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mRNA-based therapeutics have emerged as a promising strategy for cancer treatment. However, the effective delivery of mRNA into hard-to-transfect cancer cells remains a significant challenge. This study introduces a novel approach that utilizes iron oxide nanoparticles (NPs) synthesized through a layer-by-layer (LbL) method for safe and efficient mRNA delivery. The developed NPs consist of an iron oxide core modified with a thin charge-bearing layer, an mRNA middle layer, and an outer layer composed of perfluorinated polyethyleneimine with heparin (PPH), which facilitates efficient mRNA delivery. Through a comparative analysis of four nanoparticle delivery formulations, we investigated the effects of the iron oxide core's surface chemistry and surface charge on mRNA complexation, cellular uptake, and mRNA release. We identified an optimal and effective mRNA delivery platform, namely, (IOCCP)-mRNA-PPH, capable of transporting mRNA into various hard-to-transfect cancer cell lines in vitro. The (IOCCP)-mRNA-PPH formulation demonstrated significant enhancements in cellular internalization of mRNA, facilitated endosomal escape, enabled easy mRNA release, and exhibited minimal cytotoxicity. These findings suggest that (IOCCP)-mRNA-PPH holds great promise as a solution for mRNA therapy against hard-to-transfect cancers. [ABSTRACT FROM AUTHOR]

Accurate and timely crop yield prediction over large spatial regions is critical to national food security and sustainable agricultural development. However, designing a robust model for crop yield prediction over a large spatial region remains challenging due to inadequate surveyed samples and an under-development of deep-learning frameworks. To tackle this issue, we integrated multi-source (remote sensing, weather, and soil properties) data into a dual-stream deep-learning neural network model for winter wheat in China's major planting regions. The model consists of two branches for robust feature learning: one for sequential data (remote sensing and weather series data) and the other for statical data (soil properties). The extracted features by both branches were aggregated through an adaptive fusion model to forecast the final wheat yield. We trained and tested the model by using official county-level statistics of historical winter wheat yields. The model achieved an average R2 of 0.79 and a root-mean-square error of 650.21 kg/ha, superior to the compared methods and outperforming traditional machine-learning methods. The dual-stream deep-learning neural network model provided decent in-season yield prediction, with an error of about 13% compared to official statistics about two months before harvest. By effectively extracting and aggregating features from multi-source datasets, the new approach provides a practical approach to predicting winter wheat yields at the county scale over large spatial regions. [ABSTRACT FROM AUTHOR]

The gamma-ray irradiation-induced degradation of poly(ether sulfone) (PES) in a homogeneous system has been studied. By dissolving the polymer in different organic solvents, the degradation effect of gamma-ray irradiation on PES increases significantly except for dimethyl sulphoxide (DMSO). This phenomenon is supported by the experimental results of GPC. The enhancement effect of a homogeneous system is explained by increased gamma-ray irradiation energy harvesting capacity due to the presence of organic solvent molecules. [ABSTRACT FROM AUTHOR]

Grassland aboveground biomass is crucial for evaluating grassland desertification, degradation, and grassland and livestock balance. Given the lack of understanding of mechanical processes and limited simulation accuracy for grassland aboveground biomass estimation, especially at the regional scale, this study investigates a new method combining remote sensing data assimilation technology and a grassland process-based model to estimate regional grassland biomass, focusing on improving the simulation accuracy by modeling and revealing the mechanism interpretability of grassland growth processes. Xilinhot City of Inner Mongolia was used as the study area. The ModVege model was selected as the grass dynamic simulation model. A likelihood function was constructed composed of the LAI, grassland aboveground biomass, and daily measurements wherein the accumulated temperature reached ST2 (the temperature sum defining the end of reproductive growth). Then, the Markov chain Monte Carlo (MCMC) methodology was adapted to calibrate the ModVege model by maximizing the likelihood function. The time-series LAI from MOD15A3H was assimilated into the ModVege model, and the model parameters ST2 and BMGV0 (initial biomass and green vegetative tissues, respectively) were optimized at a 500 m pixel scale based on the four-dimensional variational method (4DVar) method. Compared with August 15th, the RMSE and MAPE of aboveground biomass were 242 kg/ha and 10%, respectively, after calibration. Data assimilation improved this accuracy, with the RMSE decreasing to 214 kg/ha. Overall, the aboveground grassland biomass of Xilinhot City shows spatial distribution patterns of high value in the northeast and low value in the central and southeast areas. Generally, the method implemented in this study provides an important reference for the aboveground biomass estimation of regional grassland. [ABSTRACT FROM AUTHOR]

Breast cancer has attracted tremendous research interest in treatment development as one of the major threats to public health. The use of non-viral carriers for therapeutic DNA delivery has shown promise in treating various cancer types, including breast cancer, due to their high DNA loading capacity, high cell transfection efficiency, and design versatility. However, cytotoxicity and large sizes of non-viral DNA carriers often raise safety concerns and hinder their applications in the clinic. Here we report the development of a novel nanoparticle formulation (termed NP-Chi-xPEI) that can safely and effectively deliver DNA into breast cancer cells for successful transfection. The nanoparticle is composed of an iron oxide core coated with low molecular weight (800 Da) polyethyleneimine crosslinked with chitosan via biodegradable disulfide bonds. The NP-Chi-xPEI can condense DNA into a small nanoparticle with the overall size of less than 100 nm and offer full DNA protection. Its biodegradable coating of small-molecular weight xPEI and mildly positive surface charge confer extra biocompatibility. NP-Chi-xPEI-mediated DNA delivery was shown to achieve high transfection efficiency across multiple breast cancer cell lines with significantly lower cytotoxicity as compared to the commercial transfection agent Lipofectamine 3000. With demonstrated favorable physicochemical properties and functionality, NP-Chi-xPEI may serve as a reliable vehicle to deliver DNA to breast cancer cells. [ABSTRACT FROM AUTHOR]

Agricultural drought threatens food security. Numerous remote-sensing drought indices have been developed, but their different principles, assumptions and physical quantities make it necessary to compare their suitability for drought monitoring over large areas. Here, we analyzed the performance of three typical remote sensing-based drought indices for monitoring agricultural drought in two major agricultural production regions in Shaanxi and Henan provinces, northern China (predominantly rain-fed and irrigated agriculture, respectively): vegetation health index (VHI), temperature vegetation dryness index (TVDI) and drought severity index (DSI). We compared the agreement between these indices and the standardized precipitation index (SPI), soil moisture, winter wheat yield and National Meteorological Drought Monitoring (NMDM) maps. On average, DSI outperformed the other indices, with stronger correlations with SPI and soil moisture. DSI also corresponded better with soil moisture and NMDM maps. The jointing and grain-filling stages of winter wheat are more sensitive to water stress, indicating that winter wheat required more water during these stages. Moreover, the correlations between the drought indices and SPI, soil moisture, and winter wheat yield were generally stronger in Shaanxi province than in Henan province, suggesting that remote-sensing drought indices provide more accurate predictions of the impacts of drought in predominantly rain-fed agricultural areas. [ABSTRACT FROM AUTHOR]

Accurate, timely, and fine-resolution crop phenology is essential for determining the optimal timing of agronomic management practices supporting precision agriculture and food security. Synthetic Aperture Radar (SAR) methods, unaffected by cloud occlusion, have been widely applied in monitoring maize phenology. Nonetheless, their reliance on manual threshold settings, which depend on the user's expertise, limits their applicability. Furthermore, the neglect of SAR's potential for monitoring other phenological periods (e.g., seven-leaves date (V7), jointing date (JD), tassel date (TD), and milky date (MID)) hinders their robustness, particularly for regional-scale applications. To address these issues, this study used an adaptive dynamic threshold to evaluate the ability of the Sentinel-1 cross-polarization ratio (CR) in detecting the three-leaves date (V3), V7, JD, TD, MID, and maturity date (MD) of maize. We analyzed the effect of incidence angle, precipitation, and wind speed on Sentinel-1 features to identify the optimal feature for time series fitting. Then, we employed linear regression to determine the optimal threshold and developed an adaptive dynamic threshold for phenology detection. This approach effectively mitigated the speckle noise of Sentinel-1 and minimized artificial interference caused by customary conventional thresholds. Finally, we mapped phenology across 8.3 million ha in Heilongjiang Province. The results indicated that the approach has a higher ability to detect JD (RMSE = 11.10 d), MID (RMSE = 10.31 d), and MD (RMSE = 9.41 d) than that of V3 (RMSE = 32.07 d), V7 (RMSE = 56.37 d), and TD (RMSE = 43.33 d) in Sentinel-1. Compared with Sentinel-2, the average RMSE of JD, MID, and MD decreased by 4.14%, 35.28%, and 26.48%. Moreover, when compared to different thresholds, the adaptive dynamic threshold can quickly determine the optimal threshold for detecting each phenological stage. CR is least affected by incident angle, precipitation, and wind speed, effectively suppressing noise to reflect phenological development better. This approach supports the rapid and feasible mapping of maize phenology across broad spatial regions with a few samples. [ABSTRACT FROM AUTHOR]

Accurate and timely monitoring of flooded crop areas is crucial for disaster rescue and loss assessment. However, most flooded crop monitoring methods based on synthetic aperture radar (SAR) imagery were developed for rice, which is probably inappropriate for crops with complex canopy structures that strongly attenuate SAR signals. Additionally, these methods often rely on empirical thresholds and region-specific reference samples, limiting their reliability and applicability on a larger spatial scale. To address these issues, we developed a novel flooded crop mapping approach at a regional scale using Sentinel-1 time-series data and an unsupervised Gaussian Mixture Model (GMM). Our approach leverages a Flood Separability Index (FSI) derived from the fitted probability density function of flooded and non-flooded crop areas in a GMM. This allows us to overcome the limitations of manual input selection in previous studies. The multi-temporal GMM was constructed using the time-series images with optimal polarization to estimate the flooded crop extents on a regional scale. We also investigated the scattering mechanisms of three typical crop disaster structures within an agricultural landscape area. Our results indicate that the proposed multi-temporal GMM is robust in crop planting areas with complex canopy structures. The performance of both single-temporal and multi-temporal GMMs surpasses that of baseline methods such as Otsu and K-means. Compared with VV polarization, VH polarization exhibits greater potential for accurately mapping flooded crops in complex agricultural regions. Our approach does not require labeled samples or many predefined parameters, making it fast and feasible for mapping flooded crops with complex canopy structures in large spatial areas. • We developed an automatic approach to map flooded crop dynamics with GMM. • We analyzed the crop scattering mechanisms under complicated flooding situations. • We presented a novel Flooding Separability Index (FSI) based on GMM. • The VH polarization is potential to identify the flooded crops. [ABSTRACT FROM AUTHOR]

Highlights • We proposed an up-scaling method of assimilating reflectance into model. • Constraint of key input parameters for PROSAIL through WOFOST output. • A faster data-assimilation strategy for 30-m resolution was developed. • Assimilating up-scaling reflectance achieved the highest yield estimation. Abstract To estimate regional-scale winter wheat (Triticum aestivum) yield, we developed a data-assimilation scheme that assimilates remotely sensed reflectance into a coupled crop growth–radiative transfer model. We generated a time series of 8-day, 30-m-resolution synthetic Kalman Smoothed reflectance by combining MODIS surface reflectance products with Landsat surface reflectance using a KS algorithm. We evaluated the assimilation performance using datasets with different spatial and temporal scales (e.g., three dates for the 30-m Landsat reflectance, 8-day and 1-km MODIS surface reflectance, and 8-day and 30-m synthetic KS reflectance) into the coupled WOFOST–PROSAIL model. Then we constructed a four-dimensional variational data assimilation (4DVar) cost function to account for differences between the observed and simulated reflectance. We used the shuffled complex evolution–University of Arizona (SCE-UA) algorithm to minimize the 4DVar cost function and optimize important input parameters of the coupled model. The optimized parameters were used to drive WOFOST and estimate county-level winter wheat yield in a region of China. By assimilating the synthetic KS reflectance data, we achieved the most accurate yield estimates (R 2 = 0.44, 0.39, and 0.30; RMSE = 598, 1288, and 595 kg/ha for 2009, 2013, and 2014, respectively), followed by Landsat reflectance (R 2 = 0.21, 0.22, and 0.33; RMSE = 915, 1422, and 637 kg/ha for 2009, 2013, and 2014, respectively) and MODIS reflectance (R 2 = 0.49, 0.05, and 0.22; RMSE = 1136, 1468, and 700 kg/ha for 2009, 2013, and 2014, respectively) at the county level. Thus, our method improves the reliability of regional-scale crop yield estimates. [ABSTRACT FROM AUTHOR]

Polyethersulfone (PES) and monomer methacrylic acid (MAA) were chosen to synthesize the grafted polymer PES-g-PMAA by simultaneous γ-ray irradiation in homogenous phase. Membranes prepared from pristine PES and PES-g-PMAA with different degrees of grafting (DGs), have been obtained. The kinetics of grafting reaction and the DG values were studied through sulphur elemental analysis. The morphology study of the membranes showed formation of sponge-like structure in the sub-layer of membrane, along with grafting PMAA onto the PES chains. By ultrafiltration experiments, a regular pH-dependent permeability and thermal-dependent permeability of PES-g-PMAA membranes was demonstrated through introduction of PMAA onto the PES which reduced the water flux of the membranes. The pH-dependent permeability could be attributed to pH-sensitive reconfiguration of PMAA molecule chains on the surface of membrane pore, while thermal-dependent permeability might be caused by better thermal-sensibility of PMAA chains inside the membrane. [ABSTRACT FROM AUTHOR]

Systematic study of FTIR spectra was carried out on the PES/PVP blends and PES-g-PVP copolymers. In the blends, no shift of carbonyl group absorbance was observed. In the copolymers, there was an occurrence of blue-shift that increases with the grafting degree, which can be used as a compelling evidence of PVP being grafted onto PES. This phenomenon was interpreted from the stereo-hindrance effect. Besides, as the intensity of carbonyl group absorbance increased proportionally with the degree of grafting obtained by elementary analysis, FTIR, a simple and easy tool, became feasible for the quantitative determination of PVP content in the graft copolymers. Calibration curves with high correlation coefficient were established from which the quantitative relationship between the intensity of carbonyl group absorbance normalized by a chosen internal reference and the grafting degree can be obtained. [ABSTRACT FROM AUTHOR]

Crop growth models are powerful tools for predicting crop growth and yield. Gross primary production (GPP) is a major photosynthetic flux that is directly linked to crop grain yield. To better understand the potential of GPP for regional crop yield estimation, in this study, a novel crop data-model assimilation (CDMA) framework was proposed that assimilates accumulative GPP estimates from the satellite-based vegetation photosynthesis model (VPM) into the WOrld FOod STudies (WOFOST) model using the ensemble Kalman filter (EnKF) algorithm to estimate winter wheat GPP and grain yield. Results showed that the WOFOST simulated GPP agreed with the GPP EC derived from eddy flux tower (R2 = 0.74 and 0.47 in 2015 and 2016, respectively). Assimilating GPP VPM into the WOFOST model improved site-scale GPP estimation (R2 = 0.87 and 0.67 in 2015 and 2016, respectively), and also improved regional-scale winter wheat yield estimates (R2 = 0.36 and 0.29; RMSE= 479 and 572 kg/ha in 2015 and 2016, respectively) compared with the open loop simulations (R2 = 0.14 and 0.10; RMSE= 801 and 788 kg/ha in 2015 and 2016, respectively). Our study demonstrated that assimilation of remotely sensed GPP optimized the results of carbon simulation in the WOFOST model and highlighted the potential of GPP for regional winter wheat yield estimation using a data assimilation framework. • We developed a data-assimilation framework that assimilates remotely sensed GPP data into the WOFOST crop model. • The WOFOST-simulated GPP was strongly related to eddy flux tower simulated GPP. • Assimilating VPM-based GPP into the WOFOST model improved regional-scale winter wheat yield estimation. • A novel ensemble generation method sampled posterior WOFOST parameters to generate WOFOST simulated GPP ensembles. [ABSTRACT FROM AUTHOR]

The scale mismatch between remote sensing observations and state variables simulated by crop growth models decreases the reliability of crop yield estimates. To overcome this problem, we implemented a two-step data-assimilation approach: first, we generated a time series of 30-m-resolution leaf area index (LAI) by combining Moderate Resolution Imaging Spectroradiometer (MODIS) data and three Landsat TM images with a Kalman filter algorithm (the synthetic KF LAI series); second, the time series were assimilated into the WOFOST crop growth model to generate an ensemble Kalman filter LAI time series (the EnKF-assimilated LAI series). The synthetic EnKF LAI series then drove the WOFOST model to simulate winter wheat yields at 1-km resolution for pixels with wheat fractions of at least 50%. The county-level aggregated yield estimates were compared with official statistical yields. The synthetic KF LAI time series produced a more realistic characterization of LAI phenological dynamics. Assimilation of the synthetic KF LAI series produced more accurate estimates of regional winter wheat yield ( R 2 = 0.43; root-mean-square error (RMSE) = 439 kg ha −1 ) than three other approaches: WOFOST without assimilation (determination coefficient R 2 = 0.14; RMSE = 647 kg ha −1 ), assimilation of Landsat TM LAI ( R 2 = 0.37; RMSE = 472 kg ha −1 ), and assimilation of S-G filtered MODIS LAI ( R 2 = 0.49; RMSE = 1355 kg ha −1 ). Thus, assimilating the synthetic KF LAI series into the WOFOST model with the EnKF strategy provides a reliable and promising method for improving regional estimates of winter wheat yield. [ABSTRACT FROM AUTHOR]

To predict regional-scale winter wheat yield, we developed a crop model and data assimilation framework that assimilated leaf area index (LAI) derived from Landsat TM and MODIS data into the WOFOST crop growth model. We measured LAI during seven phenological phases in two agricultural cities in China’s Hebei Province. To reduce cloud contamination, we applied Savitzky–Golay (S–G) filtering to the MODIS LAI products to obtain a filtered LAI. We then regressed field-measured LAI on Landsat TM vegetation indices to derive multi-temporal TM LAIs. We developed a nonlinear method to adjust LAI by accounting for the scale mismatch between the remotely sensed data and the model’s state variables. The TM LAI and scale-adjusted LAI datasets were assimilated into the WOFOST model to allow evaluation of the yield estimation accuracy. We constructed a four-dimensional variational data assimilation (4DVar) cost function to account for the observations and model errors during key phenological stages. We used the shuffled complex evolution–University of Arizona algorithm to minimize the 4DVar cost function between the remotely sensed and modeled LAI and to optimize two important WOFOST parameters. Finally, we simulated winter wheat yield in a 1-km grid for cells with at least 50% of their area occupied by winter wheat using the optimized WOFOST, and aggregated the results at a regional scale. The scale adjustment substantially improved the accuracy of regional wheat yield predictions ( R 2 = 0.48; RMSE = 151.92 kg ha −1 ) compared with the unassimilated results ( R 2 = 0.23; RMSE = 373.6 kg ha −1 ) and the TM LAI results ( R 2 = 0.27; RMSE = 191.6 kg ha −1 ). Thus, the assimilation performance depends strongly on the LAI retrieval accuracy and the scaling correction. Our research provides a scheme to employ remotely sensed data, ground-measured data, and a crop growth model to improve regional crop yield estimates. [ABSTRACT FROM AUTHOR]

The enhancement of cultivated land quality is an important measure to improve the production capability and ensure the food safety. At the construction course of cultivated land quality informatization, novel methods and techniques are promising to improve the current working pattern as well as raise the efficiency of cultivated land quality management. Irrigation guarantee capability is an important element of cultivated land quality monitoring, evaluation and construction. Evapotranspiration (ET) derived from remote sensing can be used for rapid monitoring of irrigation guarantee capability of cultivated land at the regional scale. In order to improve the acquisition efficiency of the regional cultivated land quality monitoring, a new evaluation method was proposed for irrigation capability evaluation of cultivated land based on Moderate Resolution Imaging Spectroradiometer (MODIS) ET products. First, in order to reduce the representative errors of meteorological data, the relationship between the monthly potential evapotranspiration (PET) parameter derived from MODIS evaporation products and the reference crop evapotranspiration (ET0) calculated from observed meteorological data with the Penman-Monteith (P-M) formula was analyzed. The continuous ET0 in space was obtained from the regression model. And then, water requirements for crop were obtained by the map of crop type from the classification of Landsat TM data and space-continuous ET0. Furthermore, the annual water requirement was calculated according to the difference of the water requirement for crop and the rainfall which was effectively consumed by crop. Additionally, based on the regional water balance principle, the actual quantity of irrigation was obtained from the difference of actual evaporation derived from ET products of MODIS and effective rainfall. The evaluation index of irrigation guarantee capability is defined as the ratio of the annual irrigation requirement and the actual quantity of irrigation, and used for the monitoring and evaluation of cultivated land irrigation guarantee capability. Finally, application and analysis were conducted in Hengshui City, Hebei Province, China. The R2 of the regression model between the PET and ET0 from meteorological stations reached 0.89. And the calculated results of the evaluation index of irrigation guarantee capability from 2005 to 2012 accurately reflected temporal-spatial variations for the cultivated land irrigation capability. Meanwhile, the comprehensive evaluation of irrigation capability, which was from the supervised classification results based on actual survey, had a good agreement with the classification results of irrigation probability in the gradation on agriculture land quality which had been updated in 2012. The area with no grade difference was up to 37% of the total cells, 38% of the study area had one-grade difference, and the area with two-grade difference was 19% of the study area. The area with three-grade difference only accounted for 6%. The grade difference of more than 75% of the cultivated land was less than one grade. Errors were mainly concentrated in the cultivated land around the city, because the MOD16A2 product with 1-km resolution was inadequate to accurately represent the information of that complex planting structure of cultivated land, and this problem would be solved with the development and improvement of remote sensing earth observation systems with higher resolution. The evaluation index of irrigation capability proposed in this paper has the advantage of clear physical meaning and easy data accessing. The experiment results indicate that the method can meet the needs of the regional cultivated land quality monitoring and evaluation. [ABSTRACT FROM AUTHOR]

Assimilating biophysical parameters derived from remote sensing into crop growth model is an important approach to improve performance of regional crop yield estimation. Currently, most researches adopt single remote sensing data source and single variable assimilation strategy, which cannot accurately reflect the interactive process among radiation, temperature and water, limiting the performance of data assimilation model. Leaf area index (LAI) and evapotranspiration (ET) are two key biophysical variables related to crop growth and grain yield. The study presents an assimilation framework to assimilate MODIS LAI product (MCD15A3) and MODIS ET product (MOD16A2) into the Soil-Water-Atmosphere-Plant (SWAP) model to improve the estimates of winter wheat yield at the regional scale. The spatial scale is one of the most challenging issues in the field of remote sensing, and the mismatching between remote sensing observations and state variables of crop model has an important impact on the performance of data assimilation model. MODIS LAI and ET products in 1 km scale are severely underestimated compared to the ground-based observations because of the mixed pixel effect and the heterogeneity within pixel, and hence the scale factors of 1 km MODIS products and the crop model's simulated parameters are totally different. So the direct assimilation of 1 km MODIS products would cause abnormal results. At present, there are two types of solutions to mitigate the scale issue; one is to scale down remote sensing parameters or scale up crop model's simulated variables, and the other is to assimilate the time series trend characteristics derived from remote sensing into crop model. In this study, two types of cost functions were constructed through comparing the generalized vector angle or first order difference of the observations and modeled LAI and ET time series trends during the growing season. Two key model parameters (i.e. irrigation water depth and emergence date) were selected as the reinitialized parameters needed to be optimized through minimizing the cost function using the SCE-UA optimization algorithm, and then the optimized parameters were input into the SWAP model for winter wheat yield estimation. Winter wheat yield assimilation estimation accuracy was evaluated for two cost functions (e.g., vector angle and first order difference) at field and regional scales. The results showed that yield estimation accuracy had been greatly improved with assimilation of LAI and ET trends than without assimilation. Furthermore, vector angle strategy (r=0.75, RMSE=494 kg/hm2) had achieved higher accuracy than first order difference (r=0.73, RMSE=667 kg/hm2). In this study, equal weights were given to LAI and ET in the cost function. Giving different weights according to the errors of the LAI and ET data at different crop phenological stages would further improve the performance of data assimilation model. LAI and ET were selected as the assimilation variables in the cost function. Additional important state variables (e.g., soil moisture) that also closely related to grain yield should be incorporated into data assimilation framework to test the impacts to the crop yield. So, a more robust approach needs to be developed to simultaneously assimilate multiple biophysical variables (e.g., LAI, ET/PET, soil moisture), and hybrid approaches, such as combining the use of EnKF and 4DVar, would allow simultaneous estimates and updating of the model parameters and state variables, and would further improve crop yield estimation at field and regional scales. [ABSTRACT FROM AUTHOR]

Assimilating biophysical parameters derived from remote sensing into crop growth model is an important approach to improve performance of regional crop yield estimation. Currently, most researches adopt single remote sensing data source and single variable assimilation strategy, which cannot accurately reflect the interactive process among radiation, temperature and water, limiting the performance of data assimilation model. Leaf area index (LAI) and evapotranspiration (ET) are two key biophysical variables related to crop growth and grain yield. The study presents an assimilation framework to assimilate MODIS LAI product (MCD15A3) and MODIS ET product (MOD16A2) into the Soil-Water-Atmosphere-Plant (SWAP) model to improve the estimates of winter wheat yield at the regional scale. The spatial scale is one of the most challenging issues in the field of remote sensing, and the mismatching between remote sensing observations and state variables of crop model has an important impact on the performance of data assimilation model. MODIS LAI and ET products in 1 km scale are severely underestimated compared to the ground-based observations because of the mixed pixel effect and the heterogeneity within pixel, and hence the scale factors of 1 km MODIS products and the crop model's simulated parameters are totally different. So the direct assimilation of 1 km MODIS products would cause abnormal results. At present, there are two types of solutions to mitigate the scale issue; one is to scale down remote sensing parameters or scale up crop model's simulated variables, and the other is to assimilate the time series trend characteristics derived from remote sensing into crop model. In this study, two types of cost functions were constructed through comparing the generalized vector angle or first order difference of the observations and modeled LAI and ET time series trends during the growing season. Two key model parameters (i.e. irrigation water depth and emergence date) were selected as the reinitialized parameters needed to be optimized through minimizing the cost function using the SCE-UA optimization algorithm, and then the optimized parameters were input into the SWAP model for winter wheat yield estimation. Winter wheat yield assimilation estimation accuracy was evaluated for two cost functions (e.g., vector angle and first order difference) at field and regional scales. The results showed that yield estimation accuracy had been greatly improved with assimilation of LAI and ET trends than without assimilation. Furthermore, vector angle strategy (r=0.75, RMSE=494 kg/hm²) had achieved higher accuracy than first order difference (r=0.73, RMSE=667 kg/hm²). In this study, equal weights were given to LAI and ET in the cost function. Giving different weights according to the errors of the LAI and ET data at different crop phenological stages would further improve the performance of data assimilation model. LAI and ET were selected as the assimilation variables in the cost function. Additional important state variables (e.g., soil moisture) that also closely related to grain yield should be incorporated into data assimilation framework to test the impacts to the crop yield. So, a more robust approach needs to be developed to simultaneously assimilate multiple biophysical variables (e.g., LAI, ET/PET, soil moisture), and hybrid approaches, such as combining the use of EnKF and 4DVar, would allow simultaneous estimates and updating of the model parameters and state variables, and would further improve crop yield estimation at field and regional scales. [ABSTRACT FROM AUTHOR]

Breast cancer has attracted tremendous research interest in treatment development as one of the major threats to public health. The use of non-viral carriers for therapeutic DNA delivery has shown promise in treating various cancer types, including breast cancer, due to their high DNA loading capacity, high cell transfection efficiency, and design versatility. However, cytotoxicity and large sizes of non-viral DNA carriers often raise safety concerns and hinder their applications in the clinic. Here we report the development of a novel nanoparticle formulation (termed NP-Chi-xPEI) that can safely and effectively deliver DNA into breast cancer cells for successful transfection. The nanoparticle is composed of an iron oxide core coated with low molecular weight (800 Da) polyethyleneimine crosslinked with chitosan via biodegradable disulfide bonds. The NP-Chi-xPEI can condense DNA into a small nanoparticle with the overall size of less than 100 nm and offer full DNA protection. Its biodegradable coating of small-molecular weight xPEI and mildly positive surface charge confer extra biocompatibility. NP-Chi-xPEI-mediated DNA delivery was shown to achieve high transfection efficiency across multiple breast cancer cell lines with significantly lower cytotoxicity as compared to the commercial transfection agent Lipofectamine 3000. With demonstrated favorable physicochemical properties and functionality, NP-Chi-xPEI may serve as a reliable vehicle to deliver DNA to breast cancer cells. [ABSTRACT FROM AUTHOR]

In July 2021, a flooding event, which attracted the attention of the whole country and even the world, broke out in Henan, resulting in dramatic losses across multiple fields (e.g., economic and agricultural). The basin at the junction of Hebi, Xinxiang, and Anyang was the most affected region, as the spread of water from the Wei river submerged surrounding agricultural land (e.g., corn-dominated). To comprehensively evaluate the flooding impacts, we proposed a framework to detect the flooding area and evaluated the degree of loss using satellite time series data. First, we proposed a double-Gaussian model to adaptively determine the threshold for flooding extraction using Synthetic Aperture Radar (SAR) data. Then, we evaluated the disaster levels of flooding with field survey samples and optical satellite images. Finally, given that crops vary in their resilience to flooding, we measured the vegetation index change before and after the flooding event using satellite time series data. We found the proposed double-Gaussian model could accurately extract the flooding area, showing great potential to support in-time flooding evaluation. We also showed that the multispectral satellite images could potentially support the classification of disaster levels (i.e., normal, slight, moderate, and severe), with an overall accuracy of 88%. Although these crops were temporarily affected by this flooding event, most recovered soon, especially for the slightly and moderately affected regions. Overall, the distribution of resilience of these affected crops was basically in line with the results of classified disaster levels. The proposed framework provides a comprehensive aspect to the retrospective study of the flooding process on crops with diverse disaster levels and resilience. It can provide rapid and timely flood damage assessment and support emergency management and disaster verification work. [ABSTRACT FROM AUTHOR]

Abstract: Regional crop yield prediction is a significant component of national food security assessment and food policy making. The crop growth model based on field scale is limited when it is extrapolated to regional scale to estimate crop yield due to the uncertainty of the input parameters. The data assimilation method which combines crop growth model and remotely sensed data has been proven to be the most effective method in regional yield estimation. The methods based on cost function are powerless with crop dynamic growth simulation and state variable dynamic update. However, sequence assimilation method has more advantages to overcome these problems, this paper presents a method of assimilation of time series HJ-1 A/B Normalized Difference Vegetation Index (NDVI) into the coupled model (e.g. WOrld FOod STudies (WOFOST) crop growth model and A two layer Canopy Reflectance Model (ACRM) radiative transfer mode) for winter wheat yield estimates using Ensemble Kalman Filter (EnKF) at the regional scale. The WOFOST model was selected as the crop growth model and calibrated and validated by the field measured data in order to accurately simulate the state variables and the growing process of winter wheat. The theoretically optimal time series LAI profile was obtained with the EnKF algorithm to reduce the errors which existed in both time series HJ-1 CCD NDVI and WOFOST–ACRM model. Finally, the winter wheat yield at the county level was estimated based on the optimized WOFOST model running on the wheat planting pixel. The experiment illustrates that in the potential mode, the EnKF algorithm has significantly improved the regional winter wheat yield estimates ( , RMSE=775 kg/ha) over the WOFOST simulation without assimilation ( , RMSE=2168 kg/ha) at county level compared to the official statistical yield data. Meanwhile, in the water-limited mode the results showed a high correlation ( , RMSE=3005 kg/ha) with statistical data. In general, our results indicate that EnKF is a reliable optimization method for assimilating remotely sensed data into the crop growth model for predicting regional winter wheat yield. [Copyright &y& Elsevier]

Abstract: Crop growth models have been applied successfully in forecasting crop yield at a local scale, while satellite remote sensing has the advantage of retrieving regional crop parameters. The new assimilation method of integrating the crop growth model with remote sensing has presented great potential in regional crop yield assessment. In this study, the Moderate Resolution Imaging Spectrometer (MODIS) leaf area index (LAI) data product was assimilated into the World Food Studies (WOFOST) crop growth model. Using the Extended Fourier Amplitude Sensitivity Test (EFAST) global sensitivity analysis approach, several local and regional crop parameters were identified to be recalibrated. The Shuffled Complex Evolution (SCE) optimization algorithm was used to estimate the emergence date, initial biomass and initial available soil water by minimizing the differences between the corrected MODIS-LAI and simulated LAI. Results indicated that the accuracy of water-limited crop yield was improved significantly after the assimilation. The root mean square error (RMSE) reduced from 983 kg/ha to 474 kg/ha and 667 kg/ha respectively in two different optimization schemes. [Copyright &y& Elsevier]

Satellite-based models have tremendous potential for monitoring crop production because satellite data can provide temporally and spatially continuous crop growth information at large scale. This study used a satellite-based vegetation production model (i.e., eddy covariance light use efficiency, EC-LUE) to estimate national winter wheat gross primary production, and then combined this model with the harvest index (ratio of aboveground biomass to yield) to convert the estimated winter wheat production to yield. Specifically, considering the spatial differences of the harvest index, we used a cross-validation method to invert the harvest index of winter wheat among counties, municipalities and provinces. Using the field-surveyed and statistical yield data, we evaluated the model performance, and found the model could explain more than 50% of the spatial variations of the yield both in field-surveyed regions and most administrative units. Overall, the mean absolute percentage errors of the yield are less than 20% in most counties, municipalities and provinces, and the mean absolute percentage errors for the production of winter wheat at the national scale is 4.06%. This study demonstrates that a satellite-based model is an alternative method for crop yield estimation on a larger scale. [ABSTRACT FROM AUTHOR]

Timely and accurate regional crop-yield estimates are crucial for guiding agronomic practices and policies to improve food security. In this study, a crop-growth model was integrated with time series of remotely sensed data through deep learning (DL) methods to improve the accuracy of regional wheat-yield estimations in Henan Province, China. Firstly, the time series of moderate-resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) were input into the long short-term memory network (LSTM) model to identify the wheat-growing region, which was further used to estimate wheat areas at the municipal and county levels. Then, the leaf area index (LAI) and grain-yield time series simulated by the Crop Environment REsource Synthesis for Wheat (CERES-Wheat) model were used to train and evaluate the LSTM, one-dimensional convolutional neural network (1-D CNN) and random forest (RF) models, respectively. Finally, an exponential model of the relationship between the field-measured LAI and MODIS NDVI was applied to obtain the regional LAI, which was input into the trained LSTM, 1-D CNN and RF models to estimate wheat yields within the wheat-growing region. The results showed that the linear correlations between the estimated wheat areas and the statistical areas were significant at both the municipal and county levels. The LSTM model provided more accurate estimates of wheat yields, with higher R2 values and lower root mean square error (RMSE) and mean relative error (MRE) values than the 1-D CNN and RF models. The LSTM model has an inherent advantage in capturing phenological information contained in the time series of the MODIS-derived LAI, which is important for satellite-based crop-yield estimates. [ABSTRACT FROM AUTHOR]

In this article, the Kuusk–Nilson forest reflectance and transmittance (FRT) model was inverted to retrieve the overstorey and understorey leaf area index (OU-LAI) of forest stands in the Longmenhe forest nature reserve in China. Data from detailed sample sites were collected in 30 forest stands representing the typical vegetation community in the study area. An uncertainty and sensitivity matrix (USM) was used to analyse the sensitivity of the FRT model parameters based on these data. The results indicated that overstorey LAI strongly influenced stand reflectance, whereas understorey LAI had a much lower impact. To predict OU-LAI in forest stands, FRT model inversion is carried out by minimizing a merit function that provides a measure of the difference between the reflectance simulated by the FRT model and the reflectance originating from optimal band selection of Hyperion data. Various combinations of Hyperion bands were tested to evaluate the most effective wavelengths for the inversion of OU-LAI. The best estimates from 17 Hyperion bands (5 VIS, 8 NIR, 4 SWIR) by the FRT model inversion showed an R 2 = 0.41 and RMSE/mean = 0.21 for overstorey LAI and R 2 = 0.49 and RMSE/mean = 0.91 for understorey LAI. Advantages and disadvantages of FRT inversion for retrieval OU-LAI combined with Hyperion data are discussed. [ABSTRACT FROM AUTHOR]

Abstract: Nitrate nitrogen (NO3 −-N) from agricultural activities has become the main source of groundwater pollution. A spatial distribution pattern of groundwater NO3 −-N pollution is vital for agricultural ecological and environmental management. The objective of this paper is to investigate the potential of artificial neural network to explore the spatial distribution of groundwater NO3 −-N pollution in Shandong intensive farming regions of China. A detailed field campaign has been carried out to obtain the 216 sample site data focusing on the intensive farming regions in Shandong province. Considering the practical difficulty of the complex nonlinear relationship between multi-factors and groundwater nitrate, a Back Propagation Neural Network (BPNN) was developed for modeling groundwater NO3 −-N concentration. In order to perform the analysis, both natural and anthropogenic factors have been studied, such as soil characteristics, fertilizer usage and terrain factors and so on. Finally, soil organic matter content, total nitrogen content and nitrogen fertilizer data were chosen as input features of the BPNN for having the best correlation with groundwater NO3 −-N concentration. The results indicated that areas with higher NO3 −-N concentration in groundwater are mainly located in the region of excessive use of nitrogen fertilizer and low groundwater runoff modulus. The application results suggested that the BPNN provide a promising approach for analyzing the spatial variability of the groundwater NO3 −-N concentration. [Copyright &y& Elsevier]

Abstract: The objective of this research is to investigate the potential of application of China Environment Satellite HJ-1A/B in monitoring rice cultivation areas in Guangdong province in southern China. Information on the rice cultivation areas is of global economic and environmental significance. A CCD camera sensor with 30 m spatial resolution onboard China Environment Satellite HJ-1A and B has visible and near infrared bands and a revisit period of four days; the temporal Normalized Difference Vegetation Index (NDVI) can therefore be obtained from HJ-1A and B data. The characteristics of the temporal NDVI derived from HJ-1A and B images of rice fields and other crops at rice growth stages in the western part of Guangdong province of China with an area of about 67000 km2 were first analyzed in this research and an algorithm for mapping paddy rice fields was developed based on the temporal changes of NDVI of rice fields from January to July, 2009. The mapping result was evaluated by field survey and the data from China Ministry of Agriculture and the promising accuracy was found with a Kappa factor of 0.71. The result of this study suggests that the China Environment Satellite HJ-1A/B has great potential in the development of an operational system for monitoring rice crop growth in southern China. [Copyright &y& Elsevier]

Crop mapping using satellite imagery is crucial for agriculture applications. However, a fundamental challenge that hinders crop mapping progress is the scarcity of samples. The latest foundation model, Segment Anything Model (SAM), provides an opportunity to address this issue, yet few studies have been conducted in this area. This study investigated the parcel segmentation performance of SAM on commonly used medium-resolution satellite imagery (i.e., Sentinel-2 and Landsat-8) and proposed a novel automated sample generation framework based on SAM. The framework comprises three steps. First, an image optimization automatically selects high-quality images as the inputs for SAM. Then, potential samples are generated based on the masks produced by SAM. Finally, the potential samples are subsequently subjected to a sample cleaning procedure to acquire the most reliable samples. Experiments were conducted in Henan Province, China, and southern Ontario, Canada, using six proven effective classifiers. The effectiveness of our method is demonstrated through the combination of field-survey-collected samples and differently proportioned generated samples. Our results indicated that directly using SAM for parcel segmentation remains challenging, unless the parcels are large, regular in shape, and have distinct color differences from surroundings. Additionally, the proposed approach significantly improved the performance of classifiers and alleviated the sample scarcity problem. Compared to classifiers trained only by field-survey-collected samples, our method resulted in an average improvement of 16% and 78.5% in Henan and Ontario, respectively. The random forest achieved relatively good performance, with weighted-average F1 of 0.97 and 0.996 obtained using Sentinel-2 imagery in the two study areas, respectively. Our study contributes insights into solutions for sample scarcity in crop mapping and highlights the promising application of foundation models like SAM. [ABSTRACT FROM AUTHOR]

Crop phenology indicators provide essential information on crop growth phases, which are highly required for agroecosystem management and yield estimation. Previous crop phenology studies were mainly conducted using coarse-resolution (e.g., 500 m) satellite data, such as the moderate resolution imaging spectroradiometer (MODIS) data. However, precision agriculture requires higher resolution phenology information of crops for better agroecosystem management, and this requirement can be met by long-term and fine-resolution Landsat observations. In this study, we generated the first national maize phenology product with a fine spatial resolution (30 m) and a long temporal span (1985–2020) in China, using all available Landsat images on the Google Earth Engine (GEE) platform. First, we extracted long-term mean phenological indicators using the harmonic model, including the v3 (i.e., the date when the third leaf is fully expanded) and the maturity phases (i.e., when the dry weight of maize grains first reaches the maximum). Second, we identified the annual dynamics of phenological indicators by measuring the difference in dates when the vegetation index in a specific year reaches the same magnitude as its long-term mean. The derived maize phenology datasets are consistent with in situ observations from the agricultural meteorological stations and the PhenoCam network. Besides, the derived fine-resolution phenology dataset agrees well with the MODIS phenology product regarding the spatial patterns and temporal dynamics. Furthermore, we observed a noticeable difference in maize phenology temporal trends before and after 2000, which is likely attributable to the changes in temperature and precipitation, which further altered the farming activities. The extracted maize phenology dataset can support precise yield estimation and deepen our understanding of the future agroecosystem response to global warming. The data are available at 10.6084/m9.figshare.16437054 (Niu et al., 2021). [ABSTRACT FROM AUTHOR]

Predicting crop maturity dates is important for improving crop harvest planning and grain quality. The prediction of crop maturity dates by assimilating remote sensing information into crop growth model has not been fully explored. In this study, a data assimilation framework incorporating the leaf area index (LAI) product from Moderate Resolution Imaging Spectroradiometer (MODIS) into a World Food Studies (WOFOST) model was proposed to predict the maturity dates of winter wheat in Henan province, China. Minimization of normalized cost function was used to obtain the input parameters of the WOFOST model. The WOFOST model was run with the re-initialized parameter to forecast the maturity dates of winter wheat grid by grid, and THORPEX Interactive Grand Global Ensemble (TIGGE) was used as forecasting period weather input in the future 15 days (d) for the WOFOST model. The results demonstrated a promising regional maturity date prediction with determination coefficient (R2) of 0.94 and the root mean square error (RMSE) of 1.86 d. The outcomes also showed that the optimal forecasting starting time for Henan was 30 April, corresponding to a stage from anthesis to grain filling. Our study indicated great potential of using data assimilation approaches in winter wheat maturity date prediction. [ABSTRACT FROM AUTHOR]

A kind of hybrid membrane with self-luminescent property was prepared by mixing rare earth nanoparticles of nano-Sr 4 Al 2 O 4 : Eu2+, Dy3+ into the casting solution dissolved with polyvinylidene fluoride (PVDF), and then coprecipitated through non-solvent induced phase separation (NIPS) method. Membrane were characterized by luminescent intensity, scanning electron microscopy (SEM), atomic force microscopy (AFM), contact angle measurement, and the results showed that the membranes with the nano-Sr 4 Al 2 O 4 : Eu2+, Dy3+ content of 0.75 wt% have the highest luminescent intensity and the lowest contact angle, but further increasing the nanoparticle content will decrease the surface pore density and change the cross-sectional structure. Meanwhile, the membrane permeability and the rejection rate proved that the addition of nano-Sr 4 Al 2 O 4 : Eu2+, Dy3+ effectively improved the flux of water and Bovine Serum Albumin/Humic Acid (BSA/HA) aqueous solution through the membrane. This hybrid membrane possessed the performance of real-time response to membrane pollution/cleaning, and finally an effectively improvement of anti-pollution property. • Self-luminescent PVDF membrane fabricated by hybriding with rare earth nanoparticles. • Real time detection of membrane fouling and membrane cleaning efficiency. • Improving anti-fouling property of PVDF membrane by rare earth nanoparticles. [ABSTRACT FROM AUTHOR]

Timely and accurate forecasting of crop yields is crucial to food security and sustainable development in the agricultural sector. However, winter wheat yield estimation and forecasting on a regional scale still remains challenging. In this study, we established a two-branch deep learning model to predict winter wheat yield in the main producing regions of China at the county level. The first branch of the model was constructed based on the Long Short-Term Memory (LSTM) networks with inputs from meteorological and remote sensing data. Another branch was constructed using Convolution Neural Networks (CNN) to model static soil features. The model was then trained using the detrended statistical yield data during 1982 to 2015 and evaluated by leave-one-year-out-validation. The evaluation results showed a promising performance of the model with the overall R 2 and RMSE of 0.77 and 721 kg/ha, respectively. We further conducted yield prediction and uncertainty analysis based on the two-branch model and obtained the forecast accuracy in one month prior to harvest of 0.75 and 732 kg/ha. Results also showed that while yield detrending could potentially introduce higher uncertainty, it had the advantage of improving the model performance in yield prediction. [ABSTRACT FROM AUTHOR]

• Provides strategies of choice of remote sensing-crop model data assimilation methods. • Focuses on the key issues to improving the performance of data assimilation. • Future research directions of remote sensing-crop model data assimilation are suggested. Timely monitoring of crop lands is important in order to make agricultural activities more sustainable, as well as ensuring food security. The use of Earth Observation (EO) data allows crop monitoring at a range of spatial scales, but can be hampered by limitations in the data. Crop growth modelling, on the other hand, can be used to simulate the physiological processes that result in crop development. Data assimilation (DA) provides a way of blending the monitoring properties of EO data with the predictive and explanatory abilities of crop growth models. In this paper, we first provide a critique of both the advantages and disadvantages of both EO data and crop growth models. We use this to introduce a solid and robust framework for DA, where different DA methods are shown to be derived from taking different assumptions in solving for the a posteriori probability density function (pdf) using Bayes' rule. This treatment allows us to provide some recommendation on the choice of DA method for particular applications. We comment on current computational challenges in scaling DA applications to large spatial scales. Future areas of research are sketched, with an emphasis on DA as an enabler for blending different observations, as well as facilitating different approaches to crop growth models. We have illustrated this review with a large number of examples from the literature. [ABSTRACT FROM AUTHOR]

Crop phenology information provides essential information on crop growth phases, which are highly required for agroecosystem management and yield estimation. Previous crop phenology studies were mainly conducted using coarse-resolution (e.g., 500 m) satellite data, such as the moderate resolution imaging spectroradiometer (MODIS) data. However, precision agriculture requires higher resolution phenology information of crops for better agroecosystem management, and this requirement can be met by long-term and fine-resolution Landsat observations. In this study, we generated the first national maize phenology product with a fine spatial resolution (30 m) and a long temporal span (1985-2020) in China, using all available Landsat images on the Google Earth Engine (GEE) platform. First, we extracted long-term mean phenological indicators using the harmonic model, including the v3 (i.e., the date when the third leaf is fully expanded) and the maturity phases (i.e., when the dry weight of maize grains first reaches the maximum). Second, we identified the annual dynamics of phenological indicators by measuring the difference of dates when the vegetation index in a specific year reaches the same magnitude as its long-term mean. The derived maize phenology datasets agree with in-situ observations from the agricultural meteorological stations and the PhenoCam network. Besides, the derived fine-resolution phenology dataset agrees well with the MODIS phenology product regarding their spatial patterns and temporal dynamics. We observed a noticeable difference in maize phenology temporal trends before and after 2000, which is likely attributable to the change of temperature and precipitation, which further alter the farming activities. The extracted maize phenology dataset can support precise yield estimation and deepen our understanding of the response of agroecosystem to global warming in the future. The data are available at https://doi.org/10.6084/m9.figshare.16437054 (Niu et al., 2021). [ABSTRACT FROM AUTHOR]

Crop yield estimation at a regional scale over a long period of time is of great significance to food security. In past decades, the integration of remote sensing observations and crop growth models has been recognized as a promising approach for crop growth monitoring and yield estimation. Optical remote sensing data are susceptible to cloud and rain, while synthetic aperture radar (SAR) can penetrate through clouds and has all-weather capabilities. This allows for more reliable and consistent crop monitoring and yield estimation in terms of radar sensor data. The aim of this study is to improve the accuracy for winter wheat yield estimation by assimilating time series soil moisture images, which are retrieved by a water cloud model using SAR and optical data as input, into the crop model. In this study, SAR images were acquired by C-band SAR sensors boarded on Sentinel-1 satellites and optical images were obtained from a Sentinel-2 multi-spectral instrument (MSI) for Hengshui city of Hebei province in China. Remote sensing data and ground data were all collected during the main growing season of winter wheat. Both the normalized difference vegetation index (NDVI), derived from Sentinel-2, and backscattering coefficients and polarimetric indicators, computed from Sentinel-1, were used in the water cloud model to derive time series soil moisture (SM) images. To improve the prediction of crop yields at the field scale, we incorporated remotely sensed soil moisture into the World Food Studies (WOFOST) model using the Ensemble Kalman Filter (EnKF) algorithm. In general, the trend of soil moisture inversion was consistent with the ground measurements, with the coefficient of determination (R2) equal to 0.45, 0.53, and 0.49, respectively, and RMSE was 9.16%, 7.43%, and 8.53%, respectively, for three observation dates. The winter wheat yield estimation results showed that the assimilation of remotely sensed soil moisture improved the correlation of observed and simulated yields (R2 = 0.35; RMSE =934 kg/ha) compared to the situation without data assimilation (R2 = 0.21; RMSE = 1330 kg/ha). Consequently, the results of this study demonstrated the potential and usefulness of assimilating SM retrieved from both Sentinel-1 C-band SAR and Sentinel-2 MSI optical remote sensing data into WOFOST model for winter wheat yield estimation and could also provide a reference for crop yield estimation with data assimilation for other crop types. [ABSTRACT FROM AUTHOR]

Leaf angle is a critical structural parameter for retrieving canopy leaf area index (LAI) using the PROSAIL model. However, the traditional method using default leaf angle distribution in the PROSAIL model does not capture the phenological dynamics of canopy growth. This study presents a LAI retrieval method for corn canopies using PROSAIL model with leaf angle distribution functions referred from terrestrial laser scanning points at four phenological stages during the growing season. Specifically, four inferred maximum-probability leaf angles were used in the Campbell ellipsoid leaf angle distribution function of PROSAIL. A Lookup table (LUT) is generated by running the PROSAIL model with inferred leaf angles, and the cost function is minimized to retrieve LAI. The results show that the leaf angle distribution functions are different for the corn plants at different phenological growing stages, and the incorporation of derived specific corn leaf angle distribution functions distribute the improvement of LAI retrieval using the PROSAIL model. This validation is done using in-situ LAI measurements and MODIS LAI in Baoding City, Hebei Province, China, and compared with the LAI retrieved using default leaf angle distribution function at the same time. The root-mean-square error (RMSE) between the retrieved LAI on 4 September 2014, using the modified PROSAIL model and the in-situ measured LAI was 0.31 m2/m2, with a strong and significant correlation (R2 = 0.82, residual range = 0 to 0.6 m2/m2, p < 0.001). Comparatively, the accuracy of LAI retrieved results using default leaf angle distribution is lower, the RMSE of which is 0.56 with R2 = 0.76 and residual range = 0 to 1.0 m2/m2, p < 0.001. This validation reveals that the introduction of inferred leaf angle distributions from TLS data points can improve the LAI retrieval accuracy using the PROSAIL model. Moreover, the comparations of LAI retrieval results on 10 July, 26 July, 19 August and 4 September with default and inferred corn leaf angle distribution functions are all compared with MODIS LAI products in the whole study area. This validation reveals that improvement exists in a wide spatial range and temporal range. All the comparisons demonstrate the potential of the modified PROSAIL model for retrieving corn canopy LAI from Landsat imagery by inferring leaf orientation from terrestrial laser scanning data. [ABSTRACT FROM AUTHOR]

Human activities have placed significant pressure on the terrestrial biosphere, leading to ecosystem degradation and carbon losses. However, the full impact of these activities on terrestrial biomass carbon remains unexplored. In this study, we examined changes in global human footprint (HFP) and human‐induced aboveground biomass carbon (AGBC) losses from 2000 to 2018. Our findings show an increasing trend in HFP globally, resulting in the conversion of wilderness areas to highly modified regions. These changes have altered global biomes' habitats, particularly in tropical and subtropical regions. We also found accelerated AGBC loss driven by HFP expansion, with a total loss of 19.99 ± 0.196 PgC from 2000 to 2018, especially in tropical regions. Additionally, AGBC is more vulnerable in the Global South than in the Global North. Human activities threaten natural habitats, resulting in increasing AGBC loss even in strictly protected areas. Therefore, scientifically guided planning of future human activities is crucial to protect half of Earth through mitigation and adaptation under future risks of climate change and global urbanization. [ABSTRACT FROM AUTHOR]

The continuous dynamic of surface urban heat island (SUHI) effect is highly needed in urban climate studies. However, temporal variations of SUHI intensity (SUHII) have been understudied in previous studies owing to the lack of spatially seamless and temporally continuous land surface temperature (LST) data, particularly in urban domains. Also, the relationship between SUHII variations and heterogeneous landscapes, reflecting various thermal and biophysical properties, has not been comprehensively explored globally. Here, we investigated the annual dynamic of nighttime SUHII and the inter- (i.e., city level) and intra-city (i.e., local climate zones level) SUHII variations of 1,028 worldwide cities, using the annual temperature cycle (ATC) model and seamless daily LST data and local climate zones (LCZ) data. The results reveal that the annual mean SUHII (SUHII M) in urban areas is 0.69 °C for nighttime with an increasing trend towards high latitudes, while the annual SUHII amplitude (SUHII A) is 0.32 °C and is relatively stable along latitudes. Global SUHII M and SUHII A indicate a robust pattern that decreases with decreasing buildings' height (i.e., from LCZ1 to LCZ3 and from LCZ4 to LCZ6) and increasing openness of building layout (i.e., from compact to open types). In contrast, the annual pattern of LST is mainly regulated by the background climate. The phase shift (i.e., day of year when the LST reaches its maximum value) of annual SUHII shows a distinct time lag effect along the LCZ gradient in cold climate zone. These findings are valuable for scientific planning of resilient cities and can mitigate the impact of heat islands on urban residents by optimizing the composition and configuration of urban landscapes, e.g., changing the height and morphology of buildings. [ABSTRACT FROM AUTHOR]

The dynamics of built-up height are a crucial aspect of urban form, enabling the characterization of urban growth patterns and the attainment of sustainable development goals. While past studies have focused on urban extent mapping, little has been done to reveal changes in vertical structures in built-up areas. In this study, we reconstructed annual urban form dynamics in Beijing, China, from 1990 to 2020, utilizing Landsat time-series data. First, we identified urban growth and renewal years using a temporal segmentation approach. Then, we reconstructed the annual dynamics of built-up heights through logical reasoning, using the reference height in 2020 and the identified changing years. We evaluated the derived dataset and analyzed the urban growth patterns (i.e., upward and outward) over the past three decades. The detected change years are reliable, with an overall accuracy of 88% (± 1 year). Meanwhile, the reconstructed built-up height data showed good agreement with surveyed results (R2: 0.92), with a root mean square error of 2.87 m and a mean absolute error of 2.07 m. Most expanded built-up areas during 1990–2020 were located at the fringe of the central city, and about 16% experiencing multiple changes. Overall, outward growth was prominent in Beijing during the first decade, while upward growth became dominant in most districts thereafter. The proposed framework provides a new way to reveal annual urban form dynamics through time series analysis, with great potential to support relevant studies such as urban climate modeling and carbon emissions studies. [ABSTRACT FROM AUTHOR]

The information of global spatially explicit urban extents under scenarios is important to mitigate future environmental risks caused by global urbanization and climate change. Although future dynamics of urban extent were commonly modeled with conversion from non-urban to urban extent using cellular-automata (CA)-based models, gradual changes of impervious surface area (ISA) at the pixel level were limitedly explored in previous studies. In this paper, we developed a global dataset of urban fractional changes at a 1 km resolution from 2020 to 2100 (5-year interval), under eight scenarios of socioeconomic pathways and climate change. First, to quantify the gradual change of ISA within the pixel, we characterized ISA growth patterns over the past decades (i.e., 1985–2015) using a sigmoid growth model and annual global artificial impervious area (GAIA) data. Then, by incorporating the ISA-based growth mechanism with the CA model, we calibrated the state-specific urban CA model with quantitative evaluation at the global scale. Finally, we projected future urban fractional changes at 1 km resolution under eight development pathways based on the harmonized urban growth demand from Land Use Harmonization2 (LUH2). The evaluation results show that the ISA-based urban CA model performs well globally, with an overall R2 of 0.9 and a root mean square error (RMSE) of 0.08 between modeled and observed ISAs in 2015. With the inclusion of temporal contexts of urban sprawl gained from GAIA, the dataset of global urban fractional change shows good agreement with 30-year historical observations from satellites. The dataset can capture spatially explicit variations of ISA and gradual ISA change within pixels. The dataset of global urban fractional change is of great use in supporting quantitative analysis of urbanization-induced ecological and environmental change at a fine scale, such as urban heat islands, energy consumption, and human–nature interactions in the urban system. The developed dataset of global urban fractional change is available at 10.6084/m9.figshare.20391117.v4 (He et al., 2022). [ABSTRACT FROM AUTHOR]

Paddy rice is the second-largest grain crop in China and plays an important role in ensuring global food security. However, there is no high-resolution map of rice covering all of China. This study developed a new rice-mapping method by combining optical and synthetic aperture radar (SAR) images in cloudy areas based on the time-weighted dynamic time warping (TWDTW) method and produced distribution maps of single-season rice in 21 provincial administrative regions of China from 2017 to 2022 at a 10 or 20 m resolution. The accuracy was examined using 108 195 survey samples and county-level statistical data. On average, the user's, producer's, and overall accuracy values over all investigated provincial administrative regions were 73.08 %, 82.81 %, and 85.23 %, respectively. Compared with the statistical data from 2017 to 2019, the distribution maps explained 83 % of the spatial variation of county-level planting areas on average. The distribution maps can be obtained at 10.57760/sciencedb.06963 (Shen et al., 2023). [ABSTRACT FROM AUTHOR]

It remains challenging to estimate a crop’s leaf area profile accurately and automatically. Terrestrial laser scanning (TLS) offers a potential solution, since it has a small footprint and can be used to rapidly and accurately resolve the canopy’s 3D structure. To improve the accuracy of vertical leaf area profile estimation, we selected point clouds from three corn plants scanned in two TLS experiments and used an improved voxel-based leaf area profile estimation method based on Wilson’s contact-frequency theory. The improvement has three aspects: First, it separates corn leaf points from stalk points using the difference-of-normals (DoN) method, which is commonly used to separate different surfaces of manmade objects such as tables or walls. The DoN method captures the geometrical difference between corn leaves and stalks. Second, it determines the optimal voxel size based on the calculated contact frequency. Third, it applies Wilson’s voxel-based contact-frequency method to the LiDAR point clouds. Comparing the estimated and measured leaf area profiles showed that separating leaf and stalk points improved the leaf area accuracy, which depended on the voxel size. The optimal voxel sizes were 10 cm × 10 cm × 10 cm and 5 cm × 5 cm × 5 cm, respectively, in the two study years based on irradiation of the corn plant with a single laser beam. The estimated vertical and cumulative leaf area profiles agreed well with the measured profiles; mean absolute errors for the samples ranged from 15.7 to 18.9%. [ABSTRACT FROM AUTHOR]

Crop residue cover is vital for reducing soil erosion and improving soil fertility, which is an important way of conserving tillage to protect the black soil in Northeast China. How much the crop residue covers on cropland is of significance for black soil protection. Landsat-8 and Sentinel-2 images were used to estimate corn residue coverage (CRC) in Northeast China in this study. The estimation model of CRC was established for improving CRC estimation accuracy by the optimal combination of spectral indices and textural features, based on soil texture zoning, using the random forest regression method. Our results revealed that (1) the optimization C5 of spectral indices and textural features improves the CRC estimation accuracy after harvesting and before sowing with determination coefficients (R2) of 0.78 and 0.73, respectively; (2) the random forest improves the CRC estimation accuracy after harvesting and before sowing with R2 of 0.81 and 0.77, respectively; (3) considering the spatial heterogeneity of the soil background and the usage of soil texture zoning models increase the accuracy of CRC estimation after harvesting and before sowing with R2 of 0.84 and 0.81, respectively. In general, the CRC estimation accuracy after harvesting was better than that before sowing. The results revealed that the corn residue coverage in most of the study area was 0.3 to 0.6 and was mainly distributed in the Songnen Plain. By the estimated corn residue coverage results, the implementation of conservation tillage practices is identified, which is vital for protecting the black soil in Northeast China. [ABSTRACT FROM AUTHOR]

Crop-type mapping is the foundation of grain security and digital agricultural management. Accuracy, efficiency and large-scale scene consistency are required to perform crop classification from remote sensing images. Many current remote-sensing crop extraction methods based on deep learning cannot account for adaptation effects in large-scale, complex scenes. Therefore, this study proposes a novel adaptive feature-fusion network for crop classification using single-temporal Sentinel-2 images. The selective patch module implemented in the network can adaptively integrate the features of different patch sizes to assess complex scenes better. TabNet was used simultaneously to extract spectral information from the center pixels of the patches. Multitask learning was used to supervise the extraction process to improve the weight of the spectral characteristics while mitigating the negative impact of a small sample size. In the network, superpixel optimization was applied to post-process the classification results to improve the crop edges. By conducting the crop classification of peanut, rice, and corn based on Sentinel-2 images in 2022 in Henan Province, China, the novel method proposed in this paper was more accurate, indicated by an F1 score of 96.53%, than other mainstream methods. This indicates our model's potential for application in crop classification in large scenes. [ABSTRACT FROM AUTHOR]

The crop model data assimilation approach has been acknowledged as an effective tool for monitoring crop growth and estimating yield. However, the choice of assimilated variables and the mismatch in scale between remotely sensed observations and crop model-simulated state variables have various effects on the performance of yield estimation. This study aims to examine the accuracy of crop yield estimation through the joint assimilation of leaf area index (LAI) and soil moisture (SM) and to examine the scale effect between remotely sensed data and crop model simulations. To address these issues, we proposed an improved crop data-model assimilation (CDMA) framework, which integrates LAI and SM, as retrieved from remotely sensed data, into the World Food Studies (WOFOST) model using the ensemble Kalman filter (EnKF) approach for winter wheat yield estimation. The results showed that the yield estimation at a 10 m grid size outperformed that at a 500 m grid size, using the same assimilation strategy. Additionally, the winter wheat yield estimation accuracy was higher when using the bivariate data assimilation method (R2 = 0.46, RMSE = 756 kg/ha) compared to the univariate method. In conclusion, our study highlights the advantages of joint assimilating LAI and SM for crop yield estimation and emphasizes the importance of finer spatial resolution in remotely sensed observations for crop yield estimation using the CDMA framework. The proposed approach would help to develop a high-accuracy crop yield monitoring system using optical and SAR retrieved parameters. [ABSTRACT FROM AUTHOR]

Efficient, fast, and accurate crop lodging monitoring is urgent for farmers, agronomists, insurance loss adjusters, and policymakers. This study aims to explore the potential of Chinese GF-1 PMS high-spatial-resolution images for corn lodging monitoring and to find a robust and efficient way to identify corn lodging accurately and efficiently. Three groups of image features and five machine-learning approaches are used for classifying non-lodged, moderately lodged, and severely lodged areas. Our results reveal that (1) the combination of spectral bands, optimized vegetation indexes, and texture features classify corn lodging with an overall accuracy of 93.81% and a Kappa coefficient of 0.91. (2) The random forest is an efficient, robust, and easy classifier to identify corn lodging with the F1-score of 0.95, 0.92, and 0.95 for non-lodged, moderately lodged, and severely lodged areas, respectively. (3) The GF-1 PMS image has great potential for identifying corn lodging on a regional scale. [ABSTRACT FROM AUTHOR]

Sun-induced chlorophyll fluorescence (SIF) has shown potential in quantifying plant responses to environmental changes by which abiotic drivers are dominated. However, SIF is a mixed signal influenced by factors such as leaf physiology, canopy structure, and sun-sensor geometry. Whether the physiological information contained in SIF can better quantify crop disease stresses dominated by biological drivers, and clearly explain the physiological variability of stressed crops, has not yet been sufficiently explored. On this basis, we took winter wheat naturally infected with stripe rust as the research object and conducted a study on the responses of physiological signals and reflectivity spectrum signals to crop disease stress dominated by biological drivers, based on in situ canopy-scale and leaf-scale data. Physiological signals include SIF, SIFyield (normalized by absorbed photosynthetically active radiation), fluorescence yield (ΦF) retrieved by NIRvP (non-physiological components of canopy SIF) and relative fluorescence yield (ΦF-r) retrieved by near-infrared radiance of vegetation (NIRvR). Reflectance spectrum signals include normalized difference vegetation index (NDVI) and near-infrared reflectance of vegetation (NIRv). At the canopy scale, six signals reached extremely significant correlations (P < 0.001) with disease severity levels (SL) under comprehensive experimental conditions (SL without dividing the experimental samples) and light disease conditions (SL < 20%). The strongest correlation between NDVI and SL (R = 0.69) was observed under the comprehensive experimental conditions, followed by NIRv (R = 0.56), ΦF-r (R = 0.53) and SIF (R = 0.51), and the response of ΦF (R = 0.45) and SIFyield (R = 0.34) to SL was weak. Under lightly diseased conditions, ΦF-r (R = 0.62) showed the strongest response to disease, followed by SIFyield (R = 0.60), SIF (R = 0.56) and NIRv (R = 0.54). The weakest correlation was observed between ΦF and SL (R = 0.51), which also showed a result approximating NDVI (R = 0.52). In the case of a high level of crop disease severity, NDVI showed advantages in disease monitoring. In the early stage of crop diseases, which we pay more attention to, compared with SIF and reflectivity spectrum signals, ΦF-r estimated by the newly proposed 'NIRvR approach' (which uses SIF together with NIRvR (i.e., SIF/ NIRvR) as a substitute for ΦF) showed superior ability to monitor crop physiological stress, and was more sensitive to plant physiological variation. At the leaf scale, the response of SIF to SL was stronger than that of NDVI. These results validate the potential of ΦF-r estimated by the NIRvR approach to monitoring disease stress dominated by biological drivers, thus providing a new research avenue for quantifying crop responses to disease stress. [ABSTRACT FROM AUTHOR]

Lodging is one of the typical abiotic adversities during paddy rice growth. In addition to affecting photosynthesis, it can seriously damage crop growth and development, such as reducing rice quality and hindering automated harvesting. It is, therefore, imperative to accurately and in good time acquire crop-lodging areas for yield prediction, agricultural insurance claims, and disaster-management decisions. However, the accuracy requirements for crop-lodging monitoring remain challenging due to complicated impact factors. Aiming at identifying paddy rice lodging on Shazai Island, Guangdong, China, caused by heavy rainfall and strong wind, a decision-tree model was constructed using multiple-parameter information from Sentinel-1 SAR images and the in situ lodging samples. The model innovatively combined the five backscattering coefficients with five polarization decomposition parameters and quantified the importance of each parameter feature. It was found that the decision-tree method coupled with polarization decomposition can be used to obtain an accurate distribution of paddy rice-lodging areas. The results showed that: (1) Radar parameters can capture the changes in lodged paddy rice. The radar parameters that best distinguish paddy rice lodging are VV, VV+VH, VH/VV, and Span. (2) Span is the parameter with the strongest feature importance, which shows the necessity of adding polarization parameters to the classification model. (3) The dual-polarized Sentinel-1 database classification model can effectively extract the area of lodging paddy rice with an overall accuracy of 84.38%, and a total area precision of 93.18%. These observations can guide the future use of SAR-based information for crop-lodging assessment and post-disaster management. [ABSTRACT FROM AUTHOR]

Although several products of the global urban extent with fine resolutions (e.g. 30 m-38 m) have been developed, quantitative evaluations of these products across spaces and times are still missing, which is crucial to future urban growth modeling. Here, we analysed the discrepancy of six global fine resolution urban extent products across spaces and times. First, we measured the area variations of urban extent among these urban products in each 0.25° grid used in the Land-Use Harmonisation (LUH2), a commonly used product with future projections in Earth system modelling. Then, we analysed the potential urban growth within each 0.25° grid using the LUH2 data under eight scenarios shaped by shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs). Finally, we identified those regions with a large discrepancy and noticeable growth of urban extent under historic and SSP-RCPs scenarios. We found the discrepancy among six products occurs in either highly developed (e.g. the United States-US and Europe) or rapidly developing (e.g. China) regions. Moreover, Eastern US, Europe, and West Africa deserve more attention in the future due to their distinct urban growth and relatively large discrepancy of urban areas. The derived results are crucial to future global urban sprawl modeling. [ABSTRACT FROM AUTHOR]

Smallholder agriculture is the bedrock of the food production system in sub-Saharan Africa. Yields in Africa are significantly below potentially attainable yields for a number of reasons, and they are particularly vulnerable to climate change impacts. Monitoring of these highly heterogeneous landscapes is needed to respond to farmer needs, develop an appropriate policy and ensure food security, and Earth observation (EO) must be part of these efforts, but there is a lack of ground data for developing and testing EO methods in western Africa, and in this paper, we present data on (i) crop locations, (ii) biophysical parameters and (iii) crop yield, and biomass was collected in 2020 and 2021 in Ghana and is reported in this paper. In 2020, crop type was surveyed in more than 1800 fields in three different agroecological zones across Ghana (the Guinea Savannah, Transition and Deciduous zones). In 2021, a smaller number of fields were surveyed in the Guinea Savannah zone, and additionally, repeated measurements of leaf area index (LAI) and leaf chlorophyll concentration were made on a set of 56 maize fields. Yield and biomass were also sampled at harvesting. LAI in the sampled fields ranged from 0.1 to 5.24 m 2 m -2 , whereas leaf chlorophyll concentration varied between 6.1 and 60.3 µ g cm -2. Yield varied between 190 and 4580 kg ha -1 , with an important within-field variability (average per-field standard deviation 381 kg ha -1). The data are used in this paper to (i) evaluate the Digital Earth Africa 2019 cropland masks, where 61 % of sampled 2020/21 cropland is flagged as cropland by the data set, (ii) develop and test an LAI retrieval method from Earth observation Planet surface reflectance data (validation correlation coefficient R=0.49 , root mean square error (RMSE) 0.44 m 2 m -2), (iii) create a maize classification data set for Ghana for 2021 (overall accuracy within the region tested: 0.84), and (iv) explore the relationship between maximum LAI and crop yield using a linear model (correlation coefficient R=0.66 and R=0.53 for in situ and Planet-derived LAI, respectively). The data set, made available here within the context of the Group on Earth Observations Global Agricultural Monitoring (GEOGLAM) initiative, is an important contribution to understanding crop evolution and distribution in smallholder farming systems and will be useful for researchers developing/validating methods to monitor these systems using Earth observation data. The data described in this paper are available from https://doi.org/10.5281/zenodo.6632083. [ABSTRACT FROM AUTHOR]

Mapping the corn dynamics at a large scale and multiple years is essential for global food security. Traditional mapping approaches by collecting training samples from field surveys are labor-intensive, challenging large-scale mapping of corn dynamics over the long term. This study developed an efficient approach to map large-scale corn dynamics in the main corn districts of the United States (US) using adaptive strategies for collecting high-quality training samples. First, this study proposed an automatic approach to collect stable and representative corn samples from the crop data layers (CDL) product. Then, this study improved the mapping performance of corn at a large scale and in earlier years by using adaptive strategies to collect limited (less than 500) but high-quality training samples. Finally, this study assessed and discussed the model performance across spaces and years using multi-source datasets from 2001 to 2020. Our results indicate the proposed approach with adaptive strategies can generate robust classification models with good performance in mapping large-scale corn dynamics. In our study area, the mean overall accuracy (OA) of corn is about 88% if using the CDL data as a reference. Besides, the R2 of corn areas at the county scale between our result and the surveyed acreage statistics is above 0.9, suggesting the proposed strategy is helpful for mapping corn dynamics across different years. The proposed approach demonstrated that limited but representative samples could map corn dynamics at a large scale with good performance, showing significant improvement compared to the traditional approach. It is feasible to map crop dynamics with multiple types by combining existing crop products with collected field samples, particularly in China, where crop products at the national scale are still lacking. [ABSTRACT FROM AUTHOR]