The synergies of SMAP enhanced and MODIS products in a random forest regression for estimating 1 km soil moisture over Africa using Google Earth Engine

Figures & data

Figure 1. MODIS land cover map over the study area. In total, the 0–10 cm depth SM observations of 31 TAHMO stations, 1 COSMOS station, and 1 SD_DEM station are used in this study to validate the proposed method.

Table 1. The primary attributes of the SM sites which were used in the validation process.

Site	Number of stations	Land cover	Climate regime	Depth (m)	Detector	Reference
SD_DEM^1	1	Cropland	Dry\Desert	0.05, 0.10	CS615, HYDRA, CS616	(Ardö, 2013)
COSMOS^2	2	Cropland	Temperate	0.00–0.10	Cosmic-ray Probe	(Zreda et al., 2012)
TAHMO^3	32	Cropland, Grassland, Sparse vegetation, Bare area	Tropical, Temperate	0.10	TEROS12, GS1 TEROS10	(van de Giesen et al., 2014)

¹Soil Data_DEMokeya.

²Cosmic-ray Soil Moisture Observing System.

³Trans-African Hydro-Meteorological Observatory (name of in situ network in Sahel Zone in Africa).

Table 2. Main characteristics of the RS observations and products used in the downscaling methods.

Dataset	Data Type and Description	Spatial Resolution	Units	Revisit Time	Temporal Coverage
SPL4SMGP.007	SMAP L4 Global Surface and Root Zone Soil Moisture	9 km × 9 km	Volume fraction	3-hourly	2015-04-02 to present
MODIS/V006/MOD11A2	Daytime and nighttime land surface temperature (LST)	1 km × 1 km	Kelvin	8-day	2000-02-18 to present
MODIS/006/MOD09A1 Surface spectral reflectance	Band 05: Shortwave/NIR, 1230–1250 nm	500 m × 500 m	Unitless		2000-02-18 to present
	Band 06: Shortwave Infrared/SWIR, 1628–1652 nm
	Band 07: Shortwave Infrared/SWIR, 2105–2155 nm
MODIS/061/MOD13A1	Vegetation Index (NDVI)			16-day
MODIS/061/MCD12Q1	Land Cover Type	500 m × 500 m		Yearly	2001-01-01 to present

Figure 2. The workflow of the proposed method for estimating SM using GEE.

Figure 3. (a) SMAP SM product (SPL4SMGP.007) at 9 km resolution, (b) $\mathrm{\Delta }{\mathrm{T}}_s$ (LST_dayLST_night) calculated using MODIS LST products with 1 km spatial resolution, (c) estimated SM map at 1 km spatial resolution using the proposed RFR method, optical/thermal features, and SM values of SMAP, and (d) 1-km SM^C map after applying the pixel correction equation on the estimated SM for Kenya, Africa.

Figure 4. (a) SMAP SM product (SPL4SMGP.007) at 9 km resolution, (b) $\mathrm{\Delta }{\mathrm{T}}_s$ (LST_day-LST_night) calculated using MODIS LST products with 1 km spatial resolution, (c) estimated SM map at 1 km spatial resolution using the proposed RFR method, optical/thermal features, and SM values of SMAP, and (d) 1-km SM^C map after applying the pixel correction equation on the estimated SM for Ghana, Africa.

Figure 5. Spatial correlation analyses conducted between the RS-derived SM and in-situ SM measurements, separately for Kenya and Ghana. (a) and (d): SPL4SMGP.007at 9 km spatial resolution, (b) and (e): estimated SM at 1 km before applying pixel-mean correction (Eq (3(3) $\mathit{S}{M}^c\left(\mathrm{i},\mathrm{j}\right)=\mathrm{SM}\left(\mathrm{i},\mathrm{j}\right)+\left[\frac{S{M}_{PMW}^a+\mathit{S}{M}_{PMW}^d}{2}\right]-\frac{1}{N}\sum _{\mathit{i},\mathit{j}}^N\mathrm{SM}\left(\mathrm{i},\mathrm{j}\right)$(3) )), and (c) and (f): SMC at 1 km after applying pixel-mean correction (Eq (3(3) $\mathit{S}{M}^c\left(\mathrm{i},\mathrm{j}\right)=\mathrm{SM}\left(\mathrm{i},\mathrm{j}\right)+\left[\frac{S{M}_{PMW}^a+\mathit{S}{M}_{PMW}^d}{2}\right]-\frac{1}{N}\sum _{\mathit{i},\mathit{j}}^N\mathrm{SM}\left(\mathrm{i},\mathrm{j}\right)$(3) )).

Figure 6. Temporal patterns of the original SMAP product with 9 km resolution, the 1 km SM map retrieved from RFR, and the SM map after applying the correction coefficient Eq (3(3) $\mathit{S}{M}^c\left(\mathrm{i},\mathrm{j}\right)=\mathrm{SM}\left(\mathrm{i},\mathrm{j}\right)+\left[\frac{S{M}_{PMW}^a+\mathit{S}{M}_{PMW}^d}{2}\right]-\frac{1}{N}\sum _{\mathit{i},\mathit{j}}^N\mathrm{SM}\left(\mathrm{i},\mathrm{j}\right)$(3) ) for 2020 with in-situ measurements of the Kenya (a) and Ghana (b) validation sites.

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van de Giesen, N., Hut, R., & Selker, J. (2014). The Trans‐African hydro‐meteorological observatory (TAHMO). WIREs Water, 1(4), 341–348. https://doi.org/10.1002/wat2.1034

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Data availability statement

All satellite data and products supporting the methodology and findings of this paper (including MODIS and SM Products) have been archived in GEE and are openly available at https://developers.google.com/earth437engine/datasets/catalog/. The field samples of SM over three validation sites, which were used in this study to validate the proposed method, were collected using the ISMN and are available at https://ismn.earth/.