Quantifying water stress effect on daily light use efficiency in Mediterranean ecosystems using satellite data

Figures & data

Table 1. Descriptive information of the study sites.

Site	Lat (°)	Lon (°)	h (m)	T (°C)	PRE (mm)	PET (mm)	Vegetation type	Data period
ES-CPa	39.2242	−0.9031	810	13	470	1448	Open shrubland	2009–2011
ES-LJu	36.9266	−2.7521	1590	12	475	1507	Open shrubland	2005, 2008, 2011
ES-LMa	39.9415	−5.7734	260	17	600	1358	Savanna	2005–2013

Note: h is the elevation from the sea level, T is the average mean annual air temperature, PRE is the average annual precipitation, and PET is the average annual potential evapotranspiration.

Table 2. Satellite products used in the study.

	Product	Variables	Use
MODIS	MCD43A1	BRDF parameters k0, k1, and k2 (8-day composites at 500-m spatial resolution).	fAPAR calculation
	MCD43A2	MCD43A1 quality flags.	fAPAR calculation
	MOD16A2	AET and PET (8-day composites at 1-km spatial resolution).	ε′3 calculation
	MODOCGA	Daily reflectance data from AQUA bands 11 and 12 at 1-km spatial resolution and their quality flag band.	ε′5 calculation
SEVIRI	LSA-07	30-minute downward surface shortwave flux at 3-km spatial resolution (MDSSF).	PAR and ε′1 and ε′2 calculations
	LSA-09	Daily integrated LSA-07 (DIDSSF).	PAR and ε′1 and ε′2 calculations
	LSA-16	30-minute AET at 3-km spatial resolution (MET).	ε′2 calculation
	LSA-17	Daily integrated LSA-16 (DMET).	ε′2 calculation
SMOS-derived	L4SMv3D	Daily SM in the first top 5 cm at 1-km spatial resolution from the synergy between SMOS L2 SM descending orbits and MODIS-derived LST and NDVI.	ε′4 calculation
	Land cover	Hybrid land-cover map for Spain obtained from CGL2000, CORINE, IGBP, and GlobCover at 1-km spatial resolution.	Vegetation type assignation for ε′6 calculation and εmax assignation

Table 3. Description of the six water stress factors.

Water stress factor	Description and data source
ε′1 = PRE/PETJ−H	PRE is the precipitation in mm m^−2 day^−1. PRE images at 1-km spatial resolution were obtained by ordinary kriging to ground data from meteorological stations. PETJ−H is the potential evapotranspiration in mm m^−2 day^−1 calculated using the Jensen–Haise empirical relationship (Equation (1)).
ε′2 = AETSEVIRI/PETJ−H	AETSEVIRI is the actual evapotranspiration in mm m^−2 day^−1 obtained from SEVIRI DMET.
ε′3 = AETMODIS/PETMODIS	AETMODIS is the actual evapotranspiration in mm m^−2 day^−1 obtained from MOD16A2. PETMODIS is the potential evapotranspiration in mm m^−2 day^−1 obtained from MOD16A2.
ε′4 = (SM–WP)/(FC–WP)	SM is the soil moisture in the first top 5 cm in m^3 m^−3 day^−1. WP is the wilting point in m^3 m^−3. FC is the field capacity in m^3 m^−3. The three variables were obtained from L4SMv3.
ε′5 = PRI	PRI is the photochemical reflectance index (Equation (2)) calculated as the normalized difference between reflectance from MODOCGA bands 12 (546–556 nm) and 11 (526–536 nm).
ε′6 = n–mVPD	VPD is vapor pressure deficit in Pa calculated as in the MOD17 algorithm (see Section 2.2). Range of application: VPDmin < VPD < VPDmax, with VPDmin and VPDmax depending on the vegetation type. They are used to calculate n and m, which are empirical parameters also depending on the vegetation type. If VPD < VPDmin, ε′6 is set to 1. If VPD > VPDmax, ε′6 is set to 0.

Table 4. Formulation of the statistics used to compare estimated (GPP′_i) and reference (GPP_EC) GPP.

Statistic	Definition
Relative mean biased error
Relative mean absolute error
Relative root mean squared error

Note: n is the number of data pairs.

Figure 1. Temporal variation of ε (black) and ε′_i (red) in ES-LMa. From top to bottom: i = 1, 2, 3, 4, 5, and 6.

Table 5. Coefficients of determination (R²) between water stress factors (ε′) and reference light use efficiency (ε).

	ES-CPa	ES-LJu	ES-LMa
ε′1	0.39	0.39	0.48
ε′2	0.08	0.31	0.48
ε′3	0.24	0.01*	0.40
ε′4	0.12	0.42	0.22
ε′5	0.00*	0.02*	0.35
ε′6	0.12	0.00*	0.20

Note: Correlations were statistically significant at 95% confidence level except when marked with *. Best results appear in bold.

Figure 2. Temporal variation of GPP_EC (black), GPP′₀ (gray), and GPP′_i (red) in ES-LMa. From top to bottom: i = 1, 2, 3, 4, 5, and 6.

Table 6. Statistics of the evaluation of water stress factors by comparing GPP′_i with GPP_EC. GPP₀ refers to the GPP calculated by means of Equation (3) without ε′_i.

	ES-CPa	ES-LJu	ES-LMa	ES-CPa	ES-LJu	ES-LMa
		R^2		rMBE
GPP′0	0.62	0.43	0.56	0.3	2	0.6
GPP′1	0.70	0.50	0.73	−0.14	0.7	0.08
GPP′2	0.68	0.71	0.78	−0.3	0.6	0.03
GPP′3	0.65	0.44	0.65	−0.2	0.6	−0.02
GPP′4	0.68	0.73	0.63	−0.3	0.9	−0.011
GPP′5	0.62	0.43	0.57	−0.018	1.3	0.25
GPP′6	0.69	0.27	0.67	0.05	0.3	0.04
	rMAE			rRMSE
GPP′0	0.4	2	0.7	0.5	2	0.8
GPP′1	0.3	0.8	0.3	0.4	0.9	0.4
GPP′2	0.4	0.7	0.3	0.5	0.8	0.4
GPP′3	0.4	0.8	0.3	0.5	0.9	0.4
GPP′4	0.4	0.9	0.4	0.6	1.0	0.5
GPP′5	0.3	1.3	0.5	0.4	1.4	0.5
GPP′6	0.3	0.7	0.3	0.4	0.8	0.4

Note: Correlations were statistically significant at 95% confidence level. Best results appear in bold.