Hyperspectral Assessment Of Soil Fertility In Farm Fields In Fukushima Decontaminated After The Radioactive Fallout

Farmland in the Fukushima region of Japan experienced unprecedented radioactive contamination as a result of the Fukushima Nuclear Power Plant disaster in 2011. Many fields (4,950 ha; over 30,000 fields to date) have been decontaminated by replacing the top surface soil with non-contaminated soil. However, the fertility of these fields is quite low and within-field heterogeneity is marked. Accordingly, appropriate management of soil and fertilizer is required for recovery of crop productivity in these decontaminated fields. Remote sensing can play a critical role in rapid spatial assessment of soil fertility. This preliminary study investigated the potential of spectral sensing approaches based on hyperspectral reflectance measurements (400-2500 nm) of soil samples from a decontaminated paddy field in the Fukushima region. Spectral index algorithms (the ratio spectral index [RSI] and normalized difference spectral index [NDSI]) and multivariate regression methods (partial least-squares regression [PLSR] and interval PLSR [iPLSR]) were used to identify accurate, robust predictive models. The iPLSR and PLSR showed higher predictive accuracy than the other methods (r(val)(2) = 0.937 and 0.802, respectively). The best spectral indices explored using RSI and NDSI have good potential for assessing the spatial variability of soil carbon content (SC; r(val)(2) = 0.730 similar to 0.844), despite using only two wavebands. The results from the RSI map (or NDSI map) approach provided useful information for creating optimal algorithms for assessing SC values using various sensors, including high-resolution optical satellite sensors. Although we must note that optimal algorithms and their applicability are often site-specific depending on soil type and surface conditions, our results imply that these spectral sensing methods can contribute to the recovery of soil fertility of decontaminated fields in the Fukushima region through careful calibration/validation procedures with sufficient in situ data.