The aboveground biomass (AGB) of a forest is an important indicator of the forest’s terrestrial carbon storage and its relation to climate change. Due to the advantage of extensive spatial coverage and low cost, coarse-resolution remote sensing data is the main data source for wall-to-wall mapping of forest AGB at the regional scale. Despite this, improving the accuracy and efficiency of forest AGB estimation is a major challenge. In this study, two optical imageries, Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m imagery and Fengyun-3C Visible and Infrared Radiometer (FY-3C VIRR) 1000 m imagery, were used and compared for forest AGB estimation in Yunnan Province, southwest China. One parametric approach, multiple linear regression (MLR), and two nonparametric approaches, k-nearest neighbor (KNN) and random forest (RF), were applied for the two imagery datasets, respectively. We evaluated the performance of the combination of remote sensing data and modeling approaches by comparing the accuracies and also explored the potential of FY-3C imagery data in forest AGB estimation at the regional scale as it was used for this purpose for the first time. We found that the machine learning models KNN and RF provided better results than MLR. From the three approaches for both MODIS and FY-3C imagery, RF performed best with R² values of 0.84 and 0.81 and RMSE of 23.18 and 23.43, respectively. Estimation of forest AGB based on MODIS was marginally better than the estimation based on FY-3C. FY-3C imagery could therefore be an additional optical remote sensing data source of coarse spatial resolution, comparable to MODIS data which has been widely used for regional forest AGB estimation. Indices related to forest canopy moisture levels from both types of imagery were sensitive to forest AGB. The RF model and MODIS imagery were then applied to map the spatial variation of forest AGB of Yunnan Province. As a result of our study, we determined that Yunnan Province has a total forest AGB of 2123.22 Mt, with a mean value of 58.05 t/ha for forestland in 2016.

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.

Irrigation is one of the key agricultural management practices of crop cultivation in the world. Irrigation practice is traceable on satellite images. Most irrigated area mapping methods were developed based on time series of NDVI or backscatter coefficient within the growing season. However, it has been found that winter irrigation out of growing season is also dominating in north China. This kind of irrigation aims to increase the soil moisture for coping with spring drought and reduce the wind erosion in spring. This study developed a remote sensing-based classification approach to identify irrigated fields out of growing season with Radom Forest algorithm. Four spectral bands and all Normalized Difference Vegetation Index (NDVI) like indices computed from any two of these four bands for each of the seven scenes of GF-1 satellite data were used as the input features in the building of separated RF models and in applying the built models for the classification. The results showed that the mean of the highest out-of-bag accuracies for seven RF models was 94.9% and the mean of the averaged out-of-bag accuracies in the plateau for seven RF models was 94.1%; the overall accuracy for all seven classified outputs was in the range of 86.8–92.5%, Kappa in the range of 84.0–91.0% and F1-Score in the range of 82.1–90.1%. These results showed that the classification was neither overperformed nor underperformed as the accuracies of all classified images were lower than the model ones. This study also found that irrigation started to be applied as early as in November and irrigated fields were increased and suspended in December and January due to freezing conditions. The newly irrigated fields were found again in March and April when the temperature rose above zero degrees. The area of irrigated fields in the study area were increasing over time with sizes of 98.6, 166.9, 208.0, 292.8, 538.0, 623.1, 653.8 km² from December to April, accounting for 6.1%, 10.4%, 12.9%, 18.2%, 33.4%, 38.7%, and 40.6% of the total irrigatable land in the study area, respectively. The results showed that the method developed in this study performed well. This study found on the satellite images that 40.6% of irrigatable fields were already irrigated before the sowing season and the irrigation authorities were supposed to improve their water supply capacity in the whole year with this information. This study may complement the traditional consideration of retrieving irrigation maps only in growing season with remote sensing images for a large area.

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.

Irrigation is one of key agricultural management practices of crop cultivation in the world. Irrigation practice is traceable on satellite image. Most irrigated area mapping methods were developed based on time series of NDVI or back scatter coefficient within the growing season. However, it found the winter irrigation out of growing season is also dominating in north China. This kind of irrigation aims to increase the soil moisture for coping with spring drought, and also reduce the wind erosion with the wet soil in the field surface as the strong wind happens in spring. This study developed a remote sensing-based classification approach to identify irrigated fields with Radom Forest algorithm out of growing season. The results showed that the mean of the highest accuracies of 7 RF models was 94.9% and the mean of the averaged accuracies of 7 RF models was 94.1%; the overall accuracy for all 7 outputs was in the range of 86.8-92.5%, Kappa in the range of 84.0-91.0% and F-1 score in the range of 82.1-90.1%. These results showed that the classification was acceptable and not over performed as the accuracies of all classified images were lower than the models. This study also found that irrigation started to apply in early November and irrigated fields were increased and suspended in December and January due to freeze. The newly irrigated fields were found again in March and April when the temperature goes up above zero degree. The area of irrigated fields in the study area were increasing over time with sizes of 98.6, 166.9, 208.0, 292.8, 538.0, 623.1, 653.8 km2 from December to April, accounting for 6.1%, 10.4%, 12.9%, 18.2%, 33.4%, 38.7%, and 40.6% of the total irrigatable land in the study area, respectively.