Fractional vegetation cover (FVC) is one of the most critical parameters in monitoring vegetation status. Accurate estimates of FVC are crucial to the use in land surface models. The dimidiate pixel model is the most widely used method for retrieval of FVC. The normalized difference vegetation index (NDVI) of bare soil endmember (NDVI_soil) is usually assumed to be invariant without taking into account the spatial variability of soil backgrounds. Two NDVI_soil determining methods were compared for estimating FVC. The first method used an invariant NDVI_soil for the Northeast China. The second method used the historical minimum NDVI along with information on soil types to estimate NDVI_soil for each soil type. We quantified the influence of variations of NDVI_soil derived from the second method on FVC estimation for each soil type and compared the differences in FVC estimated by these two methods. Analysis shows that the uncertainty in FVC estimation introduced by NDVI_soil variability can exceed 0.1 (root mean square error—RMSE), with the largest errors occurring in vegetation types with low NDVI. NDVI_soil with higher variation causes greater uncertainty on FVC. The difference between the two versions of FVC in Northeast China, is about 0.07 with an RMSE of 0.07. Validation using fine-resolution FVC reference maps shows that the second approach yields better estimates of FVC than using an invariant NDVI_soil value. The accuracy of FVC estimates is improved from 0.1 to 0.07 (RMSE), on average, in the croplands and from 0.04 to 0.03 in the grasslands. Soil backgrounds have impacts not only on NDVI_soil but also on other VI_soil. Further focus will be the selection of optimal vegetation indices and the modeling of the relationships between VI_soil and soil properties for predicting VI_soil.

Validation is an essential and important step before the application of remote sensing products. This paper introduces a prototype of the validation network for remote sensing products in China (VRPC). The VRPC aims to improve remote sensing products at a regional scale in China. These improvements will enhance the applicability of the key remote sensing products in understanding and interpretation of typical land surface processes in China. The framework of the VRPC is introduced first, including its four basic components. Then, the basic selection principles of the observation sites are described, and the principles for the validation of the remote sensing products are established. The VRPC will be realized in stages. In the first stage, four stations that have improved remote sensing observation facilities have been incorporated according to the selection principles. Certain core observation sites have been constructed at these stations. Next the Heihe Station is introduced in detail as an example. The three levels of observation (the research base, pixel-scale validation sites, and regional coverage) adopted by the Heihe Station are carefully explained. The pixel-scale validation sites with nested multi-scale observation systems in this station are the most unique feature, and these sites aim to solve some key scientific problems associated with remote sensing product validation (e.g., the scale effect and scale transformation). Multi-year of in situ measurements will ensure the high accuracy and inter-annual validity of the land products, which will provide dynamic regional monitoring and simulation capabilities in China. The observation sites of the VRPC are open, with the goal of increasing cooperation and exchange with global programs.

Soil moisture (SM) retrieval from SMOS (the Soil Moisture and Ocean Salinity mission) and SMAP (the Soil Moisture Active/Passive mission) passive microwave data over forested areas with required accuracy is of great significance and poses some challenges. In this paper, we used Ground Wireless Sensor Network (GWSN) SM measurements from 9 September to 5 November 2015 to validate SMOS and SMAP Level 3 (L3) SM products over forested areas in northeastern China. Our results found that neither SMOS nor SMAP L3 SM products were ideal, with respective RMSE (root mean square error) values of 0.31 cm³/cm³ and 0.17 cm³/cm³. Nevertheless, some improvements in SM retrieval might be achievable through refinements of the soil dielectric model with respect to high percentage of soil organic matter (SOM) in the forested area. To that end, the potential of the semi-empirical soil dielectric model proposed by Jun Liu (Liu’s model) in improving SM retrieval results over forested areas was investigated. Introducing Liu’s model into the retrieval algorithms of both SMOS and SMAP missions produced promising results. For SMAP, the RMSE of L3 SM products improved from 0.16 cm³/cm³ to 0.07 cm³/cm³ for AM (local solar time around 06:00 am) data, and from 0.17 cm³/cm³ to 0.05 cm³/cm³ for PM (local solar time around 06:00 pm) data. For SMOS ascending orbit products, the accuracy was improved by 56%, while descending orbit products improved by 45%.

Fractional vegetation cover (FVC) is one of the most critical parameters in monitoring vegetation status. Comprehensive assessment of the FVC products is critical for their improvement and use in land surface models. This study investigates the performances of two major long time serial FVC products: GEOV1 and Australian MODIS. The spatial and temporal consistencies of these products were compared during the 2000–2012 period over the main biome types across the Australian continent. Their accuracies were validated by 443 FVC in-situ measurements during the 2011–2012 period. Our results show that there are strong correlations between the GEOV1 and Australian MODIS FVC products over the main Australian continent while they exhibit large differences and uncertainties in the coastal regions covered by dense forests. GEOV1 and Australian MODIS describe similar seasonal variations over the main biome types with differences in magnitude, while Australian MODIS exhibit unstable temporal variations over grasslands and shifted seasonal variations over evergreen broadleaf forests. The GEOV1 and Australian MODIS products overestimate FVC values over the biome types with high vegetation density and underestimate FVC in sparsely vegetated areas and grasslands. Overall, the GEOV1 and Australian MODIS FVC products agree with in-situ FVC values with a RMSE around 0.10 over the Australian continent.

Volume scattering (VS) estimation plays a critical role in microwave emission modeling of the snowpack. However, it is challenging to obtain VS accurately for different frequencies by using the microwave emission model of layered snowpacks (MEMLS), which is one of the representative microwave emission models. This article develops a new VS method to consider frequency and exponential correlation length based on a snowfield campaign from November 2015 to April 2016 in Altay, China. Compared with the commonly used empirical and improved Born approximation (IBA) algorithms, the proposed VS algorithm exhibits better performances at both 18 and 36 GHz with a wide range of snow grain sizes. The bias of brightness temperatures at vertical polarization from the proposed algorithm against the observed brightness temperatures are 1.1 K and −0.4 K at 18 and 36 GHz, respectively; the root mean square errors (RMSEs) are 1.8 K and 2.6 K, respectively. The RMSEs decreased by 16.2 K at 18 GHz and 6.5 K at 36 GHz compared with those from the empirical methods and by 2.1 K and 22.2 K compared with those from the IBA. This work demonstrates that the VS difference between 18 and 36 GHz is larger and the dependence of VS on grain size is weaker than those represented by existing methods.

The spectral features of soils are a comprehensive representation of their physicochemical parameters, surface states, and internal structures. To date, spectral measurements have been mostly performed for powdered soils and smooth aggregate soils, but rarely for cracked soils; a common state of soda-saline soils. In this study, we measured the spectral features of 57 saline soil samples in powdered, aggregate, and cracked states for comparison. We then explored in depth the factors governing soil spectral features to build up simple and multiple linear regression models between the spectral features and physicochemical parameters (salt content, Na⁺, pH, and electronic conductivity (EC)) of saline soils in different states. We randomly selected 40 samples to construct the models, and used the remaining 17 samples for validation. Our results indicated that the regression models worked more effectively in predicting physicochemical parameters for cracked soils than for other soils. Subsequently, the crack ratio (CR) was introduced into the regression models to modify the spectra of soils in powdered and aggregate states. The accuracy of prediction was improved, evidenced by a 2–11% decrease in the parameters mean absolute error (MAE).