Improving the matching degree between remotely sensed phenological dates and physiological growing stages of soybean by a dynamic offset-adjustment strategy.

Crop phenology provides crucial information for determining the appropriate timing of farm management practices and predicting crop yields. Satellite remote sensing has become a burgeoning tool for rapid phenological monitoring over wide spatial regions. However, there are significant timing gaps between the satellite-based phenological feature points and ground-observed physiological growing stages of the target. In this study, a dynamic offset-adjustment strategy that aims to improve the matching degree of the above two is proposed and tested with soybean across 16 states in the United States. A series of remotely sensed phenological transition dates that are characteristics of key growing stages of soybean were retrieved using MODIS time series data over the period 2000-2020 and the offset adjustments to the dates were identified by dynamically adjusting offset values till the minimum RMSE between the remote sensing-based and the ground-observed dates of physiological growing stages were obtained. The results indicated that the offset-adjustment strategy can significantly improve the alignments between remotely sensed phenological dates and field-based physiological growing stages of soybean in contrast to these without taking adjustment, with the average RMSE dropping by 58.58 %, 51.59 %, 31.15 %, 25.33 %, 24.67 % in the downturn, peak of season (POS), upturn, stabilization and recession dates, respectively. Among tested remotely sensed characteristics, the end of season (EOS) dates show the greatest alignment with its corresponding physiological growing stage, i.e., the dropping leaves stage. Comparison of the performance of the upturn date and start of season (SOS) in monitoring the date of the emerged stage indicates that the later one exhibits a better consistency with the ground-observed emerged stage after taking the adjustment, with the average RMSE dropping by 56.52 %. The proposed offset-adjustment strategy offers an approach for adjusting remotely sensed characteristics so to make them more consistent with the ground-observed crop physiological growing stages.

Modeling soybean cultivation suitability in China and its future trends in climate change scenarios.

Soybean is an important source of oil and vegetable protein and plays a key role in agricultural production and economy. A suitability evaluation of soybean cultivation is important for identifying potential soybean planting areas. Based on the raster data of soybean harvest ratio (FSHA) and climate-soil-topography-socio-economy environmental factors, we used MaxEnt to simulate the soybean planting suitability and potential distribution in China and the future trends of soybean cultivation under climate change. Three shared socio-economic paths (SSPs) that set up in the future climate section were considered, including SSP126 (sustainable path), SSP245 (intermediate path), and SSP585 (fossil fuel dominated development path). The result shows that the suitability of soybean cultivation was primarily influenced by elevation, precipitation of warmest quarter, capacity of the clay fraction, slope, portion of primary industry, topsoil gravel content, mean diurnal temperature range and accumulated temperature ≥10 °C. High-suitability and moderate-suitability area are respectively 26.51 Mha and 41.93 Mha in China. High-suitability areas for soybean are mainly concentrated in the Northeast Plain, the North China Plain and the northern parts of the middle and lower Yangtze River plain. There were many provinces with high soybean planting potential but low development degrees, including Hebei, Henan, Shandong, Tianjin, Jilin, Liaoning, Jiangsu, Hubei and Shaanxi. From 2021 to 2060, the total area highly and moderately suitable for soybean cultivation is projected to increase first and then decrease under both SSP126 and SSP245 scenarios. However, it shows a continued upward trend under SSP585, the rising part accounting for more than 10% in the base of historical data. Specifically, under SSP585, the suitability grade in most parts of Northeast China (eastern Inner Mongolia, northern Heilongjiang and western Jilin and Liaoning) will have a general promotion, opposite to the result under SSP126. Moreover, parts of southwest China (Yunnan, Chongqing, northern Guizhou and eastern Sichuan) may be more suitable for soybean cultivation in both scenarios. This study provides a practical reference for current and future soybean planting layout and relative countermeasures.

Non-destructive monitoring of amylose content in rice by UAV-based hyperspectral images.

Amylose content (AC) is an important indicator for rice quality grading. The rapid development of unmanned aerial vehicle (UAV) technology provides rich spectral and spatial information on observed objects, making non-destructive monitoring of crop quality possible. To test the potential of UAV-based hyperspectral images in AC estimation, in this study, observations on five rice cultivars were carried out in eastern China (Zhejiang province) for four consecutive years (from 2017 to 2020). The correlations between spectral and textural variables of UAV-based hyperspectral images at different growth stages (booting, heading, filling, and ripening) and AC (%) were analyzed, and the linear regression models based on spectral variables alone, textural variables alone, and combined spectral and textural variables were established. The results showed that the sensitive bands (P< 0.001) to AC were mainly centered in the green (536∽568 nm) and red regions (630∽660nm), with spectral and textural variables at the ripening stage giving the highest negative correlation coefficient of -0.868 and -0.824, respectively. Models based on combined spectral and textural variables give better estimation than those based on spectral or textural variables alone, characterized by less variables and higher accuracy. The best models using spectral or textural variables alone both involved three growth stages (heading, filling, and ripening), with root mean square error (RMSE) of 1.01% and 1.04%, respectively, while the models based on combined spectral and textural variables have RMSE of 1.04% 0.844% with only one (ripening stage) or two (ripening and filling stages) growth stages involved. The combination of spectral and textural variables of UAV-based hyperspectral images is expected to simplify data acquisition and enhance estimation accuracy in remote sensing of rice AC.

Analysis of spatial-temporal variation in NPP based on hydrothermal conditions in the Lancang-Mekong River Basin from 2000 to 2014.

Against the background of global climate change, spatial-temporal variation in net primary productivity (NPP) has attracted much attention. To analyze NPP spatial-temporal variation within the context of changes in hydrothermal conditions, the Vegetation Photosynthesis Model (VPM) is used to elucidate the mathematical relationship between NPP and hydrothermal conditions. Based on this spatial-temporal pattern of NPP and hydrothermal conditions in the Lancang-Mekong River Basin, regression statistics, an empirical model of land evaporation, and the water and thermal product index (K) are used to evaluate correlations between NPP and hydrothermal conditions in terms of their distribution pattern and interaction. The results show the following. (1) From 2000 to 2014, NPP in the Lancang-Mekong River Basin was highest in the central region and gradually decreased toward the southwest and northwest, whereas the annual change rate in NPP showed no significant increasing trend. (2) In the Lancang Basin, the correlation between hydrothermal conditions and NPP was high with respect to their distribution patterns, though this correlation was low in the Mekong Basin. (3) Correlation between K and NPP is high in the region where the effects of water and thermal factors on vegetation growth are similar. (4) K is an effective complement to the correlation between a single hydrothermal factor (temperature or precipitation) and NPP, and the influence of hydrothermal conditions on NPP was positive in the Lancang River and negative in the Mekong River Basin. Our study quantitatively analyzes the spatial-temporal correlation between NPP and hydrothermal conditions. The findings can reflect the vegetation change tendency and provide scientific data for ecological environment development and protection in the study area.

DNA repair mechanisms in embryonic stem cells.

Embryonic stem cells (ESCs) can undergo unlimited self-renewal and retain the pluripotency to differentiate into all cell types in the body. Therefore, as a renewable source of various functional cells in the human body, ESCs hold great promise for human cell therapy. During the rapid proliferation of ESCs in culture, DNA damage, such as DNA double-stranded breaks, will occur in ESCs. Therefore, to realize the potential of ESCs in human cell therapy, it is critical to understand the mechanisms how ESCs activate DNA damage response and DNA repair to maintain genomic stability, which is a prerequisite for their use in human therapy. In this context, it has been shown that ESCs harbor much fewer spontaneous mutations than somatic cells. Consistent with the finding that ESCs are genetically more stable than somatic cells, recent studies have indicated that ESCs can mount more robust DNA damage responses and DNA repair than somatic cells to ensure their genomic integrity.

[Discussion on hyperspectral index for the estimation of cotton canopy water content].

Proper vegetation indices have decisive influences on the precision of hyperspectral estimation models for surface parameters. In the present paper, in order to find the proper hyperspectral indices for cotton canopy water content estimation, two water parameters for cotton canopy water content (EWT(canopy), equivalent water thickness; VWC, vegetation water content) and corresponding hyperspectra data were analyzed. A rigorous search procedure was used to determine the best index predictors of cotton canopy water. In the procedure, all possible ratio indices and normalized difference indices were derived from the canopy hyperspectra, involving all the two-band combinations between 350 nm and 2500 nm. Then the correlation between two water parameters and all combination indices were analyzed, and the best indices which produced maximum correlation coefficients were determined. Finally, the indices were compared with the published water indices for their performances in estimation of cotton canopy water content. The results showed that for the estimation of EWT(canopy), the new developed ratio index R1 475/R1 424 and normalized difference index (R1 475 -R1 424)/(R1 475 + R1 424) was the most proper one, and the correlation coefficient of the estimated and measured EWT(canopy) reached 0.849. For the estimation of VWC, the performance of published index was better than new developed index, the best suitable water indices for VWC estimation were (R835 - R1 650)/(R835 + R1 650), and the correlation coefficient of the estimated and measured VWC was 0.849.

Quantifying biochemical variables of corn by hyperspectral reflectance at leaf scale.

To further develop the methods to remotely sense the biochemical content of plant canopies, we report the results of an experiment to estimate the concentrations of three biochemical variables of corn, i.e., nitrogen (N), crude fat (EE) and crude fiber (CF) concentrations, by spectral reflectance and the first derivative reflectance at fresh leaf scale. The correlations between spectral reflectance and the first derivative transformation and three biochemical variables were analyzed, and a set of estimation models were established using curve-fitting analyses. Coefficient of determination (R2), root mean square error (RMSE) and relative error of prediction (REP) of estimation models were calculated for the model quality evaluations, and the possible optimum estimation models of three biochemical variables were proposed, with R2 being 0.891, 0.698 and 0.480 for the estimation models of N, EE and CF concentrations, respectively. The results also indicate that using the first derivative reflectance was better than using raw spectral reflectance for all three biochemical variables estimation, and that the first derivative reflectances at 759 nm, 1954 nm and 2370 nm were most suitable to develop the estimation models of N, EE and CF concentrations, respectively. In addition, the high correlation coefficients of the theoretical and the measured biochemical parameters were obtained, especially for nitrogen (r=0.948).

Monitoring rice nitrogen status using hyperspectral reflectance and artificial neural network.

Over use of nitrogen fertilization can result in groundwater pollution. Tools that can rapidly quantify the nitrogen status are needed for efficient fertilizer management and would be very helpful in reducing the environmental pollution caused by excessive nitrogen application. Remote sensing has a proven ability to provide spatial and temporal measurements of surface properties. In this study, the MLR (multiple linear regression) and ANN (artificial neural network) modeling methods were applied to the monitoring of rice N (nitrogen concentration, mg nitrogen g(-1) leaf dry weight) status using leaf level hyperspectral reflectance with two different input variables, and as a result four estimation models were proposed. RMSE (root-mean-square error), REP (relative error of prediction), R2 (coefficient of determination), as well as the intercept and slope between the observed and predicted N were used to test the performance of models. Very good agreements between the observed and the predicted N were obtained with all proposed models, which was especially true for the R-ANN (artificial neural network based on reflectance selected using MLR) model. Compared to the other three models, the R-ANN model improved the results by lowering the RMSE by 14.2%, 32.1%, and 31.5% for the R-LR (linear regression based on reflectance) model, PC-LR (linear regression based on principal components scores) model, and PC-ANN (artificial neural network based on principal components scores) model, respectively. It was concluded that the ANN algorithm may provide a useful exploratory and predictive tool when applied on hyperspectral reflectance data for nitrogen status monitoring. Besides, although the performance of MLR was superior to PCA used for ANN inputs selection, the encouraging results of PC-based models indicated the promising potential of ANN combined with PCA application on hyperspectral reflectance analysis.