EGU presentation

1. 1
UAS Based Soil Moisture Downscaling Using
Random Forest Regression Model
• Ruodan Zhuang1, Salvatore Manfreda2, Yijian Zeng3, Nunzio Romano2, Eyal Ben Dor4,
Antonino Maltese5, Paolo Nasta2, Nicolas Francos4, Fulvio Capodici5, Antonio
Paruta5, Giuseppe Ciraolo5, Brigitta Szabó6, János Mészáros6, George P. Petropoulos7,
Lijie Zhang8, and Zhongbo Su3,9
• 1 University of Basilicata, Italy; 2 University of Naples Federico II, Napoli, Italy; 3 University of Twente, The
Netherlands; 4 Tel Aviv University, Israel; 5 University of Palermo, Italy; 6 Centre for Agricultural Research, Hungary; 7
Technical University of Crete, Greece; 8 Forschungszentrum Jülich, Germany; 9 School of Water and Environment,
Chang’an University, China

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1. Study Area
Alento River Basin
Monteforte Cilento

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1. Monitoring Activities
UAS
10/2018
UAS
06/2019
a) In-situ measurements plots b) SoilNet and TDR measurements location
Cosmic ray probe
SoilNet probes

4. 4
2. In-situ Rainfall data
• k is an empirical factor to
indicate the decay effect from
the rainfall (0.85 and 0.98 )
• Pt-i is the precipitation value
at ith days before day of t
Antecedent Precipitation Index

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

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

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3. 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
SRTM
Digital Elevation Model
(DEM)
30m / /
LANDSAT-8 RED, GREEN BANDS 30m 16 days 2015-2019
LANDSAT-8 TIR BANDS 30m 16 days 2015-2019

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3. 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
SM
SM

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3. 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)
30m Soil moisture

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3. 5year average map of 30m predicted SSM
5year-average 30m SSM (30km*30km) MFC2 and SoilNet
MFC2
DEM 30km*30km

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4. Scaling characteristics
2019-06-12 1km resolution, 30km*30km,
Sentinel
2019-06-12 30m resolution, 30km*30km
Predicted
2019-06-13 16cm resolution,
200m*300m.

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4. Scaling characteristics: Variance
10
100
0 200 400 600 800 1000 1200 1400
lg(σ
2
)
pixel size (m)
30m
1km
1
10
100
0 0.5 1 1.5 2
lg(σ
2
)
pixel size (m)
16cm
UAS: Small area: 200m*300m 1km SSM and 30m SSM: large area 30km*30km
MFC2
b= -0,177
b= -0, 478
b= -0, 304

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4. Scaling characteristics: Variances Time series
1km sentinel SSM product Predicted 30m SSM product
Same Area: 30km*30km

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▪ Amoore, L. (2019). Doubt and the Algorithm: On the Partial Accounts of Machine Learning. Theory, Culture and Society, 36(6), 147–
169. https://doi.org/10.1177/0263276419851846
▪ Bauer-Marschallinger, B., Paulik, C., Hochstöger, S., Mistelbauer, T., Modanesi, S., Ciabatta, L., … Wagner, W. (2018). Soil moisture
from fusion of scatterometer and SAR: Closing the scale gap with temporal filtering. Remote Sensing, 10(7), 1030.
https://doi.org/10.3390/rs10071030
▪ Manfreda, S., McCabe, M. F., Miller, P. E., Lucas, R., Madrigal, V. P., Mallinis, G., … Toth, B. (2018). On the use of unmanned aerial
systems for environmental monitoring. Remote Sensing, 10(4), 641. https://doi.org/10.3390/rs10040641
▪ McCabe, M. F., Rodell, M., Alsdorf, D. E., Miralles, D. G., Uijlenhoet, R., Wagner, W., … Wood, E. F. (2017). The future of Earth
observation in hydrology. Hydrology and Earth System Sciences, 21(7), 3879–3914. https://doi.org/10.5194/hess-21-3879-2017
▪ Nasta, P., Bogena, H. R., Sica, B., Weuthen, A., Vereecken, H., & Romano, N. (2020). Integrating Invasive and Non-invasive
Monitoring Sensors to Detect Field-Scale Soil Hydrological Behavior. Frontiers in Water, 2(September).
https://doi.org/10.3389/frwa.2020.00026
▪ Paruta, A., Ciraolo, G., Capodici, F., Manfreda, S., Sasso, S. F. D., Zhuang, R., … Maltese, A. (2020). A Geostatistical Approach to Map
Near-Surface Soil Moisture Through Hyperspatial Resolution Thermal Inertia. IEEE Transactions on Geoscience and Remote Sensing,
1–18. https://doi.org/10.1109/TGRS.2020.3019200
▪ Peng, J., Loew, A., Merlin, O., & Verhoest, N. E. C. (2017). A review of spatial downscaling of satellite remotely sensed soil moisture.
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▪ Romano, N., Nasta, P., Bogena, H., De Vita, P., Stellato, L., & Vereecken, H. (2018). Monitoring Hydrological Processes for Land and
Water Resources Management in a Mediterranean Ecosystem: The Alento River Catchment Observatory. Vadose Zone Journal, 17(1),
180042. https://doi.org/10.2136/vzj2018.03.0042
▪ Tmušić, G., Manfreda, S., Aasen, H., James, M. R., Gonçalves, G., Ben-Dor, E., … McCabe, M. F. (2020). Current practices in UAS-
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Reference

15. Appendix 1: LANDSAT-8: From TIR to LST
• STEP 1:
𝐹𝑉𝐶 =
𝑁𝐷𝑉𝐼 − 𝑁𝐷𝑉𝐼𝑠
𝑁𝐷𝑉𝐼𝑣 + 𝑁𝐷𝑉𝐼𝑠
NDVI values of full vegetation cover and
bare soil.
• STEP 2:
Land surface emissivity 𝜀 retrieval:
𝑖𝑓 𝐹𝑉𝐶 = 0 → 𝜀
= 0.979 − 0.046 ∗ 𝜌𝑏𝑎𝑛𝑑4
𝑖𝑓 0 < 𝐹𝑉𝐶 < 1 → 𝜀
= 0.971(1 − 𝐹𝑉𝐶) + 0.987 ∗ 𝐹𝑉𝐶
𝑖𝑓 𝐹𝑉𝐶 = 1 → 𝜀 = 0.99
• STEP 3
𝑇𝑠 = [
𝑐2
𝜆 ln{
𝑐1
𝜆5[
[𝜀(
𝑘1
(𝑒
𝑘2
𝑇 ) − 1
) + (1 − 𝜀)𝐿𝑑] − (1 − 𝜀)𝐿𝑑
𝜀
]
+ 1}
• 𝐿𝑑 is the down-welling atmospheric radiance.
• 𝜆 is the effective band wavelength,
• T is the brightness temperature (K),
• 𝑐1 = 1.19104 × 108
𝑊𝜇𝑚4
𝑚−2
𝑠𝑟−1
• 𝑐2 = 14387.7𝜇𝑚𝐾
• K1=774.8853
• K2=1321.0789
• 𝜀 is the land surface emissivity.
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16. Appendix 2: Example map of LST
16
Presence of clouds may limit the extents of
the higher resolution LST maps
lst1: 1km MODIS LST (averaged of 13:30 and 01:30)
lst2: 30m LANDSAT LST (daytime)

17. Appendix 3: Example map of NDVI
• LANDSAT8 Band 4 (red) & 5 (Nir) (Top of atmosphere reflectance)➔ Atmosphere correction (bottom of
atmosphere reflectance) ➔ NDVI ori ➔ Interpolation (with limit)➔ S-G filter ➔ NDVI (30m)
• MODIS 1km NDVI ➔ S-G filter
17
NDVI
ndvi1: 1km MODIS NDVI
ndvi2: 30m LANDSAT NDVI