Remotely sensed data, with high spatial and temporal resolutions, can hardly be provided by only one sensor due to the tradeoff in sensor designs that balance spatial resolutions and temporal coverage. However, they are urgently needed for improving the ability of monitoring rapid landscape changes at fine scales (e.g., 30 m). One approach to acquire them is by fusing observations from sensors with different characteristics (e.g., Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS)). The existing data fusion algorithms, such as the Spatial and Temporal Data Fusion Model (STDFM), have achieved some significant progress in this field. This paper puts forward an Enhanced Spatial and Temporal Data Fusion Model (ESTDFM) based on the STDFM algorithm, by introducing a patch-based ISODATA classification method, the sliding window technology, and the temporal-weight concept. Time-series ETM+ and MODIS surface reflectance are used as test data for comparing the two algorithms. Results show that the prediction ability of the ESTDFM algorithm has been significantly improved, and is even more satisfactory in the near-infrared band (the contrasting average absolute difference [AAD]: 0.0167 vs. 0.0265). The enhanced algorithm will support subsequent research on monitoring land surface dynamic changes at finer scales.

Complex terrain, commonly represented by mountainous region, occupies nearly one-quarter of the Earth’s continental areas. An accurate understanding of water cycle, energy exchange, carbon cycle, and many other biogeophysical or biogeochemical processes in this area has become more and more important for climate change study. Due to the influences from complex topography and rapid variation in elevation, it is usually difficult for field measurements to capture the land-atmosphere interactions well, whereas land surface model (LSM) simulation provides a good alternative. A systematic review is introduced by pointing out the key issues for land surface processes simulation over complex terrain: (1) high spatial heterogeneity for land surface parameters in horizontal direction, (2) big variation of atmospheric forcing data in vertical direction related to elevation change, (3) scale effect on land surface parameterization in LSM, and (4) two-dimensional modelling which considers the gravity influence. Regarding these issues, it is promising for better simulation at this special region by involving higher spatial resolution atmospheric forcing data which can reflect the influences from topographic changes and making necessary improvements on model structure related to topographic factors. In addition, the incorporation of remote sensing techniques will significantly help to reduce uncertainties in model initialization, simulation, and validation.

The scientific community has widely reported the impacts of climate change on the Central Himalaya. To qualify and quantify these effects, long-term land surface temperature observations in both the daytime and nighttime, acquired by the Moderate Resolution Imaging Spectroradiometer from 2000 to 2017, were used in this study to investigate the spatiotemporal variations and their changing mechanism. Two periodic parameters, the mean annual surface temperature (MAST) and the annual maximum temperature (MAXT), were derived based on an annual temperature cycle model to reduce the influences from the cloud cover and were used to analyze their trend during the period. The general thermal environment represented by the average MAST indicated a significant spatial distribution pattern along with the elevation gradient. Behind the clear differences in the daytime and nighttime temperatures at different physiographical regions, the trend test conducted with the Mann-Kendall (MK) method showed that most of the areas with significant changes showed an increasing trend, and the nighttime temperatures exhibited a more significant increasing trend than the daytime temperatures, for both the MAST and MAXT, according to the changing areas. The nighttime changing areas were more widely distributed (more than 28%) than the daytime changing areas (around 10%). The average change rates of the MAST and MAXT in the daytime are 0.102 °C/yr and 0.190 °C/yr, and they are generally faster than those in the nighttime (0.048 °C/yr and 0.091 °C/yr, respectively). The driving force analysis suggested that urban expansion, shifts in the courses of lowland rivers, and the retreat of both the snow and glacier cover presented strong effects on the local thermal environment, in addition to the climatic warming effect. Moreover, the strong topographic gradient greatly influenced the change rate and evidenced a significant elevation-dependent warming effect, especially for the nighttime LST. Generally, this study suggested that the nighttime temperature was more sensitive to climate change than the daytime temperature, and this general warming trend clearly observed in the central Himalayan region could have important influences on local geophysical, hydrological, and ecological processes.

Soil moisture is a vital parameter in various land surface processes, and microwave remote sensing is widely used to estimate regional soil moisture. However, the application of the retrieved soil moisture data is restricted by its coarse spatial resolution. To overcome this weakness, many methods were proposed to downscale microwave soil moisture data. The traditional method is the microwave-optical/IR synergistic approach, in which land surface temperature (LST), vegetation index and surface albedo are key parameters. However, due to the uncertainty in absolute LST estimation, this approach is partly dependent on the accuracy of LST estimation. To eliminate the impacts of LST estimation, an improved downscaling method is proposed in this study to downscale Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) Land Parameter Retrieval Model (LPRM) soil moisture product with visible and thermal data of Meteosat Second Generation (MSG)—Spinning Enhanced Visible and Infrared Imager (SEVIRI). Two temperature temporal variation parameters related to soil moisture, including mid-morning rising rate and daily maximum temperature time, are introduced in the proposed method to replace LST. The proposed method and the traditional method are both applied to the Iberian Peninsula area for July and August 2007. Comparison of the two results shows that the coefficient of determination (R-squared) has an average improvement of 0.08 and the root mean square error has a systematic decrease. The downscaled soil moisture by the proposed method was validated by REMEDHUS soil moisture network in the study area, and site specific validation gets poor correlation between the two datasets because of the low spatial representativeness of site measurement for one MSG-SEVIRI pixel. Although the comparisons at 15 km and network scale show an improvement over the site specific comparison, it is found that the downscaling method systematically degrades the accuracy in soil moisture data, with a R-squared of 0–0.4 and 0.218 for the downscaled data set against 0.7–0.8 and 0.571 for AMSR-E data at 15 km scale and the network scale respectively.

Maximal light use efficiency (LUE) is an important ecological index of a vegetation essential attribute, and a key parameter of the LUE-based model for estimating large-scale vegetation productivity by remote sensing technology. However, although currently used in different models there still exists extensive controversy. This paper takes the Zoige Plateau in China as a case area to develop a new approach for estimating the maximal LUEs for different vegetation. Based on an existing land cover map and MODIS NDVI product, the linear unmixing method with a moving window was adopted to estimate the time-series NDVI for different end members in a MODIS NDVI pixel; then Particle Swarm Optimizer (PSO) was applied to search for the optimization of LUE retrievals through the CASA (Carnegie-Ames-Stanford Approach) model combined with time-series NDVI and ground measurements. The derived maximal LUEs present significant differences among various vegetation types. These are 0.669 gC·MJ⁻¹, 0.450 gC·MJ⁻¹ and 0.126 gC·MJ⁻¹ for the xerophilous grasslands with high, moderate and low vegetation fraction respectively, 0.192 gC·MJ⁻¹ for the hygrophilous grasslands, and 0.125 gC·MJ⁻¹ for the helobious grasslands. The field validation shows that the estimated net primary productivity (NPP) by the derived maximal LUE is closely related to the ground references, with R² of 0.8698 and root-mean-square error (RMSE) of 59.37 gC·m⁻²·a⁻¹. This indicates that the default set in the CASA model is not suitable for NPP estimation for the regional mountain area. The derived maximal LUEs can significantly improve the capability of NPP mapping, and open up the perspective for long-term monitoring of vegetation ecological health and ecosystem productivity by combining the LUE-based model with remote sensing observations.