Chinese Soil Moisture Observation Network and Time Series Data Set for High Resolution Satellite Applications.

High-quality ground observation networks are an important basis for scientific research. Here, an automatic soil observation network for high-resolution satellite applications in China (SONTE-China) was established to measure both pixel- and multilayer-based soil moisture and temperature. SONTE-China is distributed across 17 field observation stations with a variety of ecosystems, covering both dry and wet zones. In this paper, the average root mean squared error (RMSE) of station-based soil moisture for well-characterized SONTE-China sites is 0.027 m3/m3 (0.014~0.057 m3/m3) following calibration for specific soil properties. The temporal and spatial characteristics of the observed soil moisture and temperature in SONTE-China conform to the geographical location, seasonality and rainfall of each station. The time series Sentinel-1 C-band radar signal and soil moisture show strong correlations, and the RMSE of the estimated soil moisture from radar data was lower than 0.05 m3/m3 for the Guyuan and Minqin stations. SONTE-China is a soil moisture retrieval algorithm that can validate soil moisture products and provide basic data for weather forecasting, flood forecasting, agricultural drought monitoring and water resource management.

Full-band, multi-angle, multi-scale, and temporal dynamic field spectral measurements in China.

Field-measured spectra are critical for remote sensing physical modelling, retrieval of structural, biophysical, and biochemical parameters, and other practical applications. We present a library of field spectra, which includes (1) portable field spectroradiometer measurements of vegetation, soil, and snow in the full-wave band, (2) multi-angle spectra measurements of desert vegetation, chernozems, and snow with consideration of the anisotropic reflectance of land surface, (3) multi-scale spectra measurements of leaf and canopy of different vegetation cover surfaces, and (4) continuous reflectance spectra time-series data revealing vegetation growth dynamics of maize, rice, wheat, rape, grassland, and so on. To the best of our knowledge, this library is unique in simultaneously providing full-band, multi-angle, multi-scale spectral measurements of the main surface elements of China covering a large spatial extent over a 10-year period. Furthermore, the 101 by 101 satellite pixels of Landsat ETM/OLI and MODIS surface reflectance centered around the field site were extracted, providing a vital linkage between ground measurements and satellite observations. The code language used for this work is Matlab 2016a.

Faster velocity changes in the near-surface soil freeze state in croplands than in forests across northeast China from 1979 to 2020.

Climate warming has significantly changed the near-surface soil freeze state, significantly impacting terrestrial ecosystems and regional agroforestry production. As Northeast China (NEC) is highly sensitive to climate change, this study introduces the concept of velocity to analyze the spatial pattern of frozen days (FDAY), onset date of soil freeze (FON), offset date of soil freeze (FOFF), and number of soil freeze/thaw cycles in spring (FTC) in NEC from 1979 to 2020. We observed that the velocity changes of FDAY, FON, and FTC in croplands were significantly higher than those in forests (difference >1 km yr-1), with the fastest velocity changes found in the cropland of the Songnen Plain. The highest velocity of FOFF was found in the forests of the Greater Khingan Range. In most study areas (>60%), the isoline of FDAY/FON/FOFF/FTC showed a northward movement. The isoline of FDAY/FON/FOFF/FTC moved in the cold direction in each cropland region (Sanjiang, Songnen, and Liaohe River Plains) and forest regions (Greater Khingan and Lesser Khingan Ranges, and the Changbai Mountains). The results of the quantitative analysis indicate that air temperature (TA) had a more significant effect on the velocity change of FDAY and FON in cropland, whereas snowpack is the dominant factor in forests. In both forests and croplands, the main factor affecting the velocity of FOFF was snowpack, and TA mainly affected the FTC. This study is significant for formulating regional climate change countermeasures and maintaining ecological security in cold regions.

A Fast Storage Method for Drone-Borne Passive Microwave Radiation Measurement.

A drone-borne microwave radiometer requires a high sampling frequency and a continuous acquisition capability to detect and mitigate radio frequency interference (RFI), but existing methods cannot store such a large amount of data. In this paper, the dual polling write method (DPSM) for secure digital cards triggered by a timer under a multitask framework based on STM32 MCU is proposed to meet the requirements of continuous data storage. The card programming step was changed from a query waiting structure to a polling query flag bit structure, and time-sharing processing and parallel processing were used to simulate multithreading. The experimental results were as follows: (1) the time consumption of the whole storage procedure was reduced from 4000 microseconds to 200-400 microseconds; (2) the time consumption of the card programming step was reduced from 3000 microseconds in the first block and 1000 microseconds in the second and subsequent blocks to 17-174 microseconds and 18-71 microseconds, respectively, compared with the existing method; (3) the delay in the whole sampling cycle was reduced from 3942 microseconds to 0 microseconds. The results of this paper can meet the data storage requirements of a drone-borne microwave radiometer and be applied to the high-speed storage of other devices.

Comparative Analysis of the Spectral Response to Soil Salinity of Saline-Sodic Soils under Different Surface Conditions.

Desiccation cracking is a very common surface soil phenomenon of saline-sodic land. The objective of this study was to investigate the effects of salt content on the spectral reflectance of soil with and without desiccation cracks. To achieve our objective, a cracking test was performed using 17 soil samples. Following the tests, crack parameters were extracted, and correlation analysis was then performed between crack parameters and four soil properties: Na⁺, salinity (total concentration of ions), pH, and electric conductivity (EC). In order to select the optimum spectral measurement method and develop prediction models, spectral response to different soil properties were compared between the cracked soil samples and the comparative soil samples composed of the 2 mm particle size fraction processed by traditional methods. The results indicate that soil salinity dominated cracking propagation with a positive correlation. Since area and volume scattering are closer to what occurs in the field, a greater spectral response to soil properties was found for cracked soil samples relative to the comparative soil samples in the near-infrared and shortwave-infrared regions. The R² of optimal linear prediction models based on the cracked soil samples were 0.74, 0.67, 0.58, and 0.67 for Na⁺, salinity, pH, and EC, respectively.

[Estimation of vegetation water content from Landsat 8 OLI data].

The present paper aims to analyze the capabilities and limitations for retrieving vegetation water content from Landsat8 OLI (Operational Land Imager) sensor-new generation of earth observation program. First, the effect of soil background on canopy reflectance and the sensitive band to vegetation water content were analyzed based on simulated dataset from ProSail model. Then, based on vegetation water indices from Landsat8 OLI and field vegetation water content during June 1 2013 to August 14 2013, the best vegetation water index for estimating vegetation water content was found through comparing 12 different indices. The results show that: (1) red, near infrared and two shortwave infrared bands of OLI sensor are sensitive to the change in vegetation water content, and near infrared band is the most sensitive one; (2) At low vegetation coverage, solar radiation reflected by soil background will reach to spectral sensor and influence the relationship between vegetation water index and vegetation water content, and simulation results from ProSail model also show that soil background reflectance has a significant impact on vegetation canopy reflectance in both wet and dry soil conditions, so the optimized soil adjusted vegetation index (OSAVI) was used in this paper to remove the effect of soil background on vegetation water index and improve its relationship with vegetation water content; (3) for the 12 vegetation water indices, the relationship between MSI2 and vegetation water content is the best with the R-square of 0.948 and the average error of vegetation water content is 0.52 kg · m(-2); (4) it is difficult to estimate vegetation water content from vegetation water indices when vegetation water content is larger than 2 kg · m(-2) due to spectral saturation of these indices.