*5.3. Potential Applications*

Using cameras on-board a drone can provide timely estimation of the landscape albedo. For example, flying drone missions at different times of the day and season and in different weather and soil moisture conditions can provide information on the dynamic variations of albedo of the landscape. Such measurements may be especially useful in situations where monitoring with conventional radiometers is not feasible. For example, white roofs are proposed as a strategy to mitigate the urban heat island in the city landscape [41]. However, white paint on building roofs can suffer from erosion and dust deposition, and thus, its albedo can quickly decrease, from the original high values of 0.7 to 0.8 to values of 0.2 to 0.3 after a few years [42]. Direct measurement of roof albedo is challenging because roof spaces are generally not accessible by micrometeorological tower instruments. Drone flights conducted at different times can help to quantify the actual albedo and inform decisions on whether the roof needs cleaning or repainting. Another advantage of the drone methodology is its fine spatial resolution, as compared to satellite monitoring. Even with Sentinel 2 with a spatial resolution of 10 m, some landscape features (such as small fish ponds and small buildings) will become mixed satellite pixels.

#### **6. Conclusions and Future Outlook**

In this paper we tested a workflow for landscape albedo determination using images acquired by drone cameras. The key findings are as follows:


*Remote Sens.* **2018**, *10*, 1812

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2072-4292/10/11/1812/ s1, Figure S1: Pictures of some of the selected ground targets for Brooksvale Park and Yale Playground, Figure S2: Classification of the mosaicked image for Brooksvale Park (a) and Yale Playground (b), Figure S3: Visible (a), shortwave band albedo for the Yale Playground when all shadow were taken as non-vegetation (b) and vegetation (c) under clear sky conditions, Figure S4: Visible (a) and shortwave band albedo (b) for the Brooksvale Park under clear sky conditions, Figure S5: Landsat visible (a) and shortwave band albedo (b) for the Yale Playground, Figure S6: Landsat visible (a) and shortwave band albedo (b) for the Brooksvale Park, Table S1: Input parameters for the SMART model.

**Author Contributions:** Conceptualization, X.L. and C.C.; Methodology, J.M., N.T., G.T., C.C., J.X., and L.B.; Formal Analysis, C.C.; Writing—Original Draft Preparation, C.C.; Writing—Review & Editing, X.L.

**Funding:** This research was funded by the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology (grant 2017r067), Natural Science Foundation of Jiangsu Province (grants BK20180796 BK20181100), National Natural Science Foundation of China (grant 41805022), the Ministry of Education of China (grant PCSIRT), the Priority Academic Program Development of Jiangsu Higher Education Institutions (grant PAPD), and a Visiting Fellowship from China Scholarship Council (to C.C.).

**Acknowledgments:** We would like to thank Nina Kantcheva Tushev and Georgi Tushev for their help in conducting the drone experiment at the Yale Playground.

**Conflicts of Interest:** The authors declare no conflict of interest.
