Spatio-temporal coupling of land-surface and energy balance parameters with monsoon rainfall using remote-sensing technology

This research paper focuses on the spatio-temporal coupling of monsoon rainfall with land-surface and energy balance parameters, which are important for understanding hydrological, climatological, and agricultural aspects at local, regional, and global scales. The dynamics of land-surface and energy balance parameters influence summer monsoon over India. Time scales of the land-surface response to monsoon forcing are different for different land-surface conditions due to different physical processes governing the land-surface–atmosphere exchange through energy balance components. A synergy of satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) (0.05° × 0.05°) for obtaining land-surface and energy balance parameters, and the Atmospheric Infrared Sounder (AIRS) (1° × 1°) for obtaining atmospheric parameter and gridded rainfall data (1° × 1°) from the Indian Meteorological Department (IMD) during June to September for three consecutive years (2009–2011) representing low to normal rainfall, were used to develop a coupling model in the spatio-temporal domain. Surface energy fluxes were estimated using a surface energy balance model by partitioning available energy at the surface into latent heat flux (LE) and sensible heat flux (H) through the evaporative fraction (EF) concept of a 2D land-surface temperature (LST)-albedo scatter plot. The coupling models were based on statistical methods developed at both temporal and spatial scales to explain the linking of various parameters with monsoon rainfall. A significant positive relationship was obtained between rainfall and land-surface parameters such as normalized difference vegetation indices (NDVIs), and soil wetness/energy balance parameters such as LE and EF, whereas a strong negative relationship was obtained between rainfall and surface radiation parameters (LST and albedo)/energy balance parameters such as soil heat flux (G) and net radiation (R_n). This approach has demonstrated its simplicity with remote sensing technology and could identify ‘at risk’ regions at spatio-temporal scales based on coupling models.     

INSAT-3D and MODIS retrieved sea surface temperature validation and assessment over waters surrounding the Indian subcontinent

The INSAT-3D imager (4 km) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on-board Aqua and Terra space-platforms level-2 (1 km) sea surface temperature (SST_skin) product accuracy has been analysed over waters surrounding the Indian subcontinent by indirect comparison method using collocated bulk in-situ measurements (SST_depth) for 3 years (October 2013–October 2016). Statistical results show that root mean square error of all the three satellites is in range of around 0.60–0.70°C. Retrieval error is found to be slightly more in case of validation against iQuam data set. INSAT-3D is showing more underestimation with bias ranging from about ?0.16°C to ?0.20°C than MODIS sensor having bias in range of about 0.06°C to ?0.12°C. All the three missions are slightly underestimating over open-ocean with bias ranging in 0–0.17°C. INSAT-3D is significantly underestimating in-situ observations over the Arabian Sea (approximate bias = 0.27°C). Seasonal validation analysis reveals relatively high retrieval error during monsoon season than pre-monsoon and post-monsoon seasons. MODIS sensor is showing significant underestimation during monsoon with bias ranging from approximately ?0.29°C to ?0.58°C. Overall, all the three missions are performing similarly well over the study area.     

Modelling gross primary production of a tropical semi-deciduous forest in the southern Amazon Basin

Semi-deciduous forest in the Amazon Basin is sensitive to temporal variation in surface water availability that can limit seasonal rates of leaf and canopy gas exchange. We estimated the seasonal dynamics of gross primary production (GPP) over 3 years (2005–2008) using eddy covariance and assessed canopy spectral reflectance using MODIS imagery for a mature tropical semi-deciduous forest located near Sinop, Mato Grosso, Brazil. A light-use efficiency model, known as the Vegetation Photosynthesis Model (VPM), was used to estimate seasonal and inter-annual variations in GPP as a function of the enhanced vegetation index (EVI), the land surface water index (LSWI), and local meteorology. Our results indicate that the standard VPM was incapable of reproducing the seasonal variation in GPP, primarily because the model overestimated dry-season GPP. In the standard model, the scalar function that alters light-use efficiency (ε_g) as a function of water availability (W_scalar) is calculated as a linear function of the LSWI derived from MODIS; however, the LSWI is negatively correlated with several measures of water availability including precipitation, soil water content, and relative humidity (RH). Thus, during the dry season, when rainfall, soil water content, and RH are low, LSWI, and therefore, W_scalar, are at a seasonal maximum. Using previous research, we derived new functions for W_scalar based on time series of RH and photosynthetic photon flux density (PPFD) that significantly improved the performance of the VPM. Whether these new functions perform equally well in water stressed and unstressed tropical forests needs to be determined, but presumably unstressed ecosystems would have high cloud cover and humidity, which would minimize variations in W_scalar and GPP to spatial and/or temporal variation in water availability.     

Analysis of energy fluxes and land surface parameters in a grassland ecosystem: a remote sensing perspective

A remote sensing‐based land surface characterization and flux estimation study was conducted using Landsat data from 1997 to 2003 on two grazing land experimental sites located at the Agricultural Research Services (ARS), Mandan, North Dakota. Spatially distributed surface energy fluxes [net radiation (R _n), soil heat flux (G), sensible heat (H), latent heat (LE)] and surface parameters [emissivity (ε), albedo (α), normalized difference vegetation index (NDVI) and surface temperature (T _sur)] were estimated and mapped at a pixel level from Landsat images and weather information using the Surface Energy Balance Algorithm for Land (SEBAL) procedure as a function of grazing land management: heavily grazed (HGP) and moderately grazed pastures (MGP). Energy fluxes and land surface parameters were mapped and comparisons were made between the two sites. Over the study period, H, ε and T _sur from HGP were higher by 6.7%, 18.2% and 2.9% than in MGP, respectively. The study also showed that G, LE and NDVI were higher by 1.3%, 1.6% and 7.4% for MGP than in HGP, respectively. The results of this study are beneficial in understanding the trends of land surface parameters, energy and water fluxes as a function of land management.     

Satellite and ground-based monitoring of smoke in the atmosphere during the summer wildfires in European Russia in 2010 and Siberia in 2012

Using Moderate Resolution Imaging Spectroradiometer (MODIS) (Aqua and Terra satellites) and in situ observations, a comparative analysis of two large-scale smoke events caused by the summer wildfires in European Russia (ER) in 2010 and Western Siberia (WS) in 2012 was carried out. In the 5-day periods of the extreme smoke pollution (5–9 August 2010 in ER and 27–31 July 2012 in WS), the number of active fires in the equal territories, confined by the coordinates 47°–65° N, 25°–55° E and 51°–70° N, 71°–104° E, was found to be 4754 for ER and 3823 for WS. With this, the regional mean aerosol optical depths (AODs) were found to be (1.02 ± 0.02) and (1.00 ± 0.04), not much differing for both the events. The regional mean aerosol radiative forcing effects at the top (R₁) and the bottom (R₂) of the atmosphere over ER/WS according to MODIS observations were estimated to be (?61 ± 1) and (?54 ± 2) W m^?2, and (?107 ± 2) and (?96 ± 3) W m^?2, respectively. At the same time, the local values of AOD and the local absolute values of R₁ and R₂ over WS were considerably higher than those over ER. MODIS AOD (L3) data during the wildfires of 2010 were validated by AOD data obtained by the sun-sky photometer CIMEL, operating at the AERONET station Zvenigorod. The rates of radiative heating of the smoky atmosphere over ER and WS were also estimated and compared with the existed temperature anomalies, obtained using National Centers for Environmental Prediction National Center for Atmospheric Research reanalysis data. Optical and microphysical properties of smoke aerosols during the wildfires in ER and WS also revealed some similar characteristics. The aerosols were mostly found in the submicron-size fraction and characterized by very high single-scattering albedos (0.95–0.98). In the dense smoke conditions, the degree of linear polarization at the scattering angle 90° during both the events decreased to negative values ranging between ?0.1 and ?0.15. The optical properties of smoke aerosols were mainly conditioned by unusually narrow particle size distribution.     

Variability of chlorophyll-a off the southwest coast of India

Coastal upwelling off the southwest coast of India during the southwest monsoon is a well-known phenomenon that enhances the chlorophyll-a (chl-a) biomass. The present study explores this property and examines the variability of surface chl-a using satellite data obtained from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) for the period from September 1997 to December 2010. Spatial variability showed substantial cross-shore as well as along-shore gradients during the southwest monsoon. Temporal variability in chl-a was studied in conjunction with satellite observations on sea-surface temperature, sea-surface height anomaly, winds, and currents. The results revealed the dominant influence of the West India Coastal Current on chl-a variability during the upwelling and downwelling periods. Moreover, noticeable intra- and inter-annual variability was observed in the parameter. Therefore, an empirical mode decomposition (EMD) method was used to identify the oscillations influencing variability. SeaWiFS chl-a data for the period 2008–2010 were omitted from this analysis due to gaps in the record. EMD analysis revealed oscillations ranging from seasonal to a five-year periodicity. Quasi-biennial oscillations are identified as the dominant factor causing inter-annual variability in chlorophyll in the study area, compared with the El-Niño Southern Oscillation and Indian Ocean Dipole. Chl-a was also studied in two smaller grids of size 0.5° × 0.5° separated by around 300 km and representing coastal and offshore areas, to understand the nature of variability in these areas. The annual range of variability was high (0.1–8.0 mg m^?3) near the coast consequent on high upwelling intensity, and very low (about 0.1 mg m^?3) in the offshore grid due to the absence of upwelling.     

East Hainan upwelling fronts detected by remote sensing and modelled in summer

Using the Belkin and O’Reilly algorithm and high-resolution (1 km) satellite sea surface temperature (SST) and chlorophyll-a (chl-a) data from 2002 to 2011, fronts were detected off the east/northeast coast of Hainan Island, South China Sea. These fronts were mainly produced by upwelling off eastern Hainan Island, through which cold, high-salinity, high-density, and nutrient-rich bottom water was brought to the surface and subsurface and then transported to the northeast of Hainan Island by the along-shore currents. The fronts are anisotropic, with a dominant orientation SSW–NNE. A three-dimensional ocean model forced by the Quick Scatterometer (QuikSCAT) winds was employed to study the three-dimensional structure of these fronts as well as the relationship between the fronts and upwelling or summer monsoon. The results show that the front intensity (cross-frontal gradient) is strongly correlated with the along-shore local winds, and has a strong seasonal and a weak inter-annual variation with a maximum of about 0.5°C km^–1 at the subsurface (about 15 m) rather than the surface.     

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.     

Relationships between vegetation indices,fractional cover retrievals and the structure and composition of Brazilian Cerrado natural vegetation

This study explores the use of the relationship between the normalized difference vegetation index (NDVI) and the shortwave infrared ratio (SWIR32) vegetation indices (VI) to retrieve fractional cover over the structurally complex natural vegetation of the Cerrado of Brazil using a time series of imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS). Data from the EO-1 Hyperion sensor with 30 m pixel resolution is used to sample geographic and seasonal variation in NDVI, SWIR32, and the hyperspectral cellulose absorption index (CAI), and to derive end-member values for photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and bare soil (BS) from a suite of protected and/or natural vegetation sites across the Cerrado. The end-members derived from relatively pure 30 m pixels are then applied to a 500 m pixel resolution MODIS time series using linear spectral unmixing to retrieve PV, NPV, and BS fractional cover (F_PV, F_NPV, and F_BS). The two-way interaction response of MODIS-equivalent NDVI and SWIR32 was examined for regions of interest (ROI) collected within protected areas and nearby converted lands. The MODIS NDVI, SWIR32 and retrieved F_PV, F_NPV, and F_BS are then compared to detailed cover and structural composition data from field sites, and the influence of the structural and compositional variation on the VIs and cover fractions is explored. The hyperion ROI analysis indicated that the two-way NDVI–SWIR32 response behaved as an effective surrogate for the two-way NDVI–CAI response for the campo limpo/grazed pasture to cerrado sensu stricto woody gradient. The SWIR32 sensitivity to the NPV and BS variation increased as the dry season progressed, but Cerrado savannah exhibited limited dynamic range in the NDVI–CAI and NDVI–SWIR32 two-way responses compared to the entire landscape, which also comprises fallow croplands and forests. Validation analysis of MODIS retrievals with Quickbird-2 images produced an RMSE value of 0.13 for F_PV. However, the RMSE values of 0.16 and 0.18 for F_BS and F_NPV, respectively, were large relative to the seasonal and inter-annual variation. Analysis of site composition and structural data in relation to the MODIS-derived NDVI, SWIR32 and F_PV, F_NPV, and F_BS, indicated that the VI signal and derived cover fractions were influenced by a complex mix of structure and cover but included a strong year-to-year seasonal effect. Therefore, although the MODIS NDVI–SWIR32 response could be used to retrieve cover fractions across all Cerrado land covers including bare cropland, pastures and forests, sensitivity may be limited within the natural Cerrado due to sub-pixel heterogeneity and limited BS and NPV sensitivity.     

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.     

Estimation of spatially enhanced soil moisture combining remote sensing and artificial intelligence approaches

The main objective of this study is to combine remote-sensing and artificial intelligence (AI) approaches to estimate surface soil moisture (SM) at 100 m spatial and daily temporal resolution. The two main variables used in the Triangle method, that is, land-surface temperature (LST) and vegetation cover, were downscaled and calculated at 100 m spatial resolution. LSTs were downscaled applying the Wavelet-Artificial Intelligence Fusion Approach (WAIFA) on Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat imageries. Vegetation fractions were also estimated at 100 m spatial resolution using linear spectral un-mixing and Wavelet–AI models. Vegetation indices (VIs) were replaced with the vegetation fractions obtained from sub-pixel classification in the T_s–VI triangle space. The downscaled data were then used for calculating the evaporative fraction (EF), temperature-vegetation-dryness index (TVDI), vegetation temperature condition index (VTCI), and temperature-vegetation index (TVX) at 100 m spatial resolution. Thereafter, surface SM modelling was performed using a combination of Particle Swarm Optimization with Adaptive Neuro Fuzzy Inference System (PSO-ANFIS) and Support Vector Regression (PSO-SVR) modelling approaches. Results showed that the best input data set to estimate SM includes EF, TVDI, T_s, F_vegetation, F_soil, temperature (T), precipitation at time t (P_t, P_{t – 1}, P_{t – 2}), and irrigation (I). It was also confirmed that PSO-SVR outperformed the PSO-ANFIS modelling approach and could estimate SM with a coefficient of determination (R²) of 0.93 and a root mean square error (RMSE) of 1.29 at 100 spatial resolution. Range of error was limited between ?2.64% and 2.8%. It was also shown that the method proposed by Tang et al., (2010) improved the final SM estimations.     

Comparison of integrating LAS/MODIS data into a land surface model for improved estimation of surface variables through data assimilation

In this article, land surface temperature (LST) and sensible heat flux (H) data assimilation schemes were developed separately using the ensemble Kalman filter (EnKF) and the common land model (CoLM). Surface measurements of ground temperature, H, and latent heat flux (LE) collected at the Yucheng (longitude: 116° 36′ E; latitude: 36° 57′ N) and Arou (longitude: 100° 27′ E; latitude: 38° 02′ N) experimental stations were compared with the predictions by assimilating different observation sources into the CoLM. The results showed that both LST and H data assimilation schemes could improve the estimation of ground temperature and H. The root mean square error (RMSE) compared between the predictions and in situ measurements decreased more significantly with the assimilation of values of H measured by a large aperture scintillometer (LAS). Assimilating Moderate Resolution Imaging Spectroradiometer (MODIS) LST only slightly improved the predictions of H and ground temperature. Daytime to night-time comparison results using both assimilation schemes also indicated that accurately quantifying model, prediction, and observation error would improve the efficiency of the assimilation systems. The newly developed land data assimilation schemes have proved to be a feasible and practical method to improve the predictions of heat fluxes and ground temperature from CoLM. Moreover, integrating multisource data (LAS and MODIS LST) simultaneously into the land surface model is believed to result in an efficient and robust way to improve the accuracy of model predictions from a theoretical point of view.     

Bimodal variation of SST and related physical processes over the North Indian Ocean: special emphasis on satellite observations

Using sea surface temperature (SST) and wind speed retrieved by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), for the period of 1998–2003, we have studied the annual cycle of SST and confirmed the bimodal distribution of SST over the north Indian Ocean. Detailed analysis of SST revealed that the summer monsoon cooling (winter cooling) over the eastern Arabian Sea (Bay of Bengal) is more prominent than winter cooling (summer monsoon cooling). A sudden drop in surface short wave radiation by 57 W m^?2 (74 W m^?2) and rise in kinetic energy per unit mass by 24 J kg^?1 (26 J kg^?1) over the eastern Arabian Sea (Bay of Bengal) is observed in summer monsoon cooling period. The subsurface profiles of temperature and density for the spring warming and summer monsoon cooling phases are studied using the Arabian Sea Monsoon Experiment (ARMEX) data. These data indicate a shallow mixed layer during the spring warming and a deeper mixed layer during the summer monsoon cooling. Deepening of the mixed layer by 30 to 40 m with corresponding cooling of 2°C is found from warming to summer monsoon cooling in the eastern Arabian Sea. The depth of the 28°C isotherm in the eastern Arabian Sea during the spring warming is 80 m and during summer monsoon cooling it is about 60 m, while over the Bay of Bengal the 28°C isotherm is very shallow (35 m), even during the summer monsoon cooling. The time series of the isothermal layer depth and mixed layer depth during the warming phase revealed that the formation of the barrier layer in the spring warming phase and the absence of such layers during the summer cooling over the Arabian Sea. However, the barrier layer does exist over the Bay of Bengal with significant magnitude (20–25 m). The drop in the heat content with in first 50 m of the ocean from warming to the cooling phase is about 2.15 × 10⁸ J m^?2 over the Arabian Sea.     

Integrating two layers of soil moisture parameters into the MOD16 algorithm to improve evapotranspiration estimations

To integrate soil moisture into the algorithm of the Moderate Resolution Imaging Spectroradiometer (MODIS) global evapotranspiration (ET) project (MOD16), two improvements were implemented: two layers of relative soil moisture parameters were combined with a surface resistance model; and the complementary relationship was replaced with the Penman-Monteith (P-M) method to estimate the dry soil surface evaporation. In the vegetation surface resistance model, a multiplier R_sm1 was added, and the influence of the relative soil moisture in the root zone was accounted for. In the soil surface resistance model, an empirical exponential relationship was used. To calculate the relative soil moisture parameters, soil hydraulic parameters, such as field capacity (Fc), wilting point (Wp), and saturation point (Sp), were estimated according to the soil texture information; these parameters were used as critical values to estimate the relative soil moisture. Both the MOD16 method and improved method were validated using ET flux data collected at nine flux-tower sites in the USA from 2000 to 2009. The mean absolute BIAS and the root mean square error (RMSE) decreased from 0.36 to 0.30 mm day^–1 and from 1.14 to 0.97 mm day^–1, respectively, after integrating the soil moisture parameters. Meanwhile, the mean correlation coefficient (R) for the nine sites increased from 0.54 to 0.70. Therefore, the improved method performed better than the MOD16 method. Furthermore, the uncertainties associated with the MODIS leaf area index (LAI) products, flux-tower measurements, soil texture, soil moisture, and model parameters were analysed. The outlook for future modifications was also discussed.     

MERIS observations of chlorophyll-a dynamics in Erhai Lake between 2003 and 2009

The Medium Resolution Imaging Spectrometer (MERIS) was used to investigate the spatial and temporal dynamics of chlorophyll-a (chl-a) in Erhai Lake, the second largest freshwater lake in the Yunnan province of China. Six chl-a retrieval models, including four Basic ERS & Envisat (A)ATSR and Meris Toolbox (BEAM) software-incorporated algorithms and MERIS three-band and two-band models, were validated to find the best fit to extract chl-a concentration in Erhai Lake. With a chl-a range of 5–15 mg m^–3, the Lakes/Eutrophic method showed the best performance. The algorithm was then applied to eight-year cloud-free MERIS images between 2003 and 2009, with seasonal and inter-annual variability analysed. Long-term chl-a distributions of Erhai Lake revealed significant seasonal and inter-annual variability. The mean chl-a of the south lake was higher in summer (14.3 mg m^–3) than in spring (10.1 mg m^–3), while generally lower chl-a was found in the north lake with a mean chl-a of 6.4 mg m^–3 in spring and 9.0 mg m^–3 in summer, respectively. An increasing trend was found between 2006 and 2009, and the increasing rate was 12.9% for annual chl-a of the entire lake. While chl-a seasonality was attributed to the seasonal changes of the local temperature, the inter-annual variation was possibly linked to the discharged wastewater from Dali City. This work could provide critical information for decision-makers to manage Erhai Lake’s aquatic ecosystems.     

Evaluation of different orientation angle distributions within the X-Bragg scattering model for bare soil moisture estimation

In this paper, the applicability of three different orientation angle distributions of surface facets within the extended Bragg (X-Bragg) scattering model is investigated for estimation of soil moisture over bare surfaces using both Eigen-based and model-based polarimetric synthetic aperture radar (PolSAR) decomposition techniques. The three distributions considered for investigation in the X-Bragg model are uniform, half cosine, and the Lee distributions. In order to understand the sensitivity of the model using the three orientation angle distributions, key polarimetric parameters, such as scattering entropy (H), scattering anisotropy (A), scattering mechanism (α), cross-pol power (T₃₃), linear T₁₂ coherence (|γ_{(HH+VV)(HH–VV)}|), are simulated and analysed for various widths of distributions. The analysis of the simulated polarimetric parameters show that the Lee distribution has a reduced roughness validity range compared with the uniform and half cosine distributions. DLR E-SAR L-band data from the AgriSAR’2006 campaign over the Demmin test site in Northern Germany are inverted for soil moisture over bare surfaces. The inverted soil moisture from the physics-based X-Bragg model is compared with in situ measured TDR (time domain reflectometry) soil moisture values. The inversion results using the Eigen-based decomposition reveal similar root mean square error (RMSE = 14 vol.%) and inversion rates for three distributions. The model-based decomposition inversion results obtained at various fixed widths of distributions reveal that the Lee distribution shows less RMSE of 8 vol.% and high inversion rates for moderate surface roughness (ks = 0.5) as compared with half cosine and uniform distributions.     

Development of a global evapotranspiration algorithm based on MODIS and global meteorology data

The objective of this research is to develop a global remote sensing evapotranspiration (ET) algorithm based on Cleugh et al.'s [Cleugh, H.A., R. Leuning, Q. Mu, S.W. Running (2007) Regional evaporation estimates from flux tower and MODIS satellite data. Remote Sensing of Environment 106, page 285-304- 2007 (doi: 10.1016/j.rse.2006.07.007).] Penman-Monteith based ET (RS-PM). Our algorithm considers both the surface energy partitioning process and environmental constraints on ET. We use ground-based meteorological observations and remote sensing data from the MODerate Resolution Imaging Spectroradiometer (MODIS) to estimate global ET by (1) adding vapor pressure deficit and minimum air temperature constraints on stomatal conductance; (2) using leaf area index as a scalar for estimating canopy conductance; (3) replacing the Normalized Difference Vegetation Index with the Enhanced Vegetation Index thereby also changing the equation for calculation of the vegetation cover fraction (F_C); and (4) adding a calculation of soil evaporation to the previously proposed RS-PM method.We evaluate our algorithm using ET observations at 19 AmeriFlux eddy covariance flux towers. We calculated ET with both our Revised RS-PM algorithm and the RS-PM algorithm using Global Modeling and Assimilation Office (GMAO v. 4.0.0) meteorological data and compared the resulting ET estimates with observations. Results indicate that our Revised RS-PM algorithm substantially reduces the root mean square error (RMSE) of the 8-day latent heat flux (LE) averaged over the 19 towers from 64.6 W/m² (RS-PM algorithm) to 27.3 W/m² (Revised RS-PM) with tower meteorological data, and from 71.9 W/m² to 29.5 W/m² with GMAO meteorological data. The average LE bias of the tower-driven LE estimates to the LE observations changed from 39.9 W/m² to − 5.8 W/m² and from 48.2 W/m² to − 1.3 W/m² driven by GMAO data. The correlation coefficients increased slightly from 0.70 to 0.76 with the use of tower meteorological data. We then apply our Revised RS-PM algorithm to the globe using 0.05° MODIS remote sensing data and reanalysis meteorological data to obtain the annual global ET (MODIS ET) for 2001. As expected, the spatial pattern of the MODIS ET agrees well with that of the MODIS global terrestrial gross and net primary production (MOD17 GPP/NPP), with the highest ET over tropical forests and the lowest ET values in dry areas with short growing seasons. This MODIS ET product provides critical information on the regional and global water cycle and resulting environment changes.     

Estimation and comparison of evapotranspiration from MODIS and AVHRR sensors for clear sky days over the Southern Great Plains

Evapotranspiration (ET) cannot be measured directly from satellite observations but remote sensing can provide a reasonably good estimate of evaporative fraction (EF), defined as the ratio of ET and available radiant energy. It is feasible to estimate EF using a contextual interpretation of radiometric surface temperature (T_o) and normalized vegetation index (NDVI) from multiple satellites. Recent studies have successfully estimated net radiation (R_n) over large heterogeneous areas for clear sky days using only remote sensing observations. With distributed maps of EF and R_n, it is now possible to explore the feasibility and robustness of ET estimation from multiple satellites. Here we present the results of an extensive inter-comparison of spatially distributed ET and related variables (NDVI, T_o, EF and R_n) derived from MODIS and AVHRR sensors onboard EOS Terra, NOAA14 and NOAA16 satellites respectively. Our results show that although, NDVI and T_o differ with the sensor response functions and overpass times, contextual space of NDVI-T_o diagram gives comparable estimates of EF. The utility of different sensors is demonstrated by validating the estimated ET results to ground flux stations over the Southern Great Plains with a root mean square error of 53, 51 and 56.24 Wm^− 2, and a correlation of 0.84, 0.79 and 0.77 from MODIS, NOAA16 and NOAA14 sensors respectively.     

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.     

Evaluation of MODIS surrogates for meteorological humidity data in east Africa

Satellite remote-sensing technology has shown promising results in characterizing the environment in which plants and animals thrive. Scientists, biologists, and epidemiologists are adopting remotely sensed imagery to compensate for the paucity of weather information measured by weather stations. With measured humidity from three stations as baselines, our study reveals that normalized difference vegetation index (NDVI) and atmospheric saturation deficit at the 780 hPa pressure level (D_MODIS), both of which were derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, were significantly correlated with station saturation deficits (D_stn) (|r| = 0.42–0.63, p < 0.001). These metrics have the potential to estimate saturation deficits over east Africa. Four to nine days of lags were found in the NDVI responding to D_stn. For the daily estimation of D_stn, D_MODIS yielded better performance than the NDVI. However, both of them poorly explained variation in daily D_stn using simple regression models (adj. R² = 0.17–0.39). When the estimation temporal scale was changed to 16 days, performance was similar, and both were better than daily estimations. For D_stn estimation at coarser geographic scales, given that many factors such as soil, vegetation, slope, aspect, and wind speed might complicate NDVI response lags and model construction, D_MODIS is preferable as a proxy to saturation deficit over ground due to its simple relationship with D_stn.