Zhuangzhuang Feng

Irrigation-driven greening is essential for northwest China’s dryland ecosystems, where vegetation growth depends on key hydrological factors, including precipitation (PRE), evapotranspiration (ET), soil moisture (SM), and irrigation water use (IWU), which affect water availability to a certain extent. To assess greening sustainability, a 1 km IWU dataset was created for 2001–2022 by combining remote sensing and ancillary data using machine learning, overcoming limited irrigation records. By linking IWU with the normalized difference vegetation index (NDVI) and analyzing trends in irrigated areas, we implemented a regional zonation approach to identify specific risk areas and evaluated both greening sustainability and vegetation responses using water balance (WB) and various hydrological variables. The results show that NDVI has increased widely over the past two decades, with sustained positive WB and stable irrigation, indicating improved water availability. However, spatial differences exist: 35.98% of irrigated areas have rising NDVI but falling IWU, especially in the east, where higher NDVI, IWU, WB, PRE, and ΔSM (soil moisture difference between growing season end and start) reflect favorable climate and hydrology; attention should also be directed toward potential deep percolation and saline sinks. In contrast, areas with high IWU often displayed elevated NDVI but declining water availability, suggesting unsustainable greening due to excessive irrigation. In addition, the SCD_IWU-SCD_NDVI class dominates among significant NDVI-IWU trends, indicating potential for sustainable irrigation under certain drought and climate conditions. Overall, the northwestern portion of the study area exhibits the lowest water availability; cities such as Urumqi warrant particular attention. These findings identify at-risk areas and those with better water resilience, supporting targeted water–vegetation management.

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 (ploughing, harrowing, ridging, crusting, etc.). The study results show: 1) Different agricultural tillage practices lead to significant differences in SSR. The ploughed and harrowed plot correspond 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.

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.

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 (σ_PQ) does not change with the growth stage of crops, which is in conflict with the real-life situation. Therefore, the normalization results of σ_PQ based on these existing methods will affect the accuracy of object classification, target recognition, and land surface parameter inversion. Here, the change in θ-σ_PQ relationship was investigated based on time-series (April to October) σ_PQ 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 σ_PQ. Through the accuracy evaluation, the following conclusions are obtained: (1) the dependence (referring to N) of Sentinel 1 C-band σ_PQ 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 σ_PQ 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.