Soil salinization is one of the most common land desertification processes that can be found worldwide. It is a certainly severe environment hazard and threatens the stability of ecosystems. As a rapid and inexpensive tool, remote sensing technology combining with the measurements of soil spectra has been widely concerned on identifying and mapping salt effect on lands. However, as effects of the soil moisture often immerge the effects of salt to soil reflectance spectra, soil moisture became a major factor to restrict soil salinity monitoring from soil reflectance. High soil moisture content will lead to failure on soil salinity estimation from soil reflectance data. In this paper, a semianalytical model using an exponent function was developed to estimate soil salt content (SSC) under different moisture levels based on a control laboratory experiment. And the root-mean-square error and mean relative error were 0.799 g/kg and 31.294%, respectively, when the model was applied to estimate SSCs by wet soil reflectance. To sum up, considering both effects of soil moisture and soil salt on soil reflectance, the semianalytical model reduced SSC estimated error. The approach presented in this paper provides a new way of estimating soil salinity from soil spectra under various soil moisture conditions, and it will be a potential application for large-scale SSC mapping.

Forest biomass is critically important for forest dynamics in the carbon cycle. However, large-scale AGB mapping applications from remote sensing data still carry large uncertainty. In this study, an AGB estimation model was first established with three different remote sensing datasets of GF-2, Sentinel-2 and Landsat-8. Next, the optimal scale estimation result was considered as a reference AGB to obtain the relative true AGB distribution at different scales based on the law of conservation of mass, and the error of the scale effect of AGB estimation at various spatial resolutions was analyzed. Then, the information entropy of land use type was calculated to identify the heterogeneity of pixels. Finally, a scale conversion method for the entropy-weighted index was developed to correct the scale error of the estimated AGB results from coarse-resolution remote sensing images. The results showed that the random forest model had better prediction accuracy for GF-2 (4 m), Sentinel-2 (10 m) and Landsat-8 (30 m) AGB mapping. The determination coefficient between predicted and measured AGB was 0.5711, 0.4819 and 0.4321, respectively. Compared to uncorrected AGB, R² between scale-corrected results and relative true AGB increased from 0.6226 to 0.6725 for Sentinel-2, and increased from 0.5910 to 0.6704 for Landsat-8. The scale error was effectively corrected. This study can provide a reference for forest AGB estimation and scale error reduction for AGB production upscaling with consideration of the spatial heterogeneity of the forest surface.

Soil moisture plays a significant role in the global hydrological cycle, which is an important component of soil parameterization. Remote sensing is one of the most important methods used to estimate soil moisture. In this study, we developed a new nonlinear Erf-BP neural network method to establish a soil-moisture-content-estimation model with integrated multiple-resource remote-sensing data from high-resolution, hyperspectral and microwave sensors. Next, we compared the result with the single-resource remote-sensing data for SMC (soil-moisture content) estimation models by using the linear-fitting method. The results showed that the soil-moisture estimation model offers better accuracy by using multiple-resource remote-sensing data. Furthermore, the SMC predicted the results by using the new Erf-BP neural network with multiple-resource remote-sensing data and a good overall correlation coefficient of 0.6838. Compared with the linear model’s estimation results, the accuracy of the SMC estimation using the Erf-BP method was increased, and the RMSE decreased from 0.017 g/g to 0.0146 g/g, a decrease of 16.44%. These results also indicate that the improved algorithm of the Erf-BP artificial neural network has better fitting results and precision. This research provides a reference for multiple-resource remote-sensing data for soil-moisture estimation.

Soil moisture is one of the most important components of all the soil properties affecting the global hydrologic cycle. Optical remote sensing technology is one of the main parts of soil moisture estimation. In this study, we promote a soil moisture-estimating method with applications regarding various soil organic matters. The results indicate that the soil organic matter had a significant spectral feature at wavelengths larger than 900 nm. The existence of soil organic matter would lead to darker soil, and this feature was similar to the soil moisture. Meanwhile, the effect of the soil organic matter on its reflectance overlaps with the effect of soil moisture on its reflected spectrum. This can lead to the underestimation of the soil moisture content, with an MRE of 21.87%. To reduce this effect, the absorption of the soil organic matter was considered based on the Lambert–Beer law. Then, we established an SMC_g-estimating model based on the radiative transform theory while considering the effect of the soil organic matter. The results showed that the effect of the soil organic matter can be effectively reduced and the accuracy of the soil moisture estimation was increased, while MRE decreased from 21.87% to 6.53%.

Vegetation phenology is an important indicator of vegetation dynamics. The boreal forest ecosystem is the main part of terrestrial ecosystem in the Northern Hemisphere and plays an important role in global carbon balance. In this study, the dynamic threshold method combined with the ground-based phenology observation data was applied to extract the forest phenological parameters from MODIS NDVI time-series. Then, the spatiotemporal variation of forest phenology is discussed and the relationship between phenological change and climatic factors was concluded in the northeast China from 2011 to 2020. The results indicated that the distribution of the optimal extraction threshold has spatial heterogeneity, and the changing rate was 3% and 2% with 1° increase in latitude for SOS (the start of the growing season) and EOS (the end of the growing season). This research also notes that the SOS had an advanced trend at a rate of 0.29 d/a while the EOS was delayed by 0.47 d/a. This variation of phenology varied from different forest types. We also found that the preseason temperature played a major role in effecting the forest phenology. The temperature in winter of the previous year had a significant effect on SOS in current year. Temperature in autumn of the current year had a significant effect on EOS.