Detection of Phytoplankton Blooms in the Upper Gulf of Thailand Using Sentinel-3A OLCI Imagery

This study aims to use Sentinel-3A Ocean and Land Color Instrument (OLCI) imagery for detecting phytoplankton blooms in the Upper Gulf of Thailand by using the Maximum Chlorophyll Index (MCI). The results showed that the MCI was able to detect phytoplankton blooms in the study area. The areas with intense phytoplankton blooms showed high MCI values. The radiance spectrum (with a reflectance peak at wavelength 708.75 nm) and the difference between baseline wavelengths 708.75 nm and 681.25 nm were quite high. High MCI values corresponded well with locations of phytoplankton blooms seen on RGB False-Color Composite images. Thus, time series analysis of MCIs obtained from Sentinel-3A OLCI images could be used in detecting, tracking, and delineating phytoplankton blooms

Root Zone Soil Moisture Assessment at the Farm Scale Using Remote Sensing and Water Balance Models

Water resource planning and management necessitates understanding soil moisture changes with depth in the root zone at the farm scale. For measuring soil moisture, remote sensing methods have been relatively successful. Soil moisture is estimated from image data, using in situ moisture and an empirical scattering model via regression fit analysis. However, in situ sensor data are prone to misinterpretations, requiring verification. Herein, we aimed at investigating the application of soil moisture from the water balance model towards verification of in situ soil moisture sensor data before in situ data was assessed for its relationship with remote sensing data. In situ soil moisture sensor data was obtained at 10 and 30 cm, and CROPWAT8.0 furnished root zone soil moisture data. The correlation between the in situ soil moisture at 10 and 30 cm was 0.78; the correlation between the soil moisture from CROPWAT8.0 and the in situ soil moisture were 0.64 and 0.62 at 10 and 30 cm, respectively. The R2 between Sentinel-1 backscatter coefficients and in situ moisture were 0.74 and 0.68 at each depth, respectively. Therefore, the water balance model could verify sensor results before assessing in situ soil moisture data for relationship with remote sensing data

Root Zone Soil Moisture Assessment at the Farm Scale Using Remote Sensing and Water Balance Models

Water resource planning and management necessitates understanding soil moisture changes with depth in the root zone at the farm scale. For measuring soil moisture, remote sensing methods have been relatively successful. Soil moisture is estimated from image data, using in situ moisture and an empirical scattering model via regression fit analysis. However, in situ sensor data are prone to misinterpretations, requiring verification. Herein, we aimed at investigating the application of soil moisture from the water balance model towards verification of in situ soil moisture sensor data before in situ data was assessed for its relationship with remote sensing data. In situ soil moisture sensor data was obtained at 10 and 30 cm, and CROPWAT8.0 furnished root zone soil moisture data. The correlation between the in situ soil moisture at 10 and 30 cm was 0.78; the correlation between the soil moisture from CROPWAT8.0 and the in situ soil moisture were 0.64 and 0.62 at 10 and 30 cm, respectively. The R2 between Sentinel-1 backscatter coefficients and in situ moisture were 0.74 and 0.68 at each depth, respectively. Therefore, the water balance model could verify sensor results before assessing in situ soil moisture data for relationship with remote sensing data