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In the process of mineral deposits formation, elements often show abnormal enrichment. Therefore, the inversion of element contents (the prediction of element contents in mineral deposits through hyperspectral data) has some indicative significance to the exploration of mineral resources. In order to solve the problem of nonlinear copper contents inversion using hyperspectral remote sensing, this paper put forward an improved Gradient Boosting Regression Tree (IGBRT) method to invert copper contents. The main innovations of this paper are: (1) replacing the simple average function with the k-nearest-neighbour weighted average function as the node prediction function to improve the accuracy; (2) using the adaptive reduction step instead of the fixed reduction step to improve the efficiency. At the end of the paper, taking the Altun region, Xinjiang province, China as the experimental area, the iron element which was high correlated with the copper was chosen as the intermediate variable, and the contents relationship between them was established to solve the problem of the small number of copper samples in the study area. Using the improved Gradient Boosting Regression Tree method to predict the iron contents, then the copper contents could be predicted through the relationship between them. The results showed that the IGBRT algorithm had the coefficient of determination (R2) of 0.744, which were 0.185 more than the traditional one, and the learning efficiency was increased by 39.4%. The results of this study can provide a reference for remote sensing inversion of copper contents in mineral deposits of uninhabited areas, and have some important significance for the delineation of copper prospects. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between spectral indices (the normalized difference vegetation index [NDVI] and photochemical reflectance index [PRI]) and the photosynthetic capacities based on chlorophyll fluorescence (Fv/Fm ) of moss-, lichen- and cyanobacteria-dominated biological soil crusts collected from the Gurbantonggut Desert, Xinjiang, China, based on a liquid-water hydration experiment. While no photosynthetic activity was detected for dry crusts, hydrated crusts showed significantly higher Fv/Fm values than dry crusts. A significant correlation between the PRI and Fv/Fm was found in moss- and lichen-dominated crusts, and the determination coefficients were higher than those between the NDVI and Fv/Fm . [ABSTRACT FROM AUTHOR]

Crop classification is a crucial task in agricultural remote sensing, with the accuracy of such classification heavily relies on field sampling. Reusing historical samples can minimize the reliance on annual field sampling for crop classification. Although previous research primarily focused on the classification accuracy based on the reused historical samples, the underlying factors that influence the accuracy have not been adequately investigated. In this study, we employed a two-dimensional convolutional neural network (2D-CNN) model for crop classification reusing historical samples and investigated the factors influencing the classification accuracy. First, we calculated a normalized difference vegetation index (NDVI) time series from historical data to characterize crop growth patterns. Secondly, we assessed three different time-series construction methods. Subsequently, we used the 2D-CNN model to automatically extract abstract features of crop growth patterns. Finally, we designed various strategies for reusing historical samples to explore the influencing factors. Experiments were conducted in Kuitun, Xinjiang Uygur Autonomous Region, China, from 2016 to 2020, employing a long time series of Sentinel-2 images as remote sensing data. Our results indicated that optimal 2D-CNN models using an irregular satellite image time series (irSITS) outperformed random forest models in inter-annual classifications (the overall accuracies for 2016–2020 were 0.78, 0.61, 0.89, 0.89, and 0.70, respectively). In addition, we identified that the primary factors affecting classification accuracy were 1) the time-series construction method used; 2) the crop growth patterns; and 3) the sample diversity. By reusing historical samples and considering the factors influencing classification accuracy, this study provides valuable insights into high-quality crop classification mapping under limited field sample conditions. [ABSTRACT FROM AUTHOR]

Ebinur Lake is located in a typical arid region in the north-west of China. It is an area with the lowest elevation in the Junggar Basin in the Province of Xinjiang. Recent monitoring indicates that the lake surface area has increased. To obtain a continuous record of the change in lake area, a radiometric analysis of SPOT/VEGETATION (VGT) imagery was carried out based on methodology developed for regional lake area mapping. Two indices, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), were selected to identify the water body of Ebinur Lake. The indices are calculated based on the spectral reflectances in the red and near infrared bands of VGT sensor. If the NDVI is less than a critical value (0) and if the NDWI is larger than a critical value (0), the pixel is flagged as a water body. Validation indicates that the methodology to identify water bodies based on multi-spectral VGT data is applicable in our study area achieving an overall accuracy of 91.4%. Independent monitoring results elicit that the lake surface area was at its lowest in 1998. The yearly average surface area is about 503 km2. The lake area increased to 603 km2 during 1999. In the period 1999-2001 the area changes are marginal. A large area increase occurred from 2001 to 2002 till the lake area reached a surface area of 791 km2. The lake area peaks to 903 km2 in 2003 and subsequently decreased to areas of 847 km2 in 2004 and 746 km2 in 2005. Similar area change dynamics are observed when applying the remote sensing based technique. Seasonally, the typical dynamics elicit a larger surface area in spring and winter and a smaller one during summer. [ABSTRACT FROM AUTHOR]