Apple (Malus domestica Borkh. cv. “Fuji”), an important cash crop, is widely consumed around the world. Accurately predicting preharvest apple fruit yields is critical for planting policy making and agricultural management. This study attempted to explore an effective approach for predicting apple fruit yields based on time-series remote sensing data. In this study, time-series vegetation indices (VIs) were derived from Planet images and analyzed to further construct an accumulated VI ($\sum \mathrm{VIs}$)-based random forest (${\mathrm{RF}}_{\sum \mathrm{VI}}$) model and a Carnegie–Ames–Stanford approach (CASA) model for predicting apple fruit yields. The results showed that (1) $\sum \mathrm{NDVI}$ was the optimal predictor to construct an RF model for apple fruit yield, and the R², RMSE, and RPD values of the ${\mathrm{RF}}_{\sum \mathrm{NDVI}}$ model reached 0.71, 16.40 kg/tree, and 1.83, respectively. (2) The maximum light use efficiency was determined to be 0.499 g C/MJ, and the CASA_SR model (R² = 0.57, RMSE = 19.61 kg/tree, and RPD = 1.53) performed better than the CASA_NDVI model and the CASA_Average model (R², RMSE, and RPD = 0.56, 24.47 kg/tree, 1.22 and 0.57, 20.82 kg/tree, 1.44, respectively). (3) This study compared the yield prediction accuracies obtained by the models using the same dataset, and the ${\mathrm{RF}}_{\sum \mathrm{NDVI}}$ model (RPD = 1.83) showed a better performance in predicting apple fruit yields than the CASA_SR model (RPD = 1.53). The results obtained from this study indicated the potential of the ${\mathrm{RF}}_{\sum \mathrm{NDVI}}$ model based on time-series Planet images to accurately predict apple fruit yields. The models could provide spatial and quantitative information of apple fruit yield, which would be valuable for agronomists to predict regional apple production to inform and develop national planting policies, agricultural management, and export strategies.

The accurate and rapid inversion of soil salinity in regions based on the fusion of multisource remote sensing is not only practical for the treatment and utilization of saline soil but also the main trend in the development of quantitative soil salinization remote sensing. In this paper, the use of a numerical regression method to fuse spectral indexes based on high-spatial-resolution unmanned aerial vehicle (UAV) images and low-spatial-resolution satellite images was proposed to deeply assess the internal relationships between different types of remote sensing data. An inversion model of soil salt content (SSC) was constructed based on high-spatial-resolution UAV images, and the spectral indexes involved in the fusion were selected from the model. Then, a quadratic polynomial fusion function describing the relationship between the spectral indexes based on the two images was established to correct the spectral indexes based on the low-spatial-resolution satellite image (from Sentinel-2A). Then, scenario 1 (the best model based on Sentinel-2A used for the unfused Sentinel-2A spectral index), scenario 2 (the best inversion model based on UAV used for the unfused Sentinel-2A-based spectral index), and scenario 3 (the best inversion model based on UAV used for the fused Sentinel-2A-based spectral index) were compared and analyzed, and the SSC distribution map was obtained through scenario 3. The results indicate that the scenario 3 had highest accuracy, with the calibration R2 improving by 0.078-0.111, the root mean square error (RMSE) decreasing by 0.338-1.048, the validation R2 improving by 0.019-0.079, the RMSE decreasing by 0.517-1.030, and the ratio of performance to deviation (RPD) improving by 0.185-0.423. Therefore, this method can improve the accuracy of SSC remote sensing inversion, which is conducive to the accurate and rapid monitoring of SSC.

Soil salt information from unified soil salinity content (SSC) inversion models based on all samples is insufficient for accurate regional SSC monitoring. Here, a method of building SSC inversion models for different salinization grades is proposed, combined with unmanned aerial vehicle (UAV) and Sentinel-2A images. According to different salinization grades, three groups of samples (mild (M), medium-severe (S), and whole (W)) were obtained. Their SSC spectra characteristics, parameters, and quantitative inversion models were analysed, constructed, and compared, based on UAV images, and substituted into different salinization grade areas in Sentinel-2A. The UAV-based models for M and S outperformed those for W; the same trend occurred after substituted into Sentinel-2A images. The inversion results were closer to the field survey results. Models for different salinization grades achieved better regional inversion than those of the whole. UAV-based SSC models can be applied to satellite imagery to invert regional SSC.

Rapid and accurate soil salinity (SS) analysis is essential for effective management of salinized agricultural lands. However, the potential of utilizing periodic remote sensing satellite data to improve the accuracy of regional SS inversion requires further exploration. This study proposes a novel inversion approach that combines multi-temporal images captured near the SS field sampling period (September 5–10, 2020). Focusing on Wudi County, China, we analyzed three time-series Sentinel-2 images obtained near the sampling period to determine the inversion time window. Images within the window were synthesized into four combined-temporal images through three arithmetic operation strategies and one band combination strategy. SS-related spectral variables derived from both single and combined-temporal images were selected using Random Forest (RF), ReliefF, and Support Vector Machine Recursive Feature Elimination algorithms (SVM-RFE). Subsequently, inversion models were developed and compared using an Extreme Learning Machine. The optimal model was then applied to map regional SS distribution. The results demonstrate that: (1) combined-temporal models consistently outperformed single-temporal models, particularly those employing the band combination strategy, showing a 0.25–0.53 higher mean Relative Percentage Deviation (RPD); (2) models utilizing RF for variable selection exhibited superior stability and efficiency, with a mean RPD 0.02 to 0.04 higher than models using other algorithms; (3) the ELM model with band combination image and RF variable selection achieved the highest validation precision (Coefficient of Determination = 0.72, Root Mean Square Error = 0.87 dS/m, RPD = 1.93); (4) the final SS inversion map revealed a spatial gradient of increasing salinity in farmland from the southwestern area toward the northeastern coastal region, with 46.7% of farmland exhibiting yield-affecting salinity levels. These findings provide empirical insights into the development of soil remote sensing techniques and supporting agricultural-environmental management strategies.

In the context of rapid social and economic development, it is important to clarify the evolution process of production–living–ecological space (PLES) and its eco-environmental effects on the sustainable utilization of land resources on industrial bases. Based on existing research, it is difficult to accurately measure the differences in eco-environment quality caused by conversions between land-use types in different years. This paper puts forward an improved area-weighted calculation method of the eco-environmental quality index (EQI), combining the land-use transfer matrix and ecological contribution rate to analyze eco-environmental effects on typical industrial bases. The results show that PLES transitions in Gangcheng District from 2009 to 2020 have been mainly from agricultural production and grassland ecology to forest ecology space, from urban living to industrial production space, and from grassland ecology to agricultural production space. This improved method can capture the environmental changes caused by conversions between land-use types in different years. The EQI of agricultural production space increased from 0.373 to 0.388, while the EQI of forest ecology space decreased from 0.810 to 0.739, and the comprehensive EQI increased from 0.441 to 0.470, showing that the eco-environment quality of the study area was improved overall. The conversions of agricultural production and grassland ecology space to forest ecology space were the main factors in improving eco-environment quality, and the conversions of grassland and forest ecology to agricultural production and of agricultural production to industrial production space were the main reasons for the deterioration of eco-environment quality. This paper may contribute to the scientific planning of land-use direction, support the coordination of regional economic and ecological development, and provide case references for similar regions.

Remote sensing has become an effective way for regional soil organic matter (SOM) quantitative analysis. Topographic factors affect SOM content and distribution, also influence the accuracy of SOM remote sensing inversion. In large region with complex topographic conditions, characteristic topographic factors of SOM in different topographic regions are unknown, and the effect of combining characteristic topographic factors with spectral parameters on improving SOM inversion accuracy remains to be further studied. Three typical topographic regions of Shandong Province in China, namely Western plain region (WPR), Central and southern mountain region (CSMR), Eastern hilly region (EHR), were selected. Topographic factors, namely Elevation, Slope, Aspect and Relief Amplitude, were introduced. Respectively, the characteristic topographic factors and spectral parameters of SOM in each region were identified. The SOM inversion models were built separately for each region by integrating spectral parameters with topographic factors. The results revealed that as for the characteristic topographic factors of SOM, none was in the WPR, E, RA, and S were in the CSMR, E and RA were in the EHR. In combination with characteristic topographic factors, the accuracy of SOM spectral inversion models improved, the calibration R² increased by 0.075–0.102, the RMSE (Root mean square error) decreased by 0.162–0.171 g/kg, the validation R² increased by 0.067–0.095, the RMSE decreased by 0.236–0.238 g/kg, and RPD (Relative prediction deviation) increased by 0.129–0.169. The most significant improvement was observed in the CSMR with the calibration R² of 0.725, the validation R² of 0.713 and the RPD of 1.852, followed by the EHR. This study not only contributes to the advancement of soil quantitative remote sensing theory but also offers more precise data support for the development of green, low-carbon, and precision agriculture.