Land surface temperature (LST) is one of the most important variables for applications relating to the physics of land surface processes. LST rapidly changes in both space and time, and knowledge of LST and its spatiotemporal variation is essential to understand the interactions between human activity and the environment. This study investigates the spatiotemporal variation of LST according to changes in elevation. The newest version (version 6) of MODIS LST data for 2015 was used. An area of 40,000 km² (200 × 200 km²) in northwest Vietnam with elevations ranging from 8 m to 3165 m was chosen as a case study. Our results showed that the drop in LST with increased elevation varied throughout the year during both the daytime and nighttime. The monthly averages in 2015 and an altitude increase of 1000 m resulted in a decrease in LST ranging from 3.8 °C to 6.1 °C and 1.5 °C to 5.8 °C for the daytime and nighttime, respectively. This suggests that in any study relating to the spatial distribution of LST, the effect of elevation on LST should be considered. In addition, the effects of land use/cover and elevation distribution on the relationship between LST and elevation are discussed.

Vietnam is a country highly vulnerable to climate change. Specifically, climate change has seriously impacted all aspects of Vietnam’s economic and social life, especially agricultural production. In this article, we focus on analyzing the current situation and the impacts of climate change on agricultural production and food security in Vietnam, especially in the Mekong River Delta (MRD) region. Vietnam’s climate change scenarios (RCP4.5 and RCP 8.5) have warned of serious increases in temperature, rainfall, and sea level rises for the MRD in coming times. This will lead to a risk of flooding in nearly 50% of the region’s area and will seriously affect agricultural production in many aspects such as soil quality degradation, scarcity of water resources, increased droughts and floods, reduced crop productivity, and so on. These impacts will reduce Vietnam’s food supply capacity, but do not compromise national food security from a short-term perspective. Faced with this situation, the Government of Vietnam has implemented many comprehensive measures to transform agriculture towards ecology, sustainability, and low carbon emissions, with the goal of green growth and neutral carbon emissions by 2050. In particular, the focus is on combining nature-based solutions with the application of modern science and technology in agricultural production, raising awareness and the response capacity of domestic people, with international cooperation in addressing climate change issues.

Recently, the MODerate resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) is considered one of the most suitable ways to retrieve air surface temperature (T_a) – one of the most important and widely used climate variables for a wide range of applications. In fact, many successful studies have been reported in many regions of the world. Each day, four MODIS LST data are available; from two sensors (Terra and Aqua) at two local overpass times (daytime and night-time). However, due to their different overpass times, most studies have used LST daytime and night-time for daily maximum (T_max) and minimum (T_min) air surface temperature estimation, respectively. Therefore, the performance of each individual LST data, the effect of the dynamic combination of these four LST data, the effect of land surface characteristics, and the effect of LST quality on the same estimation in the same region on the accuracy of estimated T_a remains unclear. In this study, we evaluated and tested all individual LST data as well as all possible combinations of the four MODIS LST data from two separate stations with distinct land surface characteristics in northwest Vietnam for 10 years (from 2004 to 2013) under two sky conditions (all clear sky conditions and only good data – i.e. Quality Control (QC) value of 0) for daily T_a (T_max, T_min, and T_mean) estimations. In addition, the mixed data of the two stations were also evaluated. Our results showed that Terra LST data have a higher correlation with T_a than Aqua LST; which is consistent for both stations and both quality conditions (all clear sky and only good data). A closer overpass time with T_max or T_min occurrence time did not guarantee a higher accuracy of T_a estimation. Using only good LST data produced a higher accuracy of T_a estimation than using all clear sky data. However, if the percentage of good data is low (i.e. less than 30%), then the all clear sky data will provide better results for T_max estimation. Comparing the performance of the different combinations when using the single station and mixed station data, combinations including at least one night-time LST produced stable and high accuracy T_min and T_mean estimates, while the combinations with only daytime LST produced very low accuracy results. For T_max estimation, the results were less impacted by LST quality; however, they were strongly impacted by different combinations and land surface characteristics.

Land use planning is an important task for every country in the world to ensure the sustainable use of land resources for economic and social development activities. However, many land use plans have not achieved their desired effectiveness due to a lack of consideration and evaluation of factors affecting the land use planning implementation process. This study was conducted to answer the following question: what are the main factors influencing the land use planning process in Vietnam? In this study, we use multivariate regression to identify the main factors influencing land use planning in Vietnam, including the economic factor group; institutional and policy factor group; urbanisation factor group; planning factor group; social factor group; environmental factor group; and organisational and implementation factor group. The results identified five groups of factors influencing land use planning in the Dan Phuong district through the influencing factor analysis method, including policy (Po), economy (Ec), society (So), environment (En), and others (Ot). In addition, the linear regression model obtained for land use planning in Dan Phuong district is as follows: land use planning = 0.408Po + 0.454Ec + 0.398So + 0.368En + 0.259Ot. In which the group of factors with the strongest influence was the Eco factors (β = 0.454), the second was the Po factors (β = 0.408), the third was the So factors (β = 0.398), the fourth was the En factors (β = 0.368), and lastly, there were the Ot factors (β = 0.259). The results of the analysis were used to plan land use effectively and according to local conditions. This can help managers to find solutions for the sustainable use of land resources in the future.