Nitrogen (N) and phosphorus (P) are primary indicators of soil nutrients in agriculture. Accurate management of these nutrients is essential for ensuring food security. High-resolution, multi-spectral remote sensing images can provide crucial information for mapping soil nutrients at the field scale. This study compares the capabilities of ZH-1 and Sentinel-2 satellite data, along with different spectral indices, in mapping soil nutrients (total N and Olsen-P) using two machine learning algorithms, random forest (RF) and XGBoost (XGB). Two agricultural fields in Suihua City were selected as the study areas for this investigation. The results showed that Sentinel-2 data performed best in computing the total N content in soil using the RF model (R² = 0.74, RMSE = 0.10 g/kg). However, for the soil Olsen-P content, the XGBoost model performed better with ZH-1 data (R² = 0.75, RMSE = 9.79 mg/kg) than the RF model. This study demonstrates that both ZH-1 and Sentinel-2 satellite data perform well in terms of accurately mapping soil total N and Olsen-P contents using machine learning. Due to its higher spectral and spatial resolution, ZH-1 remote sensing data provides more detailed information on soil nutrient content during Olsen-P inversion and exhibits comparable accuracy.

Land surface temperature (LST) is a vital parameter associated with the land–atmosphere interface. The Moderate Resolution Imaging Spectroradiometer (MODIS) LST product can provide precise LST with high time resolution, and is widely applied in various remote sensing temperature research. However, due to its inability to penetrate the cloud and fog, its quality is not able to meet the requirements of actual research. Hence, obtaining continuous and cloudless MODIS LST datasets remains challenging for researchers. The critical point is to reconstruct missing pixels. To compare the performance of different methods, first, three kinds of methods were used to reconstruct the missing pixels, namely, temporal, spatial, and spatiotemporal methods. The predicted values using these methods were validated by the automatic weather system data (AWS) in the Heihe river basin of China. The results demonstrated that, compared with other methods, linear temporal interpolation using Aqua data had the best performance in MODIS LST reconstruction in the Heihe river basin, with an RMSE of 7.13 K and an R² of 0.82, and the NSE and PBias were 0.78 and −0.76%, respectively. Furthermore, the interpolation method was improved using adaptive windows and robust regression. First, the international Geosphere–Biosphere Program (IGBP) classification was employed to distinguish the different land surface types. Then, the invalid LST values were reconstructed using adjacent days’ effective LST values combined with a robust regression. Finally, a mean filter was applied to eliminate outliers. The overall results combined with ERA5 data were validated by AWS, with an RMSE of 6.96 K and an R² of 0.79 and the NSE and PBias were 0.77 and −0.20%, respectively. The validation demonstrated that the scheme proposed in this paper is able to accurately reconstruct the missing values and improve the accuracy of the interpolation method to a certain extent when reconstructing MODIS LST.

It is of great convenience to map daily evapotranspiration (ET) by remote sensing for agricultural water management without computing each surface energy component. This study used the operational simplified surface energy balance (SSEBop) and the remote sensing-based Penman–Monteith and Priestly–Taylor (RSPMPT) models to compute continuous daily ET over irrigated fields with the MODIS and CMADS data. The estimations were validated with eddy covariance (EC) measurements. Overall, the performance of RSPMPT with locally calibrated parameters was slightly better than that of SSEBop, with higher NSE (0.84 vs. 0.78) and R² (0.86 vs. 0.81), lower RMSE (0.78 mm·d⁻¹ vs. 0.90 mm·d⁻¹), although it had higher bias (0.03 mm·d⁻¹ vs. 0.01 mm·d⁻¹) and PBias (1.41% vs. 0.59%). Due to the consideration of land surface temperature, the SSEBop was more sensitive to ET’s change caused by irrigation before sowing in March and had a lower PBias (6.7% vs. 39.8%) than RSPMPT. On cloudy days, the SSEBop is more likely to overestimate ET than the RSPMPT. To conclude, driven by MODIS and CMADS data, the two simple models can be easily applied to map daily ET over cropland. The SSEBop is more practical in the absence of measured data to optimize the RSPMPT model parameters.