No abstract

Cistanche deserticola Y.C. Ma has long been used for medical purposes in China. It mainly grows in the Chinese provinces of Inner Mongolia, Ningxia, Gansu, and Xinjiang, and the species in the Alxa region of northwest China, have the most distinct qualities. To explain the geoherbalism quality and geographical distribution of C. deserticola, we sampled 65 wild plants in Alxa, determined their echinacoside and acteoside content, and assessed the relationship between the ecological environment and quality of C. deserticola through maximum entropy modeling and geographic information system. We identified the areas suitable for the growth of high-quality C. deserticola species. The regionalization analysis of growth suitability showed that the most influential ecological factors for the growth of C. deserticola are soil type, annual sunshine duration, altitude, temperature seasonality (standard deviation ×100), vegetation type, sunshine duration in the growing season, mean precipitation in August and mean temperature in July. The most suitable areas for growing C. deserticola are southeast of Ejin Banner, central Alxa Right Banner, and north of Alxa Left Banner. The regionalization analysis of quality suitability showeds that the most influential ecological factors for glycosides in C. deserticola are sunshine duration in June, average precipitation in May, and average temperature in March, and the best-quality C. deserticola grows in Dalaihob Town, Ejin Banner. Upon inspection, the result of the experiment reached a high accuracy of 0.994, which indicates that these results are consistent with the actual distribution of C. deserticola in Alxa. The results of this study may serve as a scientific basis for site selection of artificial planting bases for C. deserticola.

Many application fields need land surface temperature (LST) with simultaneous high spatial and temporal resolution, which can be achieved through the disaggregation technique. Most published methods built an assumed scale-independent relationship between LST and predictor variables derived from coarse spatial resolution data. However, LST disaggregation in the heterogeneous areas, especially urban areas, is very difficult to achieve and there are few studies on it. In this article, we propose an adjusted stratified stepwise regression method for temperature disaggregation in urban areas. Landsat Enhanced Thematic Plus (ETM+) data from Shanghai, China, were used to construct remote-sensing indices that are related to LST variance and retrieve LST at 60 and 480 m spatial resolution, respectively. Different stepwise regression models at 480 m resolution were built for two stratified regions according to normalized difference vegetation index (NDVI) distribution, and then each independent variable at 60 m resolution was adjusted to calculate disaggregated LST by considering its relationship with the 480 m resolution image. By using LST retrieved directly from ETM+ band 6 at 60 m spatial resolution as the reference, the proposed method comprising resampling disaggregation, the thermal data sharpening model (TsHARP)/disaggregation procedure for radiometric surface temperature (DisTrad) technique, and the LST-principal component analysis (PCA) regression algorithm were verified and compared. The results show that the temperature distribution estimated using the proposed method is most consistent with that of the reference LST in this heterogeneous study area, and that the precision improves significantly, especially for the low vegetation fraction region.

Land surface temperature (LST) is an important environmental parameter in climate change, urban heat islands, drought, public health, and other fields. Thermal infrared (TIR) remote sensing is the main method used to obtain LST information over large spatial scales. However, cloud cover results in many data gaps in remotely sensed LST datasets, greatly limiting their practical applications. Many studies have sought to fill these data gaps and reconstruct cloud-free LST datasets over the last few decades. This paper reviews the progress of LST reconstruction research. A bibliometric analysis is conducted to provide a brief overview of the papers published in this field. The existing reconstruction algorithms can be grouped into five categories: spatial gap-filling methods, temporal gap-filling methods, spatiotemporal gap-filling methods, multi-source fusion-based gap-filling methods, and surface energy balance-based gap-filling methods. The principles, advantages, and limitations of these methods are described and discussed. The applications of these methods are also outlined. In addition, the validation of filled LST values’ cloudy pixels is an important concern in LST reconstruction. The different validation methods applied for reconstructed LST datasets are also reviewed herein. Finally, prospects for future developments in LST reconstruction are provided.