scholarly journals Remotely sensed carotenoid dynamics predict photosynthetic phenology in conifer and deciduous forests

2021 ◽  
Author(s):  
Christopher YS Wong ◽  
Lina M Mercado ◽  
Altaf Arain ◽  
Ingo Ensminger
Keyword(s):  
Remote Sensing ◽  
Terrestrial Ecosystem ◽  
Remote Sensing Data ◽  
Dynamic Global Vegetation Model ◽  
Annual Carbon ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Use Efficiency ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Global Vegetation

Crucially, the phenology of photosynthesis conveys the length of the growing season. Assessing the timing of photosynthetic phenology is key for terrestrial ecosystem models for constraining annual carbon uptake. However, model representation of photosynthetic phenology remains a major limitation. Recent advances in remote sensing allow detecting changes of foliar pigment composition that regulate photosynthetic activity. We used foliar pigments changes as proxies for light-use-efficiency (LUE) to model gross primary productivity (GPP) from remote sensing data. We evaluated the performance of LUE-models with GPP from eddy covariance and against MODerate Resolution Imaging Spectroradiometer (MODIS) GPP, a conventional LUE model, and a process-based dynamic global vegetation model at an evergreen needleleaf and a deciduous broadleaf forest. Overall, the LUE-models using foliar pigment information best captured the start and end of season, demonstrating that using regulatory carotenoids and photosynthetic efficiency in LUE models can improve remote monitoring of the phenology of forest vegetation.

scholarly journals Satellite-based radiative forcing by light-absorbing particles in snow across the Northern Hemisphere

2020 ◽  
Author(s):  
Jiecan Cui ◽  
Tenglong Shi ◽  
Yue Zhou ◽  
Dongyou Wu ◽  
Xin Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Northern Hemisphere ◽  
Radiative Forcing ◽  
Remote Sensing Data ◽  
The Arctic ◽  
Snow Albedo ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Earth’S Climate

Abstract. Snow is the most reflective natural surface on Earth and consequently plays an important role in Earth’s climate. Light-absorbing particles (LAPs) deposited on the snow surface can effectively decrease snow albedo, resulting in positive radiative forcing. In this study, we used remote sensing data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) and the Snow, Ice, and Aerosol Radiative (SNICAR) model to quantify the reduction in snow albedo due to LAPs, before validating and correcting the data against in situ observations. We then incorporated these corrected albedo reduction data in the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model to estimate Northern Hemisphere radiative forcing in January and February for the period 2003–2018. Our analysis reveals an average corrected reduction in snow albedo of ~0.0246, with instantaneous radiative forcing and daily radiative forcing values of ~5.87 and ~1.69 W m−2, respectively. We also observed significant spatial variations in corrected snow albedo reduction, instantaneous radiative forcing and daily radiative forcing throughout the Northern Hemisphere, with the lowest respective values (~0.0123, ~1.09 W m−2, and ~0.29 W m−2) occurring in the Arctic and the highest (~0.1669, ~36.02 W m−2, and ~10.60 W m−2) in northeastern China. From MODIS retrievals, we determined that the LAP content of snow accounts for 57.6 % and 37.2 % of the spatial variability in Northern Hemisphere albedo reduction and radiative forcing, respectively. We also compared retrieved radiative forcing values with those of earlier studies, including local-scale observations, remote-sensing retrievals, and model-based estimates. Ultimately, estimates of radiative forcing based on satellite-retrieved data are shown to represent true conditions on both regional and global scales.

scholarly journals Development of a remote sensing-based rice yield forecasting model

2016 ◽  
Vol 14 (3) ◽  
pp. e0907 ◽  
Author(s):  
Mostafa K. Mosleh ◽  
Quazi K. Hassan ◽  
Ehsan H. Chowdhury
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Rice Yield ◽  
Decision Makers ◽  
Coefficient Of Determination ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Boro Rice

This study aimed to develop a remote sensing-based method for forecasting rice yield by considering vegetation greenness conditions during initial and peak greenness stages of the crop; and implemented for “boro” rice in Bangladeshi context. In this research, we used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived two 16-day composite of normalized difference vegetation index (NDVI) images at 250 m spatial resolution acquired during the initial (January 1 to January 16) and peak greenness (March 23/24 to April 6/7 depending on leap year) stages in conjunction with secondary datasets (i.e., boro suitability map, and ground-based information) during 2007-2012 period. The method consisted of two components: (i) developing a model for delineating area under rice cultivation before harvesting; and (ii) forecasting rice yield as a function of NDVI. Our results demonstrated strong agreements between the model (i.e., MODIS-based) and ground-based area estimates during 2010-2012 period, i.e., coefficient of determination (R2); root mean square error (RMSE); and relative error (RE) in between 0.93 to 0.95; 30,519 to 37,451 ha; and ±10% respectively at the 23 district-levels. We also found good agreements between forecasted (i.e., MODIS-based) and ground-based yields during 2010-2012 period (R2 between 0.76 and 0.86; RMSE between 0.21 and 0.29 Mton/ha, and RE between -5.45% and 6.65%) at the 23 district-levels. We believe that our developments of forecasting the boro rice yield would be useful for the decision makers in addressing food security in Bangladesh.

scholarly journals Evaluation of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) Aerosol Algorithm for Himawari-8 Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2771 ◽  
Author(s):  
Lu She ◽  
Hankui Zhang ◽  
Weile Wang ◽  
Yujie Wang ◽  
Yun Shi
Keyword(s):  
Mean Squared Error ◽  
Atmospheric Correction ◽  
Remote Sensing Data ◽  
Japan Meteorological Agency ◽  
Absolute Bias ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Highly Correlated ◽  
New Generation

Himawari-8, operated by the Japan Meteorological Agency (JMA), is a new generation geostationary satellite that provides remote sensing data to retrieve atmospheric aerosol optical depth (AOD) at high spatial (1 km) and high temporal (10 min) resolutions. The Geostationary- National Aeronautics and Space Administration (NASA) Earth exchange (GeoNEX) project recently adapted the multiangle implementation of atmospheric correction (MAIAC) algorithm, originally developed for joint retrieval of AOD and surface anisotropic reflectance with the moderate resolution imaging spectroradiometer (MODIS) data, to generate Earth monitoring products from the latest geostationary satellites including Himawari-8. This study evaluated the GeoNEX Himawari-8 ~1 km MAIAC AOD retrieved over all the aerosol robotic network (AERONET) sites between 6°N–30°N and 91°E–127°E. The corresponding JMA Himawari-8 AOD products were also evaluated for comparison. We only used cloud-free and the best quality satellite AOD retrievals and compiled a total of 16,532 MAIAC-AERONET and 21,737 JMA-AERONET contemporaneous pairs of AOD values for 2017. Statistical analyses showed that both MAIAC and JMA data are highly correlated with AERONET AOD, with the correlation coefficient (R) of ~0.77, and the root mean squared error (RMSE) of ~0.16. The absolute bias of MAIAC AOD (0.02 overestimation) appears smaller than that of the JMA AOD (0.05 underestimation). In comparison with the JMA data, the time series of MAIAC AOD were more consistent with AERONET AOD values and better capture the diurnal variations of the latter. The dependence of MAIAC AOD bias on scattering angles is also discussed.

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