An assessment of satellite-based agricultural water productivity over the Indian region

The preliminary analysis of agricultural water productivity (AWP) over India using satellite data were investigated through productivity mapping, water use (actual evapotranspiration (ET_a)/effective rainfall (R_eff) mapping and water productivity mapping. Moderate Resolution Imaging Spectroradiometer data was used for generating agricultural land cover (MCD12Q1 at 500 m), gross primary productivity (GPP; MOD17A2 at 1 km), and ET_a (MOD16A2 at 1 km). R_eff was estimated at 10 km using the United States Department of Agriculture soil conservation service method from daily National Oceanic and Atmospheric Administration Climate Prediction Center rainfall data. Six years’ (2007–2012) data were analysed from June to October. The seasonal AWP and rainwater productivity (RWP) were estimated using the ratios of seasonal GPP (kg C m^?2) and water use (mm) maps. The average AWP and RWP ranges from 1.10–1.30 kg Cm^?3 and 0.94–1.0 kg C m^?3, respectively, with no significant annual variability but a wide spatial variability over India. The highest AWP was observed in northern India (1.22–1.80 kg C m^?3) and lowest in western India (0.81–1.0 kg C m^?3). Large variations in AWP (0.69–1.80 kg C m^?3) were observed in Himachal Pradesh, Jammu and Kashmir, northeastern states (except Assam), Kerala, and Uttaranchal. The low GPP of these areas (0.0013–0.13 kg C m^?2) with low seasonal total ET_a (<101 mm) and R_eff (<72 mm) making the AWP high that do not correspond to high productivity but possible water stress. Gujarat, Rajasthan, Maharashtra, Madhya Pradesh, Jharkhand, and Karnataka showed low AWP (0.73–1.13 kg C m^?3) despite having high ET_a (261–558 mm) and high R_eff (287–469 mm), indicating significant scope for improving productivity. The highest RWP was observed in northern parts and Indo-Gangetic plains (0.80–1.6 kg C m^?3). The 6 years’ analysis reveals the status of AWP, leading to appropriate interventions to better manage land and water resources, which have great importance in global food security analysis.     

Artificial neural network–genetic algorithm for estimation of crop evapotranspiration in a semi-arid region of Iran

This study compares the daily potato crop evapotranspiration (ET_C) estimated by artificial neural network (ANN), neural network–genetic algorithm (NNGA) and multivariate nonlinear regression (MNLR) methods. Using a 6-year (2000–2005) daily meteorological data recorded at Tabriz synoptic station and the Penman–Monteith FAO 56 standard approach (PMF-56), the daily ET_C was determined during the growing season (April–September). Air temperature, wind speed at 2 m height, net solar radiation, air pressure, relative humidity and crop coefficient for every day of the growing season were selected as the input of ANN models. In this study, the genetic algorithm was applied for optimization of the parameters used in ANN approach. It was found that the optimization of the ANN parameters did not improve the performance of ANN method. The results indicated that MNLR, ANN and NNGA methods were able to predict potato ET_C at desirable level of accuracy. However, the MNLR method with highest coefficient of determination (R ² > 0.96, P value < 0.05) and minimum errors provided superior performance among the other methods.     

A Penman-Monteith evapotranspiration model with bulk surface conductance derived from remotely sensed spatial contextual information

ABSTRACT

A novel approach involving the use of the contextual information in a scatter plot of Moderate Resolution Imaging Spectrometer (MODIS) derived Land Surface Temperature versus Fraction of Vegetation (LST vs. F_v) has been proposed in this study to obtain pixel-wise values of bulk surface conductance (G_s) for use in the Penman-Monteith (PM) model for latent heat flux (λ_ET) estimation. Using a general expression for G_s derived by assuming a two-source total λ_ET (canopy transpiration plus soil evaporation) approach proposed by previous researchers, minimum and maximum values of G_s for a given region can be inferred from a trapezoidal scatter plot of pixel-wise values of LST and corresponding F_v. Using these as limiting values, G_s values for each pixel can be derived through interpolation and subsequently used with the PM model to estimate λ_ET for each pixel. The proposed methodology was implemented in 5 km × 5 km areas surrounding each of four flux towers located in tropical south-east Asia. Using climate data from the tower and derived G_s values the PM model was used to obtain pixel-wise instantaneous λ_ET values on six selected dates/times at each tower. Excellent comparisons were obtained between tower measured λ_ET and those estimated by the proposed approach for all four flux tower locations (R² = 0.85–0.96; RMSE = 18.27–33.79 W m^–2). Since the LST- F_v trapezoidal method is simple, calibration-free and easy to implement, the proposed methodology has the potential to provide accurate estimates of regional evapotranspiration with minimal data inputs.     

An agrometeorological–spectral model to estimate soybean yield,applied to southern Brazil

Soybean yield is modelled from data gathered from crops in Rio Grande do Sul State, Brazil. The model comprises an agrometeorological term, obtained by adjusting the multiplicative model of Jensen, modified by Berlato, and a spectral term, obtained from National Oceanic and Atmospheric Administration (NOAA) satellite images of the maximum Normalized Difference Vegetation Index (NDVI). The weather data used to calculate the relative evapotranspiration (ET _r /ET ₀) cover the period from 1975 to 2000, and the NDVI/NOAA images were obtained from 1982 to 2000. Application of the agrometeorological–spectral model produced better yield estimates (of about 5%) than Jensen's model, allowing the further generation of yield maps for the most significant soybean production regions within the Rio Grande do Sul State.     

Estimation of air temperature and reference evapotranspiration using MODIS land surface temperature over Greece

Moderate Resolution Imaging Spectroradiometer (MODIS), land surface temperature data, during daytime (LST_day) or night-time (LST_night), were employed for predicting maximum (T_max) or minimum (T_min) air temperature measured at ground stations, respectively, in order to be used as alternative inputs in minimum data-based reference evapotranspiration (ET) models in 28 stations in Greece during the growing season (May–October). The deviations between daily LST_night and T_min were found to be small, but they were greater between LST_day and T_max. Furthermore, the temperature vegetation index (TVX) method was employed for achieving more accurate T_max values from LST_day, after determining the normalized difference vegetation index of a full canopy (NDVI_max). The TVX method was validated on ‘temporal’ basis, but when the method was tested spatially, the improvement on the T_max estimates from LST_day was not encouraging, for being used operationally over Greece. Thus, LST_day or LST_night MODIS data were used as inputs in three ET models [Hargreaves–Samani, Droogers–Allen, and Reference Evapotranspiration Model for Complex Terrains (REMCT)] and their estimations, as compared with ground-based Penman–Monteith estimates, indicated that the REMCT model achieved the most accurate ET predictions (r = 0.93, mean bias error = 0.44 mm day^–1 and root mean square error = 0.74 mm day^–1), which can allow the spatial analysis of ET at higher spatial resolutions in areas with lack of ground temperature data.     

Growing period and drought early warning in Africa using satellite data

Vegetation growing periods for 1983-84 were determined for 28 sites in Ethiopia using data from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA series of meteorological satellites. Results offer promise for drought early warning from space. A strong correlation (r=0·99) was found between rates of change of the normalized difference vegetation index (NDVI) derived from the AVHRR data and threshold values of a soil moisture index at the beginning and ends of growing periods. The moisture index (P + S)/ET_p relates precipitation P, stored soil moisture S and potential evapotranspiration ET_p in a simple moisture balance (LPG) model that requires inputs of standard monthly meteorological data. A moisture threshold of (P + S)/ET_p = 0·5 was used to identify the beginning and end of the growing periods and to calibrate the time series of NDVI responses. Trends also detected in values of the NDVI during vegetation growth cycles suggest useful minima exist at the beginning and end of growing periods. Below respective minima of 0·10 and 0·22, growing periods are unlikely to have been initiated or to continue during a declining growth stage. Correlation analysis indicated a relation between moisture index and NDVI, with NDVI lagging in time, in most cases, by 5 or less weeks during the initial growth stage and 6 or more weeks during declining growth     

Co-active neurofuzzy inference system for evapotranspiration modeling

This study proposes co-active neuro-fuzzy inference system (CANFIS) for daily reference evapotranspiration (ET₀) modeling by using daily atmospheric parameters obtained from California Irrigation Management Information System (CIMIS) database. The CANFIS model is trained and tested using three stations from different geographical locations in California. The model is compared with the well-known conventional ET₀ models such as the CIMIS Penman equation, the Penman–Monteith equation standardized by the Food and Agriculture Organization (FAO-56 PM), the Hargreaves equation and the Turc equation. Meteorological variables; solar radiation, air temperature, relative humidity and wind speed taken from CIMIS database for 4 years (January 2002–December 2005) are used to evaluate the performance analysis of the models. Statistics such as average, standard deviation, minimum and maximum values, as well as criteria such as root mean square error (RMSE), the efficiency coefficient (E) and determination coefficient (R ²) are used to measure the performance of the CANFIS. Considerably well performance is achieved in modeling ET₀ by using CANFIS. It is concluded from the results that CANFIS can be proposed as an alternative ET₀ model to the existing conventional models.     

A simple model to estimate the daily value of the regional maximum evapotranspiration from satellite temperature and albedo images

Abstract

We have tried an adaptation of the radiation model proposed by FAO, applicable in any area, for the estimation of the regional maximum evapotranspiration, ET, from temperature and albedo images obtained from a satellite. This model is based on the relationships

ET_m = k _cET_o

ET_o = A + BR _g + CR _g T_a ^max

where k _c is the crop coefficient, ET_o is the maximum evapotranspiration of the reference crop (green grass), R_g is the global radiation obtained from satellite albedo images, T _a ^max is the maximum temperature of the air obtained from the near-midday satellite temperature and A, B and C are the empirical coefficients characteristic of each zone calculated for different intervals of wind velocity and relative humidity of the air. By applying this model to the Valentian Region (Spain) we have obtained an estimation of the maximum daily evapotranspiration to an accuracy of 20 per cent.     

Improvement in air–sea flux estimates derived from satellite observations

A new method is developed to estimate daily turbulent air–sea fluxes over the global ocean on a 0.25° grid. The required surface wind speed (w ₁₀) and specific air humidity (q ₁₀) at 10 m height are both estimated from remotely sensed measurements. w ₁₀ is obtained from the SeaWinds scatterometer on board the QuikSCAT satellite. A new empirical model relating brightness temperatures (T _b) from the Special Sensor Microwave Imager (SSM/I) and q ₁₀ is developed. It is an extension of the author's previous q ₁₀ model. In addition to T _b, the empirical model includes sea surface temperature (SST) and air–sea temperature difference data. The calibration of the new empirical q ₁₀ model utilizes q ₁₀ from the latest version of the National Oceanography Centre air–sea interaction gridded data set (NOCS2.0). Compared with mooring data, the new satellite q ₁₀ exhibits better statistical results than previous estimates. For instance, the bias, the root mean square (RMS), and the correlation coefficient values estimated from comparisons between satellite and moorings in the northeast Atlantic and the Mediterranean Sea are –0.04 g kg^?1, 0.87 g kg^?1, and 0.95, respectively. The new satellite q ₁₀ is used in combination with the newly reprocessed QuikSCAT V3, the latest version of SST analyses provided by the National Climatic Data Center (NCDC), and 10 m air temperature estimated from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses (ERA-Interim), to determine three daily gridded turbulent quantities at 0.25° spatial resolution: surface wind stress, latent heat flux (LHF), and sensible heat flux (SHF). Validation of the resulting fields is performed through a comprehensive comparison with daily, in situ values of LHF and SHF from buoys. In the northeast Atlantic basin, the satellite-derived daily LHF has bias, RMS, and correlation of 5 W m^?2, 27 W m^?2, and 0.89, respectively. For SHF, the statistical parameters are –2 W m^?2, 10 W m^?2, and 0.94, respectively. At global scale, the new satellite LHF and SHF are compared to NOCS2.0 daily estimates. Both daily fluxes exhibit similar spatial and seasonal variability. The main departures are found at latitudes south of 40° S, where satellite latent and sensible heat fluxes are generally larger.     

Drought monitoring of the wetland in the Tumen River Basin between 1991 and 2016 using Landsat TM/ETM+

Wetland areas are known as ‘the kidneys of the Earth’ because they provide important functions towards stabilizing the environment, long-term protection of water sources, effectively minimizing sediment loss, purifying surface water from industrial and agricultural pollutants, and enhancing aquifer recharge. The condition of water supply in wetlands directly affects the growth of wetland plants and local biodiversity. Therefore, drought monitoring is vital in wetlands. In this study, Vegetation Temperature Condition Index (VTCI) derived from normalized difference vegetation index (NDVI) and land surface temperature (LST) is used to observe the drought status of the wetland in the cross-border (China and North Korea) Tumen River Basin from 1991 to 2016. For this purpose, the Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) data for six periods were used for the analysis. Soil moisture maps acquired from the China Meteorological Administration Land Data Assimilation System Version 1.0 (CLDAS-V1.0) were then introduced for validating the reliability of the drought monitoring method. The results showed that most areas with a normal moisture level (decreased 25.8%) began experiencing slight drought (increased 29.7%). The coefficient of determination (R²) between VTCI and soil moisture showed values of 0.69, 0.32, and 0.2 for 0–5 cm, 0–10 cm, and 10–20 cm thicknesses, respectively. Although climate change probably contributes to the formation of drought by decreasing precipitation (50 mm decrease in Chinese section) and increasing temperature (0.5°C increase in North Korean section), human activities such as surges in daily water consumption appear as the main threats that leading to droughts in this wetland.     

Soil hydraulic parameters estimated from satellite information through data assimilation

Leaf area index (LAI) and actual evapotranspiration (ET_a) from satellite observations were used to estimate simultaneously the soil hydraulic parameters of four soil layers down to 60 cm depth using the combined soil water atmosphere plant and genetic algorithm (SWAP–GA) model. This inverse model assimilates the remotely sensed LAI and/or ET_a by searching for the most appropriate sets of soil hydraulic parameters that could minimize the difference between the observed and simulated LAI (LAI_sim) or simulated ET_a (ET_asim). The simulated soil moisture estimates derived from soil hydraulic parameters were validated using values obtained from soil moisture sensors installed in the field. Results showed that the soil hydraulic parameters derived from LAI alone yielded good estimations of soil moisture at 3 cm depth; LAI and ET_a in combination at 12 cm depth, and ET_a alone at 28 cm depth. There appeared to be no match with measurement at 60 cm depth. Additional information would therefore be needed to better estimate soil hydraulic parameters at greater depths. Despite this inability of satellite data alone to provide reliable estimates of soil moisture at the lowest depth, derivation of soil hydraulic parameters using remote sensing methods remains a promising area for research with significant application potential. This is especially the case in areas of water management for agriculture and in forecasting of floods or drought on the regional scale.     

Multitemporal comparative analysis of TRMM-3B42 satellite-estimated rainfall with surface gauge data at basin scales: daily,decadal and monthly evaluations

In order to examine the reliability and applicability of Tropical Rainfall Measuring Mission (TRMM) and Other Satellites Precipitation Product (3B42) Version 6 (TRMM-3B42) at basin scales, satellite rainfall estimates were compared with geostatistically interpolated reference data from 12 rain gauge stations for three consecutive years: 2005, 2006 and 2007. Gauge–TRMM-3B42 statistical properties for daily, decadal and monthly multitemporal precipitations were compared using the following cross-validation continuous statistical measures: mean bias error (MBE), root mean square difference (RMSD), mean absolute difference (MAD) and coefficient of determination (r ²) metrics. The averaged spatial–temporal comparisons showed that the TRMM-3B42 rainfall estimates were much closer to the geostatistically interpolated gauge data, with minimal biases of??0.40 mm day^?1,??1.78 mm decad^?1 and??6.72 mm month^?1 being observed in 2006. In the same year, the gauge and TRMM-3B42 rainfall estimates marginally correlated better than in 2005 and 2007, with the daily, decadal and monthly coefficients of determination being 82.2%, 93.9% and 96.5%, respectively. The results showed that the correlations between the gauge-derived precipitation and the TRMM-3B42-derived precipitation increased with increasing temporal intervals for all three considered years. Quantitatively, the TRMM-3B42 observations slightly overestimated the precipitations during the wet seasons and underestimated the observed rainfall during the dry seasons. The results of the study show that the estimates from TRMM-3B42 precipitation retrievals can effectively be applied in the interpolation of missing gauge data, and in the verification of precipitation uncertainties at the basin scales with minor adjustments, depending on the timescales considered.     

Quantifying characteristic growth dynamics in a semi-arid grassland ecosystem by predicting short-term NDVI phenology from daily rainfall: a simple four parameter coupled-reservoir model

Predicting impacts on phenology of the magnitude and seasonal timing of rainfall pulses in water-limited grassland ecosystems concerns ecologists, climate scientists, hydrologists, and a variety of stakeholders. This report describes a simple, effective procedure to emulate the seasonal response of grassland biomass, represented by the satellite-based normalized difference vegetation index (NDVI), to daily rainfall. The application is a straightforward adaptation of a staged linear reservoir that simulates the pulse-like entry of rainwater into the soil and its redistribution as soil moisture, the uptake of water by plant roots, short-term biomass development, followed by the subsequent transpiration of water through foliage. The algorithm precludes the need for detailed, site specific information on soil moisture dynamics, plant species, and the local hydroclimate, while providing a direct link between discrete rainfall events and consequential biomass responses throughout the growing season. We applied the algorithm using rainfall data from the Central Plains Experimental Range to predict vegetation growth dynamics in the semi-arid shortgrass steppe of North America. The mean annual rainfall is 342 mm, which is strongly bifurcated into a dominantly ‘wet’ season, where during the three wettest months (May, June and July) the mean monthly rainfall is approximately 55 mm month^?1; and a ‘dry’ season, where during the three driest months (December, January and February), the mean monthly rainfall is approximately 7 mm month^?1. NDVI data from the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 16 day, 250 m × 250 m product were used as a proxy for grassland phenology for the period-of-record 2000–2013. Allowing for temporal changes in basic parameters of the response function over the growing season, the predicted response of the model tracks the observed NDVI metric with correlation coefficients exceeding 0.92. A two-stage series reservoir is preferred, whereby the characteristic time for transfer of a rainfall event to the peak response of NDVI decreases from 24 days (early growing season) to 12 days (late growing season), while the efficiency of a given volume of rainfall to produce a correspondingly similar amount of aboveground biomass decreases by a factor of 40% from April to October. Behaviours of the characteristic time of greenup and loss of rainfall efficiency with progression of the growing season are consistent with physiological traits of cool-season C₃ grasses versus warm-season C₄ grasses, and with prior research suggesting that early season production by C₃ grasses is more responsive to a given amount of precipitation than mid-summer growth of C₄ shortgrasses. Our model explains >90% of seasonal biomass dynamics. We ascribe a systematic underprediction of observed early season greenup following drought years to a lagged or ‘legacy’ effect, as soil inorganic nitrogen, accumulated during drought, becomes available for future plant uptake.     

Estimation of daily actual evapotranspiration from remotely sensed data under complex terrain over the upper Chao river basin in North China

Daily actual evapotranspiration over the upper Chao river basin in North China on 23 June 2005 was estimated based on the Surface Energy Balance Algorithm for Land (SEBAL), in which the parameterization schemes for calculating the instantaneous solar radiation and daily integrated radiation were improved by accounting for the variations in slope and azimuth of land surface and terrain shadow in mountainous areas. The evapotranspiration (ET) estimated from satellite data in this study for the whole watershed ranges from 0 mm to 7.3 mm day^?1 with a mean of 3.4 mm day^?1, which was validated by Penman–Monteith approaches for water body and paddy land. The comparison of ET estimates for a wide range of land cover types reflected distinct mechanisms of energy partition and water removal of various land cover types, showing differences in the spatial distribution pattern of ET, which could be not only the reflection but also the driving force of advection and local circulation that may violate the surface energy balance equation in the vertical direction. The spatial variation in daily solar radiation and ET estimates under the complex terrain of forest land were elaborated and evaluated by exploring the relationship between ET estimates and elevations for wood land and grass land. In addition, the utility and limitations of SEBAL's applicability to watersheds with various land cover types and complex terrain were analysed.     

Simplifying the revised three-temperature model for remotely estimating regional evapotranspiration and its application to a semi-arid steppe

Remote sensing is a feasible and economical way of mapping the spatial distribution of evapotranspiration (ET) at scales ranging from the regional to the global level. The revised three-temperature (3T-R) model, including land-surface temperature, reference temperature, and air temperature, is a promising approach for ET mapping, but aerodynamic resistance must be included in the model. The objectives of this study are to: (1) propose a simpler remote-sensing algorithm for estimating reference temperatures in the 3T-R model that would not depend on aerodynamic resistance; and (2) test the performance of the simplified 3T-R (3T-S) model. Assuming a pixel with the maximum surface temperature is a site without evaporation or transpiration, a method was proposed to replace the reference temperatures with paired maximum temperatures for soil and canopy, collected from a region with approximately equivalent solar radiation and terrain. A case study was conducted using Landsat Thematic Mapper (TM) images and synchronous ground observations performed in an Ecosystem Observation Station in northern China during the 2009 growing season. The results indicated that: (1) daily ET (ET_{3T_S}) estimated using the 3T-S model was close to the observational data, with a mean absolute error (MAE) of 0.24 mm d^?1 and a mean absolute percentage error (MAPE) of 9.78%; (2) the 3T-S model was much simpler than the 3T-R model with respect to its calculation procedures and data requirements, suggesting it might result in less error propagation, because the MAE and MAPE between ET_{3T_R} (daily ET estimated using the 3T-R model) and the observational data were 0.36 mm d^?1 and 14.71%, respectively. Therefore, it is concluded that the 3T-S model is a simpler method, but also an accurate way of estimating regional ET.     

On the feasibility of temporally upscaling instantaneous evapotranspiration using weather forecast information

Quantification of land-surface evapotranspiration (ET) is highly significant in water resources management, climate change studies, and numerical weather prediction. The constant reference evaporative fraction method (EF_r, the ratio of the actual to reference ET), which assumes that the daily EF_r is equal to that at the satellite overpass time, is a scheme that has been widely applied to upscale remotely sensed instantaneous ET to daily ET. To overcome the difficulties encountered in the acquisition of tower-based meteorological variables, this study investigates the feasibility of using publicly available weather forecast information to estimate the daily reference ET using the constant EF_r method. A two-source energy balance model is adopted to compute the instantaneous ET using Moderate-Resolution Imaging Spectroradiometer (MODIS) remote-sensing data acquired between January 2011 and October 2012 at the Yucheng Comprehensive Experimental Station in the North China Plain. The results show that the daily maximum and minimum air temperatures from weather forecast information are consistent with the corresponding ground-based measurements, with a bias of 0.8 K and a root mean square error (RMSE) of <2.0 K. The daily global solar radiation and daily wind speed were poorly forecast when compared with the ground-based measurements. Using the meteorological variables from the daily weather forecast information produced a small bias of 0.1 mm day^–1 and an RMSE of 0.6 mm day^–1 when the estimated daily reference ET was compared with that derived using the ground-based meteorological measurements. When the remotely sensed instantaneous ET and half-hourly reference ET were as accurate as the ground-based measurements, the upscaling method produced the daily ET, using the meteorological variables from the weather forecast information, with a bias of 0.1 mm day^–1 and an RMSE of 0.7 mm day^–1.     

A simple empirical method for estimating surface water vapour pressure using MODIS near-infrared channels: applications to northern Spain's Asturias region

In this article, we present a simple methodology for obtaining algorithms to estimate surface water vapour pressure (e ₀) over cloud-free land areas using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The algorithm obtained in this case is adapted to the particular climatic characteristics of the Asturias region, but the methodology can easily be extrapolated and used to obtain algorithms for other regions around the world. The proposed method estimates e ₀ from a simple linear combination of the radiances of the MODIS near-infrared (NIR) channels more commonly applied to total precipitable water (W) estimations. Comparison between the e ₀ data measured at the ground-based meteorological stations in Asturias (daily data from 2004) versus the values predicted using the proposed algorithm gives R ² = 0.76 and residual standard error (RSE) = 2.07 hPa (16%). The algorithm was tested using the data from 2008 obtained in Asturias and in two sites outside of Asturias with similar latitudes and radiosonde observations (La Coruña and Santander). The resulting validation demonstrates that the algorithm gives good results in Asturias (root-mean-square deviation (RMSD) = 2.50 hPa (19%) and bias = 1.26 hPa, with R ² = 0.65) and when La Coruña is included (R ² = 0.61), but that its validity is decreased when Santander is also included (R ² = 0.56).

The possibility of obtaining e ₀ from three global MODIS algorithms for W retrieval was also tested and compared to our algorithm. The results show that our algorithm gives better results than the International MODIS/Atmospheric InfraRed Sounder Processing Package (IMAPP) Water Vapour Near-Infrared (WVNIR) product and the Sobrino algorithm. The MODIS Total Precipitable Water (MOD05) product is worse than that obtained with our algorithm in Asturias (R ² = 0.61 vs. R ² = 0.65), but the two values are similar if the stations in La Coruña (R ² = 0.60) and Santander (R ² = 0.56) are included in the comparison. The dominant advantage of the novel algorithm proposed in this study is that it is simpler and can be produced quickly in real time.     

Evaluation of the B‐method for determining actual evapotranspiration in a boreal forest from MODIS data

Boreal forests occupy about 11% of the terrestrial surface and represent an important contribution to global energy balance. The ground measurement of daily evapotranspiration (LE_d) is very difficult due to the limitations on experiments. The objective of this paper is to present and explore the applicability of the B‐method for monitoring actual LE_d in these ecosystems. The method shown in this paper allows us to determine the surface fluxes over boreal forests on a daily basis from instantaneous information registered in a conventional meteorological tower, as well as the canopy temperature (T _c) retrieved by satellite. Images collected by the MODIS (moderate resolution imaging spectroradiometer) on board EOS‐Terra have been used for this study. The parameters of the model were calibrated from the SIFLEX‐2002 (Solar Induced Fluorescence Experiment 2002) campaign dataset in a northern boreal forest in Finland. A study of these parameters was made on an hourly basis in order to make the method applicable, not only at midday but within an interval of 7 h around it. This is an important advance with respect to the original formulation of this approach since the overpass time of satellites can be very variable. The comparison between T _c ground measured with a thermal infrared radiometer, and T _c retrieved from land surface temperature (LST) MODIS data, showed an estimation error of ±1.4°C for viewing angles from 5 to 60°. A complete sensitivity analysis was carried out and an estimation error of about ±35%, corresponding to the interval 10.00–11.00 h UTC, was shown as the lowest in LE_d retrieval. Finally, the method was validated over the study site using 21 MODIS images for 2002 and 2003. The results were compared with eddy‐correlation ground measurements. An accuracy of ±1.0 mm/day and an overestimation of 0.3 mm/day were shown in the LE_d retrieval.     

Numerical Modeling of Flame Inhibition by CF3Br

Abstract

This letter follows up the theoretical analysis by Seguin and Itier (1983) of a simplified linear relationship between daily evaporation and midday surface temperature. The use of a basic set of equations describing surface fluxesallows us to obtain sets of combined (ET_d, T_s—T_a) values for various surface roughness (z₀) values ET_d. being the daily evaporation and (T_s—T_a)the difference between surface and air temperature. The procedure, applied to two published experimental studies, allows us to refine the empirically obtained relationship. It is thereafter used to simulate the effect of surface roughness upon the coefficients of the simplified relationships: the origin a is found to be insensitive to z₀ (with a constant value of 1–27mm/d) and the slope b may be computed as a simple function of z₀, thus allowing us to propose a formulation applying to a large range of surface roughness (from 1mm to 10cm).     

Improvements in the estimation of daily minimum air temperature in peninsular Spain using MODIS land surface temperature

Air temperature (T_a) is a key variable in many environmental risk models and plays a very important role in climate change research. In previous studies we developed models for estimating the daily maximum (T_max), mean (T_mean), and minimum air temperature (T_min) in peninsular Spain over cloud-free land areas using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Those models were obtained empirically through linear regressions between daily T_a and daytime Terra-MODIS land surface temperature (LST), and then optimized by including spatio-temporal variables. The best T_mean and T_max models were satisfactory (coefficient of determination (R²) of 0.91–0.93; and residual standard error (RSE) of 1.88–2.25 K), but not the T_min models (R² = 0.80–0.81 and RSE = 2.83–3.00 K). In this article T_min models are improved using night-time Aqua LST instead of daytime Terra LST, and then refined including total precipitable water (W) retrieved from daytime Terra-MODIS data and the spatio-temporal variables curvature (c), longitude (λ), Julian day of the year (JD) and elevation (h). The best T_min models are based on the National Aeronautics and Space Administration (NASA) standard product MYD11 LST; and on the direct broadcast version of this product, the International MODIS/AIRS Processing Package (IMAPP) LST product. Models based on Sobrino’s LST1 algorithm were also tested, with worse results. The improved T_min models yield R² = 0.91–0.92 and RSE = 1.75 K and model validations obtain similar R² and RSE values, root mean square error of the differences (RMSD) of 1.87–1.88 K and bias = 0.11 K. The main advantage of the T_min models based on the IMAPP LST product is that they can be generated in nearly real-time using the MODIS direct broadcast system at the University of Oviedo.