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.