The Bangkok Metropolitan Area is an example of urban sprawl that has undergone rapid expansion and major changes in urban composition and building configuration. This city is now faced with the urban heat island phenomenon. Initial observations of land surface temperature (LST) in recent years have indicated that LST has tended to increase in both urban and suburban areas. The purposes of this study were to: (1) assess different land cover types and combinations of land cover composition along an LST gradient, and (2) investigate effect of building configuration types on the LST in densely urban areas. We analyzed the urban composition variation of 4,960 land cover samples using a 500 m × 500 m grid and configuration metrics in spatial patterns from Landsat 8 data and a high-resolution database of buildings obtained from GIS data of the Bangkok Metropolitan Area. The results indicated that the fraction of land cover composition was strongly related to LST. Our results suggested that LST can be effectively mitigated by using below green (shrubs, grasses, and yards), above green (trees, orchards, mangroves, and perennial plants) and water land cover. By increasing tree canopy to around 20%, water body to around 30% or green yard/shrub to around 40% of the built-up areas, it is possible to reduce LST significantly. Urban configurations (edge density, patch density, large patch, mean patch size, building height, compactness of building, building type, and building use) affecting on LST were studied. Increased edge density, patch density of buildings, and building height caused reductions in LST. Distribution of LST patterns can be significantly related with urban composition or land configuration features. The results of this study can increase understanding of the interaction between urban composition and configuration metrics. Moreover, our findings may be useful in the mitigation of the impact of LST in urban-sprawl cities.

Soil hydraulic parameters are essential inputs to agricultural and hydrologic models for simulating soil moisture. These parameters however are difficult to obtain especially when the application is aimed at the regional scale. Laboratory and field methods have been used for quantifying soil hydraulic parameters but they are proved to be laborious and expensive. An emerging alternative of estimating soil hydraulic parameters is soil moisture model inversion using remote sensing (RS) data. Although soil hydraulic parameters could not be derived directly from remote sensing, they could be quantified by the inverse modeling of RS data. In this study, we conducted a multi-criteria inverse modeling approach to estimate the rootzone soil hydraulic parameters in a rainfed rice field at depths 3, 12, 28 and 60 cm, respectively. The conditioning data used in the inverse modeling are leaf area index (LAI) and actual evapotranspiration (ET a) from satellite imageries, and soil moisture (SM) data from in situ measurements. The performances of all the model inversion experiments were evaluated against observed soil moisture in the field, and measured LAI during the growing season. The results showed that using remotely sensed LAI and ET a in the inverse modeling provided a good matching between observed and simulated soil moisture down to 28 cm depth from the soil surface. With the addition of soil moisture information from the site, the model inversion significantly improved the soil moisture simulation up to a depth of 60 cm.

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.