008, the soil moisture distribution of multiple soil layers was measured using time domain reflectometry when the ALOS flew over the field. Soil moisture in the 0–20 cm soil layer showed the largest correlation coefficient with σ ( r = 0.403). The σ values also showed a strong correlation with the ground surface coverage ratio by cabbage plants. Our results suggested that PALSAR could estimate soil moisture distribution of the 0–20 cm soil layer across a bare field and a crop coverage ratio when crops were planted.
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