BRDF characterization and calibration inter-comparison between Terra MODIS, Aqua MODIS, and S-NPP VIIRS

MODerate-resolution Imaging Spectroradiometer (MODIS) has 36 bands. Among them, 16 thermal emissive bands covering a wavelength range from 3.8 to 14.4 μm. After 16 years on-orbit operation, the electronic crosstalk of a few Terra MODIS thermal emissive bands develop substantial issues which cause biases in the EV brightness temperature measurements and surface feature contamination. The crosstalk effects on band 27 with center wavelength at 6.7 μm and band 29 at 8.5 μm increased significantly in recent years, affecting downstream products such as water vapor and cloud mask. The crosstalk issue can be observed from nearly monthly scheduled lunar measurements, from which the crosstalk coefficients can be derived. Most of MODIS thermal bands are saturated at moon surface temperatures and the development of an alternative approach is very helpful for verification. In this work, a physical model was developed to assess the crosstalk impact on calibration as well as in Earth view brightness temperature retrieval. This model was applied to Terra MODIS band 29 empirically for correction of Earth brightness temperature measurements. In the model development, the detector nonlinear response is considered. The impacts of the electronic crosstalk are assessed in two steps. The first step consists of determining the impact on calibration using the on-board blackbody (BB). Due to the detector nonlinear response and large background signal, both linear and nonlinear coefficients are affected by the crosstalk from sending bands. The crosstalk impact on calibration coefficients was calculated. The second step is to calculate the effects on the Earth view brightness temperature retrieval. The effects include those from affected calibration coefficients and the contamination of Earth view measurements. This model links the measurement bias with crosstalk coefficients, detector nonlinearity, and the ratio of Earth measurements between the sending and receiving bands. The correction of the electronic crosstalk can be implemented empirically from the processed bias at different brightness temperature. The implementation can be done through two approaches. As routine calibration assessment for thermal infrared bands, the trending over select Earth scenes is processed for all the detectors in a band and the band averaged bias is derived for certain time. In this case, the correction of an affected band can be made using the regression of the model with band averaged bias and then corrections of detector differences are applied. The second approach requires the trending for individual detectors and the bias for each detector is used for regression with the model. A test using the first approach was made for Terra MODIS band 29 with the biases derived from long-term trending of sea surface temperature and Dome-C surface temperature.

[1]  Amit Angal,et al.  Absolute Calibration of Optical Satellite Sensors Using Libya 4 Pseudo Invariant Calibration Site , 2014, Remote. Sens..

[2]  Xiaoxiong Xiong,et al.  Development, characterization, and performance of the EOS MODIS sensors , 2003, SPIE Optics + Photonics.

[3]  Xiaoxiong Xiong,et al.  An overview of the Earth Observing System MODIS instrument and associated data systems performance , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[4]  William L. Barnes,et al.  MODIS: a global imaging spectroradiometer for the Earth Observing System , 1992, Optics East.

[5]  Xiaoxiong Xiong,et al.  Status of terra MODIS and aqua modis , 2003 .

[6]  Changyong Cao,et al.  Predicting Simultaneous Nadir Overpasses among Polar-Orbiting Meteorological Satellites for the Intersatellite Calibration of Radiometers , 2004 .

[7]  Xiangqian Wu,et al.  Postlaunch Calibration Update of MetOp-B AVHRR Reflective Solar Channels Using MetOp-A , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Xiaoxiong Xiong,et al.  Early On-Orbit Performance of the Visible Infrared Imaging Radiometer Suite Onboard the Suomi National Polar-Orbiting Partnership (S-NPP) Satellite , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[9]  A. Kuusk,et al.  A reflectance model for the homogeneous plant canopy and its inversion , 1989 .

[10]  Amit Angal,et al.  Using the Sonoran and Libyan Desert test sites to monitor the temporal stability of reflective solar bands for Landsat 7 enhanced thematic mapper plus and Terra moderate resolution imaging spectroradiometer sensors , 2010 .

[11]  Amit Angal,et al.  Applications of Spectral Band Adjustment Factors (SBAF) for Cross-Calibration , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Amit Angal,et al.  Characterization of Terra and Aqua MODIS VIS, NIR, and SWIR Spectral Bands' Calibration Stability , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[13]  J. Butler,et al.  VIIRS on‐orbit calibration methodology and performance , 2014 .

[14]  Aisheng Wu,et al.  Terra and Aqua MODIS inter‐comparison of three reflective solar bands using AVHRR onboard the NOAA‐KLM satellites , 2008 .

[15]  J. Townshend,et al.  Characterizing land surface anisotropy from AVHRR data at a global scale using high performance computing , 2001 .

[16]  J. Roujean,et al.  A bidirectional reflectance model of the Earth's surface for the correction of remote sensing data , 1992 .

[17]  Amit Angal,et al.  Radiometric Cross-Calibration of EO-1 ALI With L7 ETM+ and Terra MODIS Sensors Using Near-Simultaneous Desert Observations , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[18]  A. Mccarthy Development , 1996, Current Opinion in Neurobiology.