Sandy range land refers to a major component of grassland area types in the semi-arid area of northern China. Monitoring of vegetation and land surface temperature (LST) using remote sensing technology can help determine the degree of desertification in a regional and/or sub regional scale, as in the Horqin Sandy Land selected in this study. Correlation analysis was performed to examine the relationship between the fractional vegetation coverage (FVC) and the LST within one growing season (from May to August 2017), at different spatial scales. The results showed that the FVC increased from 0.12 in May to 0.29 in August, and the LST increased first and then declined. The highest LST was 41.68 °C in July, while the lowest was 28.62 °C in August. At the grid scale, the LST increased first and then declined with the increase of the FVC on 25 May, 10 June, and 29 August; the FVC ranged from 0.29–0.38, 0.27–0.32, and 0.29–0.38 with the preference of the ‘turning point’, respectively. A negative correlation was identified between the FVC and the LST and without any ‘turning point’ in the fitting curve on 28 July. The correlation between FVC and LST complied with the grid scale at the sample area scale. The coupling analysis of landscape pattern expressed by FVC and LST showed that, the landscape evenness, Euclidean nearest neighbor distance, and landscape splitting degree all showed strong coupling correlation in any study period (P). The landscape aggregation of FVC and LST showed a good coupling at the relatively high and low air temperature conditions of P1 and P3. Landscape contagion showed a good coupling between FVC and LST at relatively moderate air temperature condition of P1 and P4. Air temperature conditions and characteristics of vegetation coverage should be considered for a more targeted analysis when analyzing the relationship between FVC and LST and attention should be paid to the timing and type of study area in practical application.

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Remote sensing ecological index (RSEI) has the advantages of rapid, repeatable and relatively accurate in regional eco-environment quality assessment. Due to the lack of consideration of the interaction of adjacent analysis units in RSEI calculation, there is a few uncertainties in the assessment results. Based on RSEI, the landscape diversity index (LDI) was introduced, which considered the heterogeneity caused by the difference between the assessment unit and the adjacent one, and rebuilt modified remote sensing ecological index (MRSEI) to evaluate the eco-environment quality in the artificial oasis of Ningxia section of Yellow River. The results showed that the area of Fair and Poor grades in the low MRSEI year (2000) was greater than that of other grades, and the area of Moderate and Fair grades was greater than that of other grades in the high MRSEI year (2020). The conversion characteristics of different grades were Poor and Fair grades to adjacent high grades. During the study period, the eco-environment quality of the study area was improved, and the composition and pattern of land use types had a significant impact on MRSEI. Introduction of LDI-improved MRSEI can not only include the heterogeneous effect between the analysis unit and the adjacent one, but also consider the spatial scale effect of LDI to make the evaluation results more credible. However, some evaluation factors of RSEI and MRSEI (e.g., LDI, NDVI, and NDBSI) represent the accumulation of surface status over long-time scales, while others (e.g., Wet and LST) reflects only short-time scale features of the land surface. Therefore, how to eliminate the uncertainty caused by temporal scale mismatch is a challenge for RSEI and MRSEI applications.

The response of vegetation phenology to global climate change is one of the main forms in terrestrial ecosystem change, the study of vegetation phenology is an important complement to the understanding of how global climate change affects ecosystems in multiple dimensions. We selected the distribution area of Larix gmelinii in The Greater Khingan Mountains as a case area by eliminating the heterogeneity of vegetation types, with the support of Google Earth Engine platform, we studied the effects of different aspects and land surface temperature (LST) on remote sensing phenology (RSP) that is defined as start of growing season (SOS), end of growing season (EOS) and length of growing season (LOS) respectively in the study area through Normalized Difference Vegetation Index (NDVI) changes. The results showed that SOS advanced in different aspects during the study period, and the advance amplitude of SOS on the east and west aspect was greater than that on the south and north. Except for the east aspect, EOS showed a slight postponed, and LOS was prolonged on all aspects. The latitude difference between 51° and 53° N had no significant effect on L. gmelinii in different aspects. LST had an obviously direct effect on the RSP of L. gmelinii in different aspects, and the effect of LST on SOS and LOS was significantly greater than that on EOS. The effect of LST on SOS and LOS was significant in April and spring. The main contributor to the increase of LOS was the advance of SOS, while the postponed of EOS has a relatively small contribution to LOS. Due to the redistribution of meteorological factor by aspect, the spatial and temporal heterogeneity of RSP tends to be complex, so determining the same aspect is one of the main ways to reduce the phenological heterogeneity in the study of vegetation RSP.