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Timely and accurate monitoring of large-scale crop harvest progress (CHP) is crucial for optimizing agricultural management. Remote sensing shows great potential, but most studies focus on small-scale analysis with limited features and single data sources, lacking large-area mapping. To address this, we developed a regional-scale CHP monitoring framework combining optical and radar data. We analyzed the response of features to corn harvest at Youyi Farm. We developed new indices and algorithms, achieving large-scale CHP mapping. Our findings: First, all 15 features respond to harvest, with shortwave infrared reflectance and the vegetation tillage index (VTI) being the most sensitive. Second, the current binary classification algorithm for mature corn (MC) and crushed straw (CS) is unsuitable for regions with plowed soil (PS). It often misclassifies PS as MC due to similar optical characteristics, causing inaccurate corn harvest monitoring. The new VTI and simple VTI solve this by better distinguishing MC, CS, and PS. Third, corn harvest progress monitoring shows high accuracy, with an overall accuracy of 0.99 for optical data and 0.95 for radar data. Fourth, combining Sentinel-1 and Sentinel-2 data increased the monitoring frequency from 6–8 days to an average of 2–3 days. Fifth, a time-series misclassification correction strategy was developed and applied, correcting an average area of 3.3% of the study area. Sixth, an earlier harvest start time in 2021, aligned with drought occurrences, confirms the feasibility of the CHP mapping framework. This research provides an effective tool for monitoring CHP, which can support agricultural management.

Soil surface roughness (SSR) is an important factor affecting soil erosion and soil nutrient transport. Human tillage leads to increased instability in SSR, and the characteristics of SSR caused by different tillage practices await further study. This research utilizes terrestrial laser scanning (TLS) to measure the SSR of six farmland plots (25 m × 25 m) and analyzes the characteristics of SSR under different tillage practices (plowing, harrowing, ridging, crusting, etc.). The study results show: 1) Different agricultural tillage practices lead to significant differences in SSR. The plowed and harrowed plot corresponds to the maximum (2.49 cm) and minimum (1.5 cm) root mean square height (RMSH), respectively. Correlation length (CL) is more affected by different tillage practices than RMSH. The difference in CL between the ridged and harrowed plot is 2.6 times. 2) Ridging and crusting caused significant directional variation in SSR. The SSR anisotropy of the harrowed plot can be disregarded. 3) Under the condition of measuring soil profile in 12 directions and randomly sampling 70 times in each direction, the profile length must be at least 3 m to ensure that the measurement error of SSR is better than 5% compared to the “true” value. TLS can measure two-dimensional SSR. Therefore, it is only necessary to ensure that the measurement range is at least 3 m × 3 m. The study results provide a reference for the high-precision measurement of SSR (RMSH and CL) under different agricultural tillage practices.

The impact of fertilization and irrigation on heavy metal accumulation in saline–alkali soil and its underlying mechanisms are critical issues given the constraints that soil salinization places on agricultural development and crop quality. This study addressed these issues by investigating the effects of adjusting organic fertilizer types, proportions, and irrigation volumes on the physicochemical properties of lightly to moderately saline–alkali soils and analyzing the interaction mechanisms between microorganisms and heavy metals. The results indicate that the rational application of organic fertilizers combined with supplemental irrigation can mitigate soil salinity accumulation and water deficits, and reduce the soil pH, thereby enhancing soil oxidation, promoting nitrogen transformation and increasing nitrate–nitrogen levels. As the proportion of organic fertilizers increased, heavy metal residues, enrichment, and risk indices in the crop grains also increased. Compared to no irrigation, supplemental irrigation of 22 mm during the grain-filling stage increased soil surface Cd content, Zn content, and the potential ecological risk index (HRI) by 10.2%, 3.1%, and 8%, respectively, while simultaneously reducing the heavy metal content in grains by 12–13.5% and decreasing heavy metal enrichment. Principal component analysis revealed the primary factors influencing Cu and Zn residues and Cd accumulation in the crop grains. Soil salinity was significantly negatively correlated with soil pH, organic matter, total nitrogen, and ammonium nitrogen, whereas soil organic matter, total nitrogen, ammonium nitrogen, soil pH, oxidation–reduction potential, soluble nitrogen, and microbial biomass nitrogen were positively correlated. The accumulation and residues of Zn and Cu in the soil were more closely correlated with the soil properties compared to those of Cd. Specifically, Zn accumulation on the soil surface was primarily related to aliphatic organic functional groups, followed by soil salinity. Residual Zn in the crop grains was primarily associated with soil oxidation–reduction properties, followed by soil moisture content. The accumulation of Cu on the soil surface was mainly correlated with the microbial biomass carbon (MBC), whereas the residual Cu in the crop grains was primarily linked to the soil moisture content. These findings provide theoretical insights for improving saline–alkali soils and managing heavy metal contamination, with implications for sustainable agriculture and environmental protection.

To mitigate the issues of severe farmland soil salinization, the environmental degradation stemming from the overuse of chemical fertilizers, and suboptimal soil composition, a study was conducted to investigate the influence of different types and ratios of organic fertilizers on the physical and chemical attributes of saline–alkali soil. This study aimed to investigate the relationship between different types and proportions of organic fertilizers, soil moisture, organic fertilizer application rates, organic carbon molecular structure, and the soil environment in saline–alkali soils. Reducing the application of chemical fertilizers and substituting them with organic fertilizers can improve the soil quality of saline–alkali lands. The results indicated that replacing a part of the urea with organic fertilizer in saline–alkali farmland reduced the soil salinity by 11.1 to 22.8% in the 0–60 cm soil layer, decreased the soil pH by 0.11 to 1.52%, and increased the soil redox potential (Eh) values by 2.5 to 4.3% in the 0–20 cm layer of the mild and moderate saline–alkali soils. It also decreased the accumulation of the soil organic matter (OM) during the growing season. Compared to commercial organic fertilizers, natural organic fertilizers increased the accumulation of the soil soluble carbon (DOC) and nitrogen (DON), resulting in less soil salinity accumulation. When commercial organic fertilizer was applied in a 1:1 ratio with inorganic fertilizer, the salt accumulation was minimized. Compared to conventional fertilization, organic fertilizer reduced the accumulation of the NH4+-N (ammonium nitrogen) and NO3−-N (nitrate nitrogen) in the soil by 3.1 to 22.6%. In comparison to conventional chemical fertilizers, the application of organic fertilizer in the mild and moderate saline–alkali soils increased the accumulation of the DOC, DON, microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), and microbial quotient during the grain-filling stage. Specifically, it increased the DOC, DON, and DOC/DON by 12.7 to 26.7%, 12 to 59.3%, and 15.2 to 35.5%, respectively. The application of commercial organic fertilizer in the mild saline–alkali soils increased the MBC, MBN, MBC/SOC, and MBN/TN by 37.1, 65.6, 36.7, and 4.7%, respectively. Through analyzing the relative proportions of soil surface organic carbon functional groups during the grain filling period, we observed that, after the application of organic fertilizer, the OM in the mildly salinized soils primarily originated from terrestrial plant litter, whereas, in moderately salinized soils, the OM was mainly derived from microbial sources.

Abstract 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.

Given the low water and fertiliser use efficiency and the extensive distribution of sand interlayered soil in the Hetao irrigation district (HID), this study aimed to investigate the effects of different irrigation and fertilisation regimes on root parameters and yield in spring maize grown in sand interlayered soil. A two-year field plot experiment was conducted using the spring maize “Ximeng 3358” under three irrigation and nitrogen levels. Root length (RL), surface area (RS), diameter (RD), volume (RV), and length density (RLD), grain yield, and water use efficiency (WUE) were examined. Root growth was inhibited at the sand layer, with approximately 72.46–87.37% of the roots concentrated in the 0–40 cm soil layer. Notably, the proportion of roots in the bottom layer was 24.61–87.37% higher than that in the sub-bottom layer. Moreover, RL, RS, RD, and RV peaked in the medium irrigation and nitrogen fertilisation (I2F2) treatment. Furthermore, correlation analysis showed that the root parameters were significantly positively correlated with yield and WUE, with RS being most correlated to yield and WUE. Roots at a narrow row spacing of 20 cm (NR20) and at a depth of 10–20 cm were strongly correlated with yield and WUE. Conclusively, the I2F2 treatment can be used as the optimal combination of water and nitrogen for sand interlayered soil farmlands.

Summary: New vessel formation (angiogenesis) is an essential physiological process for embryologic development, normal growth, and tissue repair. Angiogenesis is tightly regulated at the molecular level. Dysregulation of angiogenesis occurs in various pathologies and is one of the hallmarks of cancer. However, most existing methods for evaluating cell vascular formation are limited to static analysis and prone to bias due to time, field of vision, and parameter selection. Code scripts, such as AngiogenesisAnalyzer.ijm, AutomaticMeasure.ijm, and VM.R., were developed to study the dynamic angiogenesis process. This method was used to screen drugs that could affect the time, maximum value, tilt, and decline rate of cell vascular formation and angiogenesis. Animal experiments have confirmed that these drugs could inhibit the formation of blood vessels. This work provides a new perspective for the research of angiogenesis process and is helpful to the development of drugs related to angiogenesis.

Passive microwave remote sensing is an effective way to obtain global soil moisture (SM) measurements, and many studies have explored the uncertainty inherent in microwave-based SM products. However, SM product accuracy has not been evaluated in northeast China, a national and global production base for commodity grain. In this study, a ground-based wireless sensor network with 28 observation nodes that were spatially distributed within 36 × 36 km was established to achieve satellite-scale “true” SM values through sensor calibration for specific soil types, senor consistency testing, and spatial scale transformation. The uncertainties of four passive microwave SM products (SMAP L3, SMOS L3, the Japan Aerospace Exploration Agency/JAXA AMSR2, and FY3C) were investigated and the following conclusions were obtained: 1) SMAP SM accuracy was very close to the expected application accuracy of 0.04 cm3/cm3, followed by SMOS, FY3C, and AMSR2; 2) for SMOS and SMAP, there were no significant temporal changes in SM errors, except for the larger error of descending SMOS SM and June SM values for descending SMAP and ascending SMOS. AMSR2 SM generally underestimated field SM, while FY3C SM values under low vegetation conditions were more consistent with field data, with an error of about 0.06 cm3/cm3; 3) agricultural activities and rainfall caused the soil surface roughness to increase or decrease within a growing season, which may have been an important source of satellite-scale SM error indicated by high bias values in July for both SMAP and SMOS; and 4) the standard deviation of field SM (0.06 cm3/cm3) produced a SMAP SM error of about 0.06 cm3/cm3 in low vegetation water content conditions, indicating that SM spatial heterogeneity cannot be ignored in the retrieval algorithm. This article investigated the accuracy and error sources of four satellite SM products in the farmland area of northeast China, and identified future research directions for further improving SM algorithms.

In order to reduce the impact of noise on the accuracy of inversion products based on SAR images, many filtering algorithms have been developed for noise reduction of SAR images. This paper proposes a filtering method based on the spatial autocorrelation feature of the block fast Fourier transform (BFFT). The method statistically analyses the autocorrelation length of speckle noise on Sentinel-1B images for different features and then constructs a relationship between autocorrelation length and noise period. After that, the size of the optimal FFT filtering window radius was determined based on the relationship between the noise period and the components in the image frequency domain. Finally, we filtered the SAR image within the parcels. We compared BFFT with six commonly used filtering methods. The results show that: (1) The noise periods of the soybean, corn, paddy, and water objects on the SAR image have little difference, with noise periods of 3.36, 3.17, 3.13, and 3.14 pixels on the VV polarization and 3.49, 3.17, 2.94, and 2.42 pixels on the VH polarization; (2) after the BFFT filtering in the land parcel area, the mean value of the backscattering coefficient (BC) kept constant, whilst at the same time, the standard deviation (STD) was reduced to half of that before the filtering and (3) the BFFT and NLM filtering methods have a better effect on noise reduction inside the block. The BFFT filtering method retains the variation trend between different regions within the block and preserves the block boundary’s clarity. This study provides a new idea for refined image processing.

The model-free predictive control (MFPC) scheme is an effective scheme to enhance the parameter robustness of model predictive control. However, the MFPC scheme can be affected by the current gradient updating frequency. This paper proposes an improved MFPC scheme for a T-type three-level inverter. First, a novel current gradient updating method is designed to estimate all current gradients per control period, which uses the current gradient relationship between different voltage vectors and eliminates the effect of current gradients updating stagnation. Moreover, a sector judgment method based on the current gradient is proposed. Redundant small vectors are accurately judged and the computational burden is greatly reduced. Finally, simulation and experimental comparisons on a T-type three-level inverter verify the effectiveness of the proposed MFPC scheme.

Both radar and optical signals are sensitive to the change of surface soil moisture (SSM) and surface roughness properties (such as root mean squared height- RMSH), and the accuracy of retrieved SSM from single radar and optical remote sensing data is influenced by the spatiotemporal change of surface roughness. Here, we attempt to explore a method to simultaneously estimate SSM and RMSH of bare soil by combining optical and radar data, so as to weaken the effect of surface roughness on SSM inversion results. To achieve this goal, two satellite synchronous ground experiments were carried out, collecting 88 sampling plots each with an area of 50 m × 50 m. Radar backscattering coefficient and spectral reflectance are uniformly corrected to a fixed observation direction and solar incident direction respectively, which can eliminate the difference of satellite signal resulted from various sun-satellite geometry. Combining radar backscattering and optical reflectance model, Sentinel-1 and Sentinel-2 data are used to simultaneously retrieve SSM and RMSH of bared soils, and some conclusions are given as below: 1) a strong correlation is observed for (radar and optical) satellite signals and soil surface parameters (SSM and RMSH); 2) a higher accuracy was obtained by the combined use of optical and radar data, indicated by the decreased root mean squared error of retrieved SSM (~0.045 cm3/cm3) and RMSH (~0.8 cm); 3) the further improvement of retrieved SSM and RMSH was achieved by introducing their initial values, revealing that the prior knowledge of soil properties is also beneficial to improve the retrieval accuracy. This study proposed an framework for simultaneous estimation of SSM and RMSH by combining optical and radar data, and its feasibility is verified by experimental data.

Wide mode SAR images have an apparent incidence angle effect. The existing incident angle normalization methods assume that the relationship between the incident angle (θ) and the backscattering coefficient (σPQ) does not change with the growth stage of crops, which is in conflict with the real-life situation. Therefore, the normalization results of σPQ based on these existing methods will affect the accuracy of object classification, target recognition, and land surface parameter inversion. Here, the change in θ-σPQ relationship was investigated based on time-series (April to October) σPQ of maize canopies in northeast China, and a dynamic method based on normalized difference vegetation index (NDVI) was developed to normalize the effect of θ on σPQ. Through the accuracy evaluation, the following conclusions are obtained: (1) the dependence (referring to N) of Sentinel 1 C-band σPQ on θ varies with maize NDVI. In addition, the value of N changed from 9.35 to 0.66 at VV polarization from bare soil to biomass peak, and from 6.26 to 0.99 at VH polarization; (2) a dynamic method was proposed to quantify the change of N based on its strong correlation with NDVI, indicated by R2 of 0.82 and 0.80 for VV and VH polarization, respectively; and (3) the overall root mean square error of normalized σPQ based on the newly-developed dynamic method is 0.51 dB, and this accuracy outperforms the original first-order cosine method (1.37 dB) and cosine square law method (1.08 dB) by about 63% and 53% on the whole. This study provides a dynamic framework for normalizing radar backscatter coefficient, improving the retrieval accuracy of land surface parameters from radar remote sensing.

The launch of the SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) satellites has led to the development of a series of L-band soil moisture retrieval algorithms. In these algorithms, many input parameters (such as leaf area index and soil texture) and empirical coefficients (such as roughness coefficient (hP, NRP) and crop structure parameter (bP, ttP)) are needed to calculate surface soil moisture (SSM) from microwave brightness temperature. Many previous studies have focused on how to determine the value of these coefficients and input parameters. Nevertheless, it can be difficult to obtain their ‘real’ values with low uncertainty across large spatial scales. To avoid this problem, a passive microwave remote sensing SSM inversion algorithm based on the principle of change detection was proposed and tested using theoretical simulation and a field SSM dataset for an agricultural area in northeastern China. This algorithm was initially used to estimate SSM for radar remote sensing. First, theoretical simulation results were used to confirm the linear relationship between the change rates for SSM and surface emissivity, for both H and V polarization. This demonstrated the reliability of the change detection algorithm. Second, minimum emissivity (or the difference between maximum emissivity and minimum emissivity) was modeled with a linear relationship between vegetation water content, derived from a three-year (2016–2018) SMAP L3 SSM dataset. Third, SSM values estimated by the change detection algorithm were in good agreement with SMAP L3 SSM and field SSM, with RMSE values ranging from 0.015~0.031 cm3/cm3 and 0.038~0.051 cm3/cm3, respectively. The V polarization SSM accuracy was higher than H polarization and combined H and V polarization accuracy. The retrieved SSM error from the change detection algorithm was similar to SMAP SSM due to errors inherited from the training dataset. The SSM algorithm proposed here is simple in form, has fewer input parameters, and avoids the uncertainty of input parameters. It is very suitable for global applications and will provide a new algorithm option for SSM estimation from microwave brightness temperature.