This paper developed a practical split-window (SW) algorithm to estimate land surface temperature (LST) from Thermal Infrared Sensor (TIRS) aboard Landsat 8. The coefficients of the SW algorithm were determined based on atmospheric water vapor sub-ranges, which were obtained through a modified split-window covariance–variance ratio method. The channel emissivities were acquired from newly released global land cover products at 30 m and from a fraction of the vegetation cover calculated from visible and near-infrared images aboard Landsat 8. Simulation results showed that the new algorithm can obtain LST with an accuracy of better than 1.0 K. The model consistency to the noise of the brightness temperature, emissivity and water vapor was conducted, which indicated the robustness of the new algorithm in LST retrieval. Furthermore, based on comparisons, the new algorithm performed better than the existing algorithms in retrieving LST from TIRS data. Finally, the SW algorithm was proven to be reliable through application in different regions. To further confirm the credibility of the SW algorithm, the LST will be validated in the future.

In this article, a new index, the visible and shortwave infrared drought index (VSDI), is proposed for monitoring both soil and vegetation moisture using optical spectral bands. VSDI is defined as , where ρ represents the reflectance of shortwave infrared (SWIR) red and blue channels, respectively. VSDI is theoretically based on the difference between moisture-sensitive bands (SWIR and red) and moisture reference band (blue), and is expected to be efficient for agricultural drought monitoring over different land-cover types during the plant-growing season. The fractional water index (FWI) derived from 49 Mesonet stations over nine climate divisions (CDs) across Oklahoma are used as ground truth data and VSDI is compared with three other drought indices. The results show that VSDI generally presents the highest correlation with FWI among the four indices, either for whole sites or for individual CDs. The NDVI threshold method is applied to demonstrate the satisfactory performance of VSDI over different land-cover types. A time-lag analysis is also conducted and suggests that VSDI can be used as a real-time drought indicator with a time lag of less than 8 days. The VSDI drought maps are produced and compared with the US Drought Monitor (USDM) maps. A good agreement has been observed between the two products, and finer spatial information is also found in VSDI. In conclusion, VSDI appears to be a real-time drought indicator that is applicable over different land-cover types and is suitable for drought monitoring through the plant-growing season.

Atmospheric water vapor (wv) is required for the accurate retrieval of the land surface temperature from remote sensing data and other applications. This work aims to estimate wv from Landsat 8 Thermal InfraRed Sensor (TIRS) images using a new modified split‐window covariance‐variance ratio (MSWCVR) method on the basis of the brightness temperatures of two thermal infrared bands. Results show that the MSWCVR method can theoretically retrieve wv with an accuracy better than 0.3 g/cm² for dry atmosphere (wv <2 g/cm²) conditions and better than 0.5 g/cm² for wet atmosphere conditions. The method was applied at different locations with dry and moist atmospheres and was validated at 42 ground sites using AERONET (Aerosol Robotic Network) ground‐measured data and MODIS (Moderate Resolution Imaging Spectroradiometer) products. The results show that the retrieved wv from the TIRS data is highly correlated with the wv of AERONET and MODIS but is generally larger. This difference was probably attributed to the uncertainty of radiometric calibration and stray light coming outside from field of view of TIRS instrument in the current images. Consequently, the data quality and radiometric calibration of the TIRS data should be improved in the future.

Building extraction from high-resolution remote sensing images has widely been studied for its great significance in obtaining geographic information. Many methods based on deep learning have been tried for the task; however, there is still much to explore about designing layers or modules for remote sensing data and taking full use of the unique features of buildings like shape and boundary. In this letter, an end-to-end network architecture based on U-Net is proposed. The U-Net architecture is modified with Xception module for remote sensing images to extract effective features. Also, multitask learning is adopted to incorporate the structure information of buildings. Two standard data sets (Massachusetts building data set and Vaihingen Data set) of high-resolution remote sensing images are selected to test our model and it achieves state-of-the-art results.

In this paper, drought status of northwestern China is evaluated using the Terra–Moderate Resolution Imaging Spectroradiometer (MODIS) data with a newly developed method called perpendicular drought index (PDI), which is defined as a line segment that is parallel with the soil line and perpendicular to the normal line of soil line intersecting the coordinate origin in the two‐dimensional scatter plot of red against near infrared (NIR) wavelength reflectance. To validate the PDI in macroscale applications, quantitative evaluation of drought conditions in Ningxia, Northwestern China is carried out by comparing the PDI with one of the well‐known drought indexes, namely, temperature‐vegetation index (TVX). Linear regression between ground‐measured soil moisture data and the PDI and the TVX was made. Results show that satellite based PDI and TVX has significant correlation with 0–20 cm averaged soil moisture obtained over the meteorological observing stations across the whole study area. The highest correlation of R ² = 0.48 for the PDI and R ² = 0.40 for the TVX is obtained when compared with average soil moisture from 0 to 20 cm soil depth. According to the drought critical values defined by soil hydrologic parameters including soil moisture, wilting coefficient and field moisture capacity, the PDI based drought guidelines are established, and then the drought status in the study area is evaluated using the PDI. It is evident from the results showing the spatial distribution of drought in northwestern China that the PDI is highly accordant with field drought status.

This study proposed a new narrow band index to characterize the Cu (Copper) stress degree on vegetation (Copper Stress Vegetation Index, CSVI). Firstly, the spectral reflectance and biochemical data of wheat, pea, locust and ash were analysed using Pearson correlation coefficient (r) to select wavelengths sensitive to Cu stress. The calculated Pearson correlation coefficients suggested that the reflectance near 550 nm and 700 nm correlated positively with Cu contents in leaves and solutions, and negative correlation was present in the range of 800–900 nm. Secondly, the selected wavelengths of 550 nm, 700 nm, and 850 nm were used to establish CSVI, and it was compared with existing popular vegetation indices (VIs) related to heavy metal stress (Normalized Difference Vegetation Index (NDVI), Red-Edge Position (REP), Difference Vegetation Index (DVI), Photochemical Reflectance Index (PRI)) by calculating Pearson correlation coefficient between VIs and Cu contents in leaves and solutions. Thirdly, verifications of CSVI on other vegetations were conducted, and the performance of CSVI was also compared with that of NDVI, REP, DVI, and PRI. The results suggested that CSVI showed significant correlation with Cu stress degree, and the correlation of CSVI was much stronger than that of other VIs for all the tested vegetations. The proposed CSVI characterizes the Cu stress degree on vegetation with advantages of better effectiveness, straightforward calculation, and robustness for different vegetations. This study focused on the spectral reflectance at the leaf scale, so it is expected that future work extends it to canopy scale and mixed-pixel scale.