This paper studied the applicability of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data for lithological classification in the shallowly covered Eastern Yanshan Mountain of Eastern China. The interpreted objects include quartz sandstone, carbonate rock, gneiss, and andesite. An integrated approach was employed to process the remote sensing data: First, the matched filtering method, accompanied by the ASTER library and imagery spectra as reference, was used to enhance the targeted information, namely the remotely visible (typical) rock outcrops. As the andesite is frequently weathered to iron-rich minerals in the field, so the high-resolution SPOT6 3/1 image, instead of the ASTER image, was used to locate the outcrops. Second, the fractal digital number-frequency algorithm was developed to preliminarily extract the lithology anomaly patches; and the obtained geological anomalies were generalized into three types: anomalies related to outcrops, anomalies caused by different forms of the same lithology, and random noise. Third, digital elevation model derived slope masks, in combination with the spatial intersection operation, was used to eliminate the pseudo-outcrop anomalies and noise. Finally, the accuracy assessment was conducted by referencing the local rock-outcrop database, and the misclassification rates for quartz sandstone, carbonate rock, gneiss, and andesite are 8.9%, 12.5%, 23%, and 48.3%, respectively. This study has contributed a useful case study for remote-sensing lithology mapping in shallowly covered areas, and the proposed method should have a great potential to be applied to many similar cases.

Solar-induced chlorophyll fluorescence (SIF) from ground, airborne, and satellite-based observations has been increasingly used in drought monitoring recently due to its close relationship with photosynthesis. SIF emissions respond rapidly to droughts, relative to the widely used vegetation indices (VIs), thus indicating their potential for early drought monitoring. The response of SIF to droughts can be attributed to the confounding effects of both the physiology and canopy structure. In order to reduce the reabsorption and scattering effects, the total emitted SIF (SIFtot) was proposed and served as a better tool to estimate GPP compared with the top-of-canopy SIF (SIFtoc). However, the response time and response magnitude of SIFtot to droughts and its relationships with the environmental parameters and soil moisture (SM) (i.e., the knowledge of drought monitoring using SIFtot) remains unclear. Here, the continuous ground data of F760toc (SIFtoc at 760 nm) from a nadir view that was downscaled to F760tot (SIFtot at 760 nm), NIR_v, and the NDVI, SM, meteorological, and crop growth parameters were measured from four winter wheat plots with different intensities of drought (well-watered, moderate drought, severe drought, and extreme drought) over 2 months. The results indicated that F760tot was more closely correlated with the SM than the VIs at short time lags but weaker at longer time lags. The daily mean values of F760tot and NIR_v were able to distinguish the differences between different drought levels, and F760tot responded quickly to the onset of drought, especially for the moderate drought intensity. These findings demonstrated that F760tot has potential for early drought monitoring and may contribute to mitigating the risk of agricultural drought.

Lithological mapping in weathered and vegetated coastal zone is always a crucial but challenging issue for geological remote sensing. This study tested the applicability of Sentinel-2A and ASTER GDEM data for lithological mapping in the coastal zone of Shanwei district, southern China. Here, a comprehensive scheme for tuff and granite outcrops extraction and lithological classification was employed to process the remote sensing data: 1) Using the matched filtering with image-derived spectra as references, the weathered but representative outcrop-related patches were preliminarily extracted based on the fractal “DN-A” (digital number-area) algorithm; 2) The image masks of NDXI (NDVI, NDSI and MNDWI) in conjunction with spatial intersection, were conducted to minimize the influence of vegetation coverage; 3) Considering terrain features caused by differential weathering, slope analysis have allowed us to discriminate from tuff to granite outcrops; 4) A set of empirical verification criteria were established to assess the classification accuracy, which are exactly 82.4~83.2% of tuff and 91.3~92.3% of granite. The peak values of tuff and granite patch areas are 0–0.1 km² (51%) and 0–0.5 km² (60%), and 90% of patches are < 0.6 km² of tuff and < 3.5 km² of granite, respectively. The protolith of extracted weathered outcrops were further identified by several excavated trenches. This research has contributed a useful case study for remote-sensing lithological mapping in weathered and vegetated coastal zone, and the proposed approach should have a great possibility to be extrapolated for other similar cases.

Digital Outcrop Models (DOMs) are becoming increasingly attractive tools in geoscientific fields. Light Detection and Ranging (LiDAR) is the most common used technology to create such 3D models recently, but not applicable for the fieldwork in current geological survey because of their high cost and weight. Fortunately, the advent of Structure-from-Motion (SfM) provides a low-cost but effective method for creating virtual 3-D outcrops. This study presents a routine and effective workflow of using SfM method to create a reliable and accurate DOM from field survey to model reconstruction based on Agisoft PhotoScan, and summarized the requirements and considerations of each step in detail, including preliminary preparations, ground control points and photo collection path in field surveys, as well as photos quality inspection, aligning photos, building dense point clouds and georeferencing model in SfM model creation. This systematic procedure can lay a solid foundation for subsequent geological information extraction.

Sun-induced chlorophyll fluorescence (SIF) has recently been used for the early detection of plant stress. The response of SIF to water stress is complicated by the combination of physiological and non-physiological dynamics in SIF variations and has not been well explored. This paper aims to explore the physiological and non-physiological responses of SIF under different levels of water stress, by analyzing the data from winter wheat with four irrigation treatments (well-watered, moderate, severe, and extreme water stresses). Here the near-infrared radiance of vegetation (NIRvR) and the canopy near-infrared reflectance of vegetation multiplied by incoming sunlight (NIRvP) were selected to represent the non-physiological information of SIF. Then the physiological information (fluorescence yield, ΦF) was extracted by dividing SIF by NIRvP or NIRvR. The results indicate that the physiological and non-physiological components of SIF were effective indicators for water stress detection. Compared to NDVI, the physiological and non-physiological components exhibited faster responses to different levels of water stress. The non-physiological component explored 68%–77% of SIF under different levels of water stress, and its contributions were firstly decreased and then increased as water stress increased. The stress-induced physiological decrease explored ∼50% of SIF decrease under severe water stress and exceeded the non-physiological decrease at the onset of extreme water stress. This study provides direct insights into the physiological and non-physiological dynamics of SIF under different levels of water stress, which contribute to improving the detection of plant stress using SIF.