Soil salinization is a serious environmental issue that significantly influences crop yield and soil fertility, especially in coastal areas. Numerous studies have been conducted on the salinity status in Pakistan. Information about the geospatial and temporal distribution of salinity in the Sujawal district is still lacking. The present study examines the soil salinity status and the impact of seawater intrusion in the entire district from 1990 to 2017 using field and remote sensing (RS) data. In addition, 210 soil samples at different depths (0–20, 20–40, and 40–60 cm) were collected from randomly selected locations for lab measurements of physiochemical properties. The results showed that the soil texture classes were mainly fine to medium particles. The samples collected at the 0–20 cm depth were mostly dominated by three textural classes of soil: clay at 19.5%, clay loam at 25.6%, and loam at 32.9%. The electrical conductivity (EC) of 65.7% soil samples collected from the top layer exceeded the normal range. The quantitative results indicated that the exchangeable sodium percentage (ESP) ranged between 1.38 and 64.58, and 72.2% of the top layer soil samples had ESP >15, while 81.5% of soil samples were in the normal range of soil pH. Furthermore, the results indicated that the vegetation decreased by 8.6% from 1990 to 2017, while barren land and water bodies increased significantly, by approximately 4.4% and 4.2%, respectively. The extreme and high salinity classes were characterized by high contents of soluble salt on the surface in the Jati and Shah Bandar subdistricts. In addition, the soil EC values at the 0–20 cm depth were significantly correlated with the salinity index (S1). Therefore, it was concluded that more than 50% of the top layer of soil was affected by salinity due to seawater intrusion, low rainfall, climate change, and erratic river flow. It is suggested that remote sensing (RS) data are more suitable for the detection of the soil salinity status of a region and impose a lower cost compared to other conventional approaches. However, this study could provide significant knowledge to land managers, policymakers, and government officials to allow them to take action to implement salinity control measures in the study area.

The atmosphere has substantial effects on optical remote sensing imagery of the Earth’s surface from space. These effects come through the functioning of atmospheric particles on the radiometric transfer from the Earth’s surface through the atmosphere to the sensor in space. Precipitable water vapor (PWV), CO₂, ozone, and aerosol in the atmosphere are very important among the particles through their functioning. This study presented an algorithm to retrieve total PWV from the Chinese second-generation polar-orbiting meteorological satellite FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) data, which have three near-infrared (NIR) water vapor absorbing channels, i.e., channel 16, 17, and 18. The algorithm was improved from the radiance ratio technique initially developed for Moderate-Resolution Imaging Spectroradiometer (MODIS) data. MODTRAN 5 was used to simulate the process of radiant transfer from the ground surfaces to the sensor at various atmospheric conditions for estimation of the coefficients of ratio technique, which was achieved through statistical regression analysis between the simulated radiance and transmittance values for FY-3D MERSI-2 NIR channels. The algorithm was then constructed as a linear combination of the three-water vapor absorbing channels of FY-3D MERSI-2. Measurements from two ground-based reference datasets were used to validate the algorithm: the sun photometer measurements of Aerosol Robotic Network (AERONET) and the microwave radiometer measurements of Energy’s Atmospheric Radiation Measurement Program (ARMP). The validation results showed that the algorithm performs very well when compared with the ground-based reference datasets. The estimated PWV values come with root mean square error (RMSE) of 0.28 g/cm² for the ARMP and 0.26 g/cm² for the AERONET datasets, with bias of 0.072 g/cm² and 0.096 g/cm² for the two reference datasets, respectively. The accuracy of the proposed algorithm revealed a better consistency with ground-based reference datasets. Thus, the proposed algorithm could be used as an alternative to retrieve PWV from FY-3D MERSI-2 data for various remote sensing applications such as agricultural monitoring, climate change, hydrologic cycle, and so on at various regional and global scales.

Cloud-free remote sensing images are required for many applications, such as land cover classification, land surface temperature retrieval and agricultural-drought monitoring. Cloud cover in remote sensing images can be pervasive, dynamic and often unavoidable. Current techniques of cloud removal for the VNIR (visible and near-infrared) bands still encounters the problem of pixel values estimated for the cloudy area incomparable and inconsistent with the cloud-free region in the target image. In this paper, we proposed an efficient approach to remove thick clouds and their shadows in VNIR bands using multi-temporal images with good maintenance of DN (digital number) value consistency. We constructed the spectral similarity between the target image and reference one for DN value estimation of the cloudy pixels. The information reconstruction was done with 10 neighboring cloud-free pair-pixels with the highest similarity over a small window centering the cloudy pixel between target and reference images. Four Landsat5 TM images around Nanjing city of Jiangsu Province in Eastern China were used to validate the approach over four representative surface patterns (mountain, plain, water and city) for diverse sizes of cloud cover. Comparison with the conventional approaches indicates high accuracy of the approach in cloud removal for the VNIR bands. The approach was applied to the Landsat8 OLI (Operational Land Imager) image on 29 April 2016 in Nanjing area using two reference images. Very good consistency was achieved in the resulted images, which confirms that the proposed approach could be served as an alternative for cloud removal in the VNIR bands using multi-temporal images.

Land surface temperature (LST) is an essential parameter widely used in environmental studies. The Medium Resolution Spectral Imager II (MERSI-II) boarded on the second generation Chinese polar-orbiting meteorological satellite, Fengyun-3D (FY-3D), provides a new opportunity for LST retrieval at a spatial resolution of 250 m that is higher than that of the already widely used Moderate Resolution Imaging Spectrometer (MODIS) LST data of 1000 m. However, there is no operational LST product from FY-3D MERSI-II data available for free access. Therefore, in this study, we developed an improved two-factor split-window algorithm (TFSWA) of LST retrieval from this data source as it has two thermal-infrared (TIR) bands. The essential coefficients of the TFSWA algorithm have been carefully and precisely estimated for the FY-3D MERSI-II TIR thermal bands. A new approach for estimating land surface emissivity has been developed using the ASTER Global Emissivity Database (ASTER GED) and the International Geosphere-Biosphere Program (IGBP) data. A model to estimate the atmospheric water vapor content (AWVC) from the three atmospheric water vapor absorption bands (bands 16, 17, and 18) has been developed as AWVC has been recognized as the most important factor determining the variation of AT. Using MODTRAN 5.2, the equations for the AT estimate from the retrieved AWVC were established. In addition, the AT of the pixels at the far edge of FY-3D MERSI-II data may be strongly affected by the increase of the optical path. Viewing zenith angle (VZA) correction equations were proposed in the study to correct this effect on AT estimation. Field data from four stations were applied to validate the improved TFSWA in the study. Cross-validation with MODIS LST (MYD11) was also conducted to evaluate the improved TFSWA. The cross-validation result indicates that the FY-3D MERSI-II LST from the improved TFSWA are comparable with MODIS LST while the correlation coefficients between FY-3D MERSI-II LST and MODIS LST over the Mid-East China region are in the range of 0.84~0.98 for different seasons and land cover types. Validation with 318 field LST samples indicates that the average MAE and R² of the scenes at the four stations are about 1.97 K and 0.98, respectively. Thus, it could be concluded that the improved TFSWA developed in the study can be a good algorithm for LST retrieval from FY-3D MERSI-II data with acceptable accuracy.

The concept of land surface temperature (LST) encompasses both surface energy balance and land surface activities. The study of climate change greatly benefits from an understanding of the geographical and temporal fluctuations of LST. In this study, we utilized an improved version of the TFSW algorithm to retrieve the LST from the Medium resolution spectral imager II (MERSI-II) data for the first time in Pakistan. MERSI-II is a payload for the Chinese meteorological satellite Fengyun 3D (FY-3D), and it has the capability for use in various remote sensing applications such as climate change and drought monitoring, with higher spatial and temporal resolutions. Once the LSTs were retrieved, accuracy of the LSTs were investigated. Later, LST datasets were used to detect the spatiotemporal variations of LST in Pakistan. Monthly, seasonal, and annual datasets were utilized to detect increasing and decreasing LST trends in the regions, with Mann–Kendall and Sen’s slope estimator tool. In addition, we further revealed the long-term spatiotemporal variations of LST by utilizing Moderate Resolution Imaging Spectrometer (MODIS) LST observations. The cross-validation analysis shows that the retrieved LST of MERSI-II was more consistent with the MODIS MYD11A1 LST product compared to the MYD21A1. The spatial distribution of LSTs demonstrates that the mean LST exhibits a pattern of spatial variability, with high values in the southern areas and low values in the northern areas; there are areas that do not follow this trend, possibly due to reasons of elevation and types of land cover also influencing the LST’s spatial distribution. The annual mean LST trend increases in the northern regions and decreases in the southern regions, ranging between −0.013 and 0.019 °C/year. The trend of long-term analysis were also consistent with MERSI-II, excepting region II, with increasing effects. This study will be helpful for various environmental and climate change studies.

An experiment was conducted to assess the yield potential and Water Use Efficiency (WUE) of Canola crop grown under sprinkler and basin irrigation techniques on a clay loam soil. The experiment was designed by the Principles of Randomized Complete Block Design (RCBD) with two treatments, viz. T1 (sprinkler irrigation method) and T2 (basin irrigation method), replicated four times. Marginal quality water with Electrical Conductivity (ECw) of 1.83 dS/m and pH of 8.2 was used for irrigation. The crop yield obtained under T1 and T2 treatments were 1,407.9 and 1,123.8 kg ha-1, respectively; 20% more yield was observed under T1 treatment. In the given order of treatments (T1 and T2), the Canola crop used 3,605 and 4,453 m3 ha-1 of irrigation water, hence, 19% water saving was achieved by T1 treatment. The WUE attained under T1 and T2 treatments were 0.39 and 0.25 kg m-3 respectively; therefore, 35.8% WUE was enhanced under T1 treatment. While comparing the agronomic parameters of the crop under the two procedures, all the observed parameters (plant height, number of branches plant-1, number of pods plant-1, pod length, number of seeds pod-1 and biomass plant-1) were superior in case of T1 treatment than T2 treatment. The soil Electrical Conductivity (ECse), Sodium Absorption Ratio (SAR), and Exchangeable Sodium Percentage (ESP) increased, and pH decreased under both the treatments, however, the maximum increase in ECse, SAR and ESP, and maximum decrease in pH occurred under T2 treatment. Statistical analysis showed that the crop yield, irrigation water use, WUE, ECse, pH, SAR, ESP, and all the agronomic parameters differed significantly (p < 0.05) under the two treatments.