This document presents a method for estimating net surface radiative fluxes over rugged terrain using satellite measurements. Artificial neural network models are developed to retrieve net shortwave and longwave fluxes over horizontal surfaces. Topographic radiation models are then used to correct for the effects of terrain, accounting for factors like shading and sky view ratio. The method is validated using in situ radiation data and applied to a case study area in Tibet, showing that considering topography results in wider variation of estimated fluxes compared to assuming a horizontal surface. Accounting for topographic effects is found to be important for accurately representing surface radiation budgets over mountainous regions.

1. Clear sky Net Surface Radiative Fluxes over Rugged Terrain from Satellite Measurements Tianxing Wang (txwang.rs@gmail.com) Guangjian Yan (gjyan@bnu.edu.cn) Xihan Mu (muxihan@163.com) Ling Chen (chenling8247@126.com) Beijing Normal University

2. Outline of the presentation Background Methods Results and discussion Summary

4. Limitations Most work focuses on the derivation of downwelling SW and upwelling LW radiation, the effective methods for directly estimating land surface net radiation are highly needed, so that the error propagation can be avoided To date, the available radiative fluxes products (e.g., ISCCP, GEWEX,CERES) from remotely sensed data are spatially too coarse to meet the requirements of land applications Almost all current researches ignore the topographic effect over the rugged terrain areas which account for about 2/3 of the global land

5. Methodologies MODIS L1B/MOD03/MOD07/ MOD35/MOD11/MOD04 Fluxes over horizontal surfaces (SW & LW) Fluxes over horizontal surfaces using Artificial Neuron Network Correcting terrain shading Topographic modeling Short wave topographic radiation model Longwave topographic radiation model Fluxes over rugged terrain (SW & LW) Fluxes over rugged terrain

6. Reasons for using ANN The ANN model can accept more input variables and output more desired quantities It has been attempted by many researches in the field of radiative flux budget proving its feasibility in such topic It’s convenient to couple the multi-output of ANN with the topographic model for retrieving fluxes over rugged area

8. single-hidden layer feed-forward network

9. BP training algorithmShortwave ANN model Longwave ANN model

10. Validation of the ANN models Two years of 2008~2009 in situ data are collected as reference from seven U.S. SurfRad sites under clear sky SurfRad sites From :http://www.srrb.noaa.gov/surfrad/ Validation results of the ANN models The maximum root mean square errors (RMSE) of ANN models are less than 45W/m2 (watts per square meter) and 25W/m2 for net SW and LW radiative fluxes, with the average biases are less than 15 W/m2 and 5W/m2, respectively. These accuracies are better than the existing algorithms showing the effectiveness of the ANN models.

12. lower LST due to shadowsTerrain Shadow means the overlaying sphere may be obstructed by terrain, in this situation the sky-view-ratio is less than 1 Dubayah, R. and S. Loechel (1997)

13. Topographic radiation models (SW) Solar direct flux: Sky diffused flux: Reflected from around terrain: Net flux over rugged terrain:

14. Topographic radiation models (LW) Surface emitted flux: Sky emitted flux: Emitted from around terrain: Net flux over rugged terrain: Similarly, by considering the three factors, a LW topographic radiation model is also suggested。This model is more complex compared to the SW model, since it relate to LST in shadow area and the broadband emissivity etc.

15. Correction for terrain shading Seven shading situations It should be noted that, all inputs in the SW and LW topographic radiation models are the unobstructed fluxes, thus, the terrain shading of the outputs of ANN models need to be removed before incorporated in these models. This figure shows the seven shading situations，including A B C D

16. Correction for terrain shading These are the correcting formulas for those seven situations, the details of these variables can be found in the paper. Correction for terrain shading in Shortwave band: Correction for terrain shading in Longwave band:

18. the variation of the fluxes due to the terrain undulation is much wider than that of fluxes neglecting the topographic effect

19. fluxes estimated by assuming a horizontal surface are difficult to reflect the true status of surface radiation budget in terms of both spatial distribution and specific flux valuesNet SW surface radiative fluxes for considering (left) and neglecting (right) the topographic effect

21. the variation of the fluxes due to the terrain undulation is much wider than that of fluxes neglecting the topographic effect

22. The white spots in the left image correspond to the shadowed area, where the LST is low, thus the net LW fluxes are relatively highNet LW surface radiative fluxes for considering (left) and neglecting (right) the topographic effect

24. it will attribute great errors to the estimated fluxes if the terrain undulation effect is not taken into account over rugged terrain area. The errors can reach up to 100 W/m2, and even larger for SW fluxes.Total net surface radiative fluxes for considering (left) and neglecting (right) the topographic effect

25. Summary Two ANN models have been developed in this paper, with which the net surface SW and LW radiative fluxes over horizontal surface can be directly retrieved with better accuracy Coupling the outputs of ANN models, the corresponding SW and LW topographic radiation models are also proposed The results show that the ANN models suggested here are rather effective, and the topographic effect on the net surface fluxes is so significant that the assumption of horizontal surface is not applicable over rugged terrain

26. Thanks for your attention!