Actual evapotranspiration (ET) with high spatiotemporal resolution is very important for the research on agricultural water resource management and the water cycle processes, and it is helpful to realize precision agriculture and smart agriculture, and provides critical references for agricultural layout planning. Due to the impact of the clouds, weather environment, and the orbital period of optical satellite, there are difficulties in providing daily remote sensing data that are not contaminated by clouds for estimating daily ET with high spatial-temporal resolution. By improving the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), this manuscript proposes the method to fuse high temporal and low spatial resolution Weather Research and Forecasting (WRF) model surface skin temperature (TSK) with the low temporal and high spatial resolution remote sensing surface temperature for obtaining high spatiotemporal resolution daily surface temperature to be used in the estimation of the high spatial resolution daily ET (ET_WRF_HR). The distinction of this study from the previous literatures can be summarized as the novel application of the fusion of WRF-simulated TSK and remote sensing surface temperature, giving full play to the availability of model surface skin temperature data at any time and region, making up for the shortcomings of the remote sensing data, and combining the high spatial resolution of remote sensing data to obtain ET with high spatial (Landsat-like scale) and temporal (daily) resolution. The ET_WRF_HR were cross-validated and quantitatively verified with MODIS ET products (MOD16) and observations (ET_Obs) from eddy covariance system. Results showed that ET_WRF_HR not only better reflects the difference and dynamic evolution process of ET for different land types but also better identifies the details of various fine geographical objects. It also represented a high correlation with the ET_Obs by the R² amount reaching 0.9186. Besides, the RMSE and BIAS between ET_WRF_HR and the ET_Obs are obtained as 0.77 mm/d and −0.08 mm/d respectively. High R², as well as the small RMSE and BIAS amounts, indicate that ET_WRF_HR has achieved a very good performance.

Using Surface Energy Balance System (SEBS) to estimate actual evapotranspiration (ET) on a regional scale generally uses gridded meteorological data by interpolating data from meteorological stations with mathematical interpolation. The heterogeneity of underlying surfaces cannot be effectively considered when interpolating meteorological station measurements to gridded data only by mathematical interpolation. This study aims to highlight the improvement of modeled meteorological data from the Weather Research and Forecasting (WRF) mesoscale numerical model which fully considers the heterogeneity of underlying surfaces over the data from mathematical interpolation method when providing accurate meteorological input for SEBS model. Meteorological data at 1 km resolution in the Hotan Oasis were simulated and then were put into SEBS model to estimate the daily actual ET. The accuracy of WRF simulation was evaluated through comparison with data collected at the meteorological station. Results found that the WRF-simulated wind speed, air temperature, relative humidity and surface pressure correlate well with the meteorological stations measurements (R² are 0.628, 0.8242, 0.8089 and 0.8915, respectively). Comparison between ET calculated using the meteorological data simulated from the WRF (ETa-WRF) and meteorological data interpolated from measurements at met stations (ETa-STA) showed that ETa-WRF could better reflect the ET difference between different land cover, and capture the vegetation growing trend, especially in areas with sparse vegetation, where ETa-STA intends to overestimate. In addition, ETa-WRF has less noise in barren areas compared to ETa-STA. Our findings suggest that WRF can provide more reliable meteorological input for SEBS model than mathematical interpolation method.

Surface temperature is one of the key parameters for estimating regional evapotranspiration (ET) based on the Surface Energy Balance System (SEBS) model using remote sensing data. However, continuous daily remote sensing surface temperature data are often not available due to the weather and environmental conditions. This paper proposed a scheme to obtain reliable ET that estimating ET using WRF-simulated surface skin temperature (TSK) and then modifying the deviation using the normalized difference vegetation index (NDVI). This study aims to explore whether the model data can be a viable option when the continuous-time-series remote sensing surface temperature is missing for estimating ET. Comparison results show that the correlation between WRF TSK and the measured temperature of the 2-cm soil (T_s) is better than MODIS land surface temperature (LST) in the study area, while the correlation between MODIS LST and the measured surface radiation temperature (IRT) is better than WRF TSK. The MODIS LST is significantly higher than T_s, and the WRF TSK is closer to T_s than MODIS LST. However, the ET calculated using WRF TSK was not good, exhibiting relatively high ET in the whole area and a poor correlation with the measurements, whereby R², RMSE, and the percent bias (PBIAS) were equal to 0.1256, 5.2783 mm, and −202.17%, respectively. According to the principle of land surface process simulation in WRF, this paper proposes using NDVI to modify ET calculated using TSK. The comparison between the modified ET and the measurements exhibited a relatively good correlation, with R² = 0.7532, RMSE = 1.0993 mm, and PBIAS = −17.9%. Therefore, the model surface temperature data can be used to estimate continuous-time-series regional ET when NDVI is used to modify the deviation, which indicates the surface temperature data simulated by the WRF Model can become the optional data for estimating ET and compensate for the shortcoming of poor time continuity of remote sensing data, further expanding the application prospects of meteorological model data in the remote sensing field.

Monitoring the spatiotemporal distribution of formaldehyde (HCHO) is crucial for reducing volatile organic compounds (VOCs) emissions, and the long-term evolution of socio-natural sources contributions to tropospheric HCHO over China is still unclear. We propose an oversampling algorithm for quantitatively tracking the evolution of uncertainty, which lowers the uncertainty of the original Level 2 OMI HCHO data (50 % -105 %) to 0-50 %, and then we examine the evolution of contributions from various emissions sources applying multi-scale correlation. We found that the high formaldehyde vertical column densities (VCD) caused by human activities in eastern China are crossing the Hu Huanyong Line, which was formerly used to demarcate the population distribution. National-scale analysis indicate that HCHO VCD are significantly correlated with per capita Gross Domestic Product (per capita GDP) (r = 0.948) and Normalized Difference Vegetation Index (r = 0.864), while no substantial correlation with land surface temperature (LST) (r = 0.233). A valuable finding at city-scale is that the vast majority of cities exhibits clear latitude zoning characteristics in the correlation between HCHO VCD and per capita GDP. Diagnosis at pixel scale reveals that anthropogenic emissions continue to weaken the contributions of emissions caused by the increase in vegetation proportion. NDVI = 0.8 is the critical characteristic point where the contribution of natural source exceeds that of anthropogenic sources, while the point presents a decreasing trend in recent years due to the enhancement of human activities levels. Rise in LST over vegetation areas show positive driving effect on formaldehyde emissions, but continuous urbanization is diminishing this contribution. NDVI = 0.8 is a characteristic point to determine whether the contribution proportion of regional surface temperature to formaldehyde emissions from vegetation begun to rise. Our research identifies the evolutionary process and characteristics of the spatiotemporal distribution and socio-nature sources contributions of tropospheric formaldehyde of China from 2005 to 2022.

Soil moisture content (SMC) plays an essential role in geoscience research. The SMC can be retrieved using an artificial neural network (ANN) based on remote sensing data. The quantity and quality of samples for ANN training and testing are two critical factors that affect the SMC retrieving results. This study focused on sample optimization in both quantity and quality. On the one hand, a sparse sample exploitation (SSE) method was developed to solve the problem of sample scarcity, resultant from cloud obstruction in optical images and the malfunction of in situ SMC-measuring instruments. With this method, data typically excluded in conventional approaches can be adequately employed. On the other hand, apart from the basic input parameters commonly discussed in previous studies, a couple of new parameters were optimized to improve the feature description. The Sentinel-1 SAR and Landsat-8 images were adopted to retrieve SMC in the study area in eastern Austria. By the SSE method, the number of available samples increased from 264 to 635 for ANN training and testing, and the retrieval accuracy could be markedly improved. Furthermore, the optimized parameters also improve the inversion effect, and the elevation was the most influential input parameter.

Accurate cropland classification is important for agricultural monitoring and related decision-making. The commonly used input spectral features for classification cannot be employed to effectively distinguish crops that have similar spectro-temporal features. This study attempted to improve the classification accuracy of crops using both the thermal feature, i.e., the land surface temperature (LST), and the spectral feature, i.e., the normalized difference vegetation index (NDVI), for classification. To amplify the temperature differences between the crops, a temperature index, namely, the modified land surface temperature index (mLSTI) was built using the LST. The mLSTI was calculated by subtracting the average LST of an image from the LST of each pixel. To study the adaptability of the proposed method to different areas, three study areas were selected. A comparison of the classification results obtained using the NDVI time series and NDVI + mLSTI time series showed that for long time series from June to November, the classification accuracy when using the mLSTI and NDVI time series was higher (85.6% for study area 1 in California, 96.3% for area 2 in Kansas, and 91.2% for area 3 in Texas) than that when using the NDVI time series alone (82.0% for area 1, 94.7% for area 2, and 90.9% for area 3); the same was true in most of the cases when using the shorter time series. With the addition of the mLSTI time series, the shorter time series achieved higher classification accuracy, which is beneficial for timely crop identification. The sorghum and soybean crops, which exhibit similar NDVI feature curves in this study, could be better distinguished by adding the mLSTI time series. The results demonstrated that the classification accuracy of crops can be improved by adding mLSTI long time series, particularly for distinguishing crops with similar NDVI characteristics in a given study area.