Abdominal compartment syndrome in a monochorionic-triamniotic triplet pregnancy complicated by feto-fetal transfusion syndrome.

A 40-year-old primigravida woman with a monochorionic-triamniotic (MT) triplet pregnancy was hospitalized due to threatened abortion at 16 gestational weeks. Polyhydramnios in two fetuses and oligohydramnios in the third supported a diagnosis of feto-fetal transfusion syndrome (FFTS) at 23 weeks and 3 days of gestation. Severe dyspnea and liver dysfunction required intensive care unit admission and mechanical ventilation support, and abdominal compartment syndrome (ACS) caused by polyhydramnios was clinically diagnosed. When her general condition was not improved regardless of intensive care, the patient delivered the three fetuses by cesarean section at 23 weeks and 5 days gestation. Abdominal decompression was achieved with delivery, and the patient was discharged 13 days after operation without morbidity. This is the first case report of ACS caused by FFTS in a MT triplet pregnancy resulting in extremely preterm birth.

Correction: Obata, K., et al. Cross-Calibration between ASTER and MODIS Visible to Near-Infrared Bands for Improvement of ASTER Radiometric Calibration. Sensors 2017, 17, 1793.

The authors wish to make the following corrections to this paper [...].

Cross-Calibration between ASTER and MODIS Visible to Near-Infrared Bands for Improvement of ASTER Radiometric Calibration.

Radiometric cross-calibration between the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Terra-Moderate Resolution Imaging Spectroradiometer (MODIS) has been partially used to derive the ASTER radiometric calibration coefficient (RCC) curve as a function of date on visible to near-infrared bands. However, cross-calibration is not sufficiently accurate, since the effects of the differences in the sensor's spectral and spatial responses are not fully mitigated. The present study attempts to evaluate radiometric consistency across two sensors using an improved cross-calibration algorithm to address the spectral and spatial effects and derive cross-calibration-based RCCs, which increases the ASTER calibration accuracy. Overall, radiances measured with ASTER bands 1 and 2 are on averages 3.9% and 3.6% greater than the ones measured on the same scene with their MODIS counterparts and ASTER band 3N (nadir) is 0.6% smaller than its MODIS counterpart in current radiance/reflectance products. The percentage root mean squared errors (%RMSEs) between the radiances of two sensors are 3.7, 4.2, and 2.3 for ASTER band 1, 2, and 3N, respectively, which are slightly greater or smaller than the required ASTER radiometric calibration accuracy (4%). The uncertainty of the cross-calibration is analyzed by elaborating the error budget table to evaluate the International System of Units (SI)-traceability of the results. The use of the derived RCCs will allow further reduction of errors in ASTER radiometric calibration and subsequently improve interoperability across sensors for synergistic applications.

Improved Accuracy of the Asymmetric Second-Order Vegetation Isoline Equation over the RED-NIR Reflectance Space.

The relationship between two reflectances of different bands is often encountered in cross calibration and parameter retrievals from remotely-sensed data. The asymmetric-order vegetation isoline is one such relationship, derived previously, where truncation error was reduced from the first-order approximated isoline by including a second-order term. This study introduces a technique for optimizing the magnitude of the second-order term and further improving the isoline equation's accuracy while maintaining the simplicity of the derived formulation. A single constant factor was introduced into the formulation to adjust the second-order term. This factor was optimized by simulating canopy radiative transfer. Numerical experiments revealed that the errors in the optimized asymmetric isoline were reduced in magnitude to nearly 1/25 of the errors obtained from the first-order vegetation isoline equation, and to nearly one-fifth of the error obtained from the non-optimized asymmetric isoline equation. The errors in the optimized asymmetric isoline were compared with the magnitudes of the signal-to-noise ratio (SNR) estimates reported for four specific sensors aboard four Earth observation satellites. These results indicated that the error in the asymmetric isoline could be reduced to the level of the SNR by adjusting a single factor.