presentation given during the meeting on "Remote Sensing for land degradation analysis and sustainable management of agroforestry systems".

1. 1
Soil Moisture Retrievals from Unmanned Aerial Systems (UAS)
HARMONIOUS WG3 members: Yijian Zeng, Zhongbo Su, Eyal Ben Dor, Antonino Maltese, Fulvio Capodici, Antonio Paruta, Nicolas Francos,
Giuseppe Ciraolo, Brigitta Szabó, János Mészáros , George P. Petropoulos, Lijie Zhang
Nunzio Romano1, Ruodan Zhuang2*, Salvatore Manfreda3, Silvano Fortunato Dal Sasso2, Carolina
Allocca1, Paolo Nasta1
1 Department of Agricultural Sciences, AFBE Division, University of Naples Federico II, Portici (NA), Italy
2 Department of European and Mediterranean Cultures, Architecture, Environment, Cultural Heritage, University of Basilicata, Matera
(MT), Italy
3 Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Napoli (NA), Italy
* e-mail: ruodan.zhuang@unibas.it

2. 2
1. Study Area and Data
2. Methods
3. Thermal Inertia Method
4. Random Forest Regression Model
5. Simplified Triangle Model
OUTLINE

3. 3
1. Study Area
Alento River Basin
Monteforte Cilento

4. 4
1. Monitoring Activities
UAS10/2018
UAS06/2019
a) In-situ measurements plots b) SoilNet and TDR measurements location

5. 5
1. UAS Surveys
RGB Multispectral Thermal
UAS Photos
Othomosaic
DSM
Reflectance: Red, Green, NIR LST: sunrise, noon
Noon Sunrise + Noon
a) UAS survey

6. 6
2. Methods
Methods input output
Thermal Inertia UAS TI/VIS/NIR SSM on bare soil pixel
Random forest (RF) Regression
Model Downscaling
UAS TI/VIS/NIR,
Land surface features
SSM on bare soil pixel
Triangle Model UAS TI/VIS/NIR SM (vegetated area) + ET

7. 7
3. Thermal Inertia Method
a) Flowchart of Thermal Inertia Method
b) NDVI c) LST

8. 8
3. Thermal Inertia Method: Results
c) Apparent Thermal Inertia vs Soil moisture
a) Apparent Thermal Inertia b) Soil Moisture
y=8.56x-0.15
Case1: 24-Oct-2018

9. 9
3. Thermal Inertia Method: Results
y = 1.0879x + 0.0077
R² = 0.73
0.1
0.15
0.2
0.25
0.3
0.35
0.1 0.15 0.2 0.25 0.3
SoilMoisture(cm3/cm3)
ATI (˚C-1)
Case2: 14-June-2018
a) Apparent Thermal Inertia b) Soil Moisture
c) Apparent Thermal Inertia vs Soil moisture

10. 10
4. Random Forest Regression Model:
Two Steps Downscaling
c) 16cm & 1km resolution DEM
a) Flowchart of RF regression model
b) Two steps downscaling

11. 11
4. RF Regression Model:
Coarse Resolution (1km & 30m) Data
Datasets (Sensor) Variables Spatial resolution Temporal resolution Duration
Sentinel-1 C-SAR
Surface soil moisture
(SSM)
1km Daily 2015-2019
MODIS
Land surface
temperature (LST)
1km Daily 2015-2019
MODIS
Normalized difference
vegetation (NDVI)
1km
10 days 2015-2019
SRTM30+
Digital Elevation Model
(DEM)
30m / /
LANDSAT RED, GREEN BANDS 30m 16 days 2015-2019
LANDSAT TIR BANDS 30m 16 days 2015-2019

12. 12
4. RF Regression Model: MODEL I
1km➔30m
Feature: API, Importance: 0.56
LST, Importance: 0.27
NDVI, Importance: 0.10
DEM, Importance: 0.07
RMSE: 11.17 [saturation degree]
r2: 0.84
Pearson correlation coefficient: 0.91
a) RF Regression Model I Test Results
b) Evaluation of the Estimated SM Time Series

13. 13
4. RF Regression Model: MODEL II
30m➔16cm
a) RF Regression Model II Test Results b) Validation of the Estimated SM c) Estimated SM map (14-June-2019)

14. 14
5. Simplified Triangle Model
a) A Simplified Triangle Model
b) Estimated SM Map
c) Validation of Estimated SM
Observation Estimated Difference
(Petropoulos et al., IJRS 2020)

15. 15
Thanks!

16. 16
▪ Petropoulos, G.P., A. Maltese, T. N. Carlson, G. Provenzano, A. Pavlides, G. Ciraolo, D. Hristopulos, F. Capodici, C. Chalkias, G. Dardanelli, S. Manfreda, Exploring
the use of UAVs with the simplified “triangle” technique for Soil Water Content and Evaporative Fraction retrievals in a Mediterranean setting, International
Journal of Remote Sensing, (doi: 10.1080/01431161.2020.1841319) 2020.
▪ Paruta, A., P. Nasta, G. Ciraolo, F. Capodici, S. Manfreda, N. Romano, E. Bendor, Y. Zeng, A. Maltese, S. F. Dal Sasso and R. Zhuang, A geostatistical approach to
map near-surface soil moisture through hyper-spatial resolution thermal inertia, IEEE Transactions on Geoscience and Remote Sensing, (doi:
10.1109/TGRS.2020.3019200) 2020. [pdf]
▪ Su, Z., Y. Zeng, N. Romano, S. Manfreda, F. Francés, E.B. Dor, B. Szabó, G. Vico, P. Nasta, R. Zhuang, N. Francos, J. Mészáros, S.F. Dal Sasso, M. Bassiouni, L.
Zhang, D.T. Rwasoka, B. Retsios, L. Yu, M.L. Blatchford, C. Mannaerts, An Integrative Information Aqueduct to Close the Gaps between Satellite Observation
of Water Cycle and Local Sustainable Management of Water Resources, Water, 12, 1495, (doi: 10.3390/w12051495) 2020. [pdf]
▪ Zhuang, R.; Y. Zeng; S. Manfreda; Z. Su, Quantifying Long-term Land Surface and Root Zone Soil Moisture over Tibetan Plateau, Remote Sensing,12, 509,
(doi: 10.3390/rs12030509) 2020. [pdf]
▪ Tmušić, G., S. Manfreda, H. Aasen, M. James, G. Gonçalves E. Ben-Dor, A. Brook, M Polinova, J.J. Arranz, J. Mészáros, R. Zhuang, K. Johansen, Y. Malbeteau, I.P.
de Lima, C. Davids, S. Herban, M. McCabe, Practical guidance for UAS-based environmental mapping, Remote Sensing, 12, 1001, (doi: 10.3390/rs12061001)
2020. [pdf]
▪ Manfreda, S., M. F. McCabe, P. E. Miller, R. Lucas, V. Pajuelo Madrigal, G. Mallinis, E. Ben-Dor, D. Helman, L. Estes, G. Ciraolo, J. Müllerová, F. Tauro, M. I. de Lima,
J. L. M. P. de Lima, A. Maltese, F. Frances, K. Caylor, M. Kohv, M. Perks, G. Ruiz-Pérez, Z. Su, G. Vico, and B. Toth, On the Use of Unmanned Aerial Systems for
Environmental Monitoring, Remote Sensing, 10(4), 641; (doi:10.3390/rs10040641) 2018. [pdf]
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