Nutrient elements are crucial for the growth, yield, and quality of rapeseed. Rapid and non-destructive monitoring of canopy nutrient status in rapeseed is of great significance for precise nutrient diagnosis and growth assessment. Although spectral remote sensing technology has become an efficient alternative to traditional laboratory methods, conventional approaches often struggle to effectively and finely extract specific feature information when faced with the complex canopy spectral environment of rapeseed, limiting the accuracy of synchronous monitoring for multiple nutrient elements.

By exploring the hyperspectral response mechanism of nutrient elements in rapeseed, this study aimed to construct quantitative estimation models for six nutrient elements in rapeseed leaves, namely boron (B), nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and sulfur (S), and improve their estimation accuracy, while analyzing the impact of different modeling methods on nutrient estimation, thereby providing a reference for precision nutrient management.

Based on hyperspectral data acquired during key growth stages of rapeseed and corresponding leaf nutrient concentrations, this study employed fractional-order differentiation (FOD) to enhance spectral feature signals and systematically compared the estimation accuracy of leaf nutrient contents using three machine learning algorithms: partial least squares regression (PLSR), support vector regression (SVR), and random forest regression (RFR).

Compared with the original spectral models, the models based on FOD spectra showed a 13%-30% increase in R², with the smallest performance difference between the training and test sets (average ΔR² =0.09). In particular, high-order FOD (>1.0) effectively highlighted subtle features like the red-edge slope and eliminated baseline drift, making the R² of the optimal models for N and B reach 0.89 and 0.87, respectively. Among the three algorithms, RFR exhibited the most robust performance (test-set R² of for different nutrients ranging from 0.48 to 0.89), with its selected sensitive bands (e.g., protein and chlorophyll absorption regions) closely align with crop physiological mechanisms. Spatial mapping revealed heterogeneous distribution characteristics of nutrients, confirming the model's ability to interpret field micro-environments.

This study proposed and validated a coupled methodological framework of FOD-RFR, which was capable of effectively deciphering subtle spectral features of multiple nutrients throughout the entire growth cycle of rapeseed, enabling non-destructive simultaneous estimation with significantly improved accuracy. This framework not only achieved differentiated collaborative estimation of multiple nutrients in rapeseed but also provided valuable insights and references for the remote sensing monitoring of complex biochemical parameters in field crops.

To address the uncertainty in the extraction and dynamic monitoring of cropland cropping intensity (CI) caused by frequent cloud cover, fragmented farmland, and multi-cropping systems in southern China, this study aimed to fully leverage the advantages of multi-scale remote sensing observations to achieve efficient and accurate CI mapping for Hubei Province from 2000 to 2021, and to analyze the spatiotemporal evolution of regional agricultural production patterns.

Time-series NDVI data from 250 m MODIS and 30 m Landsat were integrated using four representative spatiotemporal fusion algorithms: STARFM, ESTARFM, STNLFFM, and GF-SG. Fusion performance was comprehensively evaluated based on spectral fidelity (AD, RMSE) and spatial detail accuracy (Edge, LBP). The optimal algorithm was used to generate a 30 m/8-day NDVI dataset for 2000-2021. Cropland CI was extracted using a phenology-based peak detection method, and then its spatiotemporal variation was analyzed.

Compared with the other three spatiotemporal fusion algorithms, the GF-SG algorithm demonstrated the best performance in both spectral fidelity and spatial detail accuracy (|AD|<0.021, RMSE<0.111; |Edge|<0.55, |LBP|<0.10). The reconstructed NDVI time series using this algorithm improved the accuracy of cropland CI extraction by 0.02%-5.53%. Based on ground samples, the overall classification accuracy of cropland CI in Hubei Province reached 86.60%. From 2000 to 2021, approximately 20%-25% of croplands in the study area experienced CI transitions every five years, with the most significant changes occurring between 2005-2010 (25.79%) and the least between 2010-2015 (20.07%). The dominant transition type shifted from 'single-cropping to double-cropping' (13.49%) in the early years to 'double-cropping to single-cropping' (9.35%) and 'single-cropping to fallow' (4.90%) in the later years.

Over the past two decades, Hubei Province has developed a diversified cultivation pattern dominated by single cropping, with coexistence of double cropping and fallow practices. The evolution of cropland CI has been jointly driven by policy guidance, labor force changes, resource input, and adjustments in cropping structure. By integrating multi-scale remote sensing data from MODIS and Landsat, this study constructed a high spatiotemporal resolution NDVI dataset, which enabled efficient and accurate extraction of long-term cropland CI in complex agricultural landscapes. The findings offered the critical support for agricultural production management and the development of cropland protection policies.

Due to the abilities of all-time and all-weather data acquisition, the microwave remote sensing holds great potentials to identify rice in regions dominated by cloudy and rainy weather. The aim of this study was to analyze the consistency of classification results for single and double cropping rice by using optical and SAR remote sensing data, and then to explore the optimal SAR imagery features for rice classification.

In this study, using the object-based random forest classifier on the Google Earth Engine platform, Sentinel-1 and Sentinel-2 images were adopted to extract the single and double cropping rice from four typical rice growing areas in the Dongting Lake Plain. To analyze the optimal SAR features for the single and double cropping rice identification and the consistency of classification results based on Sentinel-1 and Sentinel-2 images, nine scenarios were established by the combination of different sensors and features and compared the performances of different scenarios. Furthermore, the R² and DTW distance between the NDVI time series and the SAR backscatter coefficient time series (VH, VH/VV) were calculated, respectively.

The overall accuracy of single and double rice cropping identification by using VH, VV and VH/VV time series was 90.42%, 82.08% and 88.33%, respectively. Moreover, the combination of VH and VH/VV time series could achieve a better performance (91.67%) for mapping single and double cropping rice. The derived R² and DTW distance between VH (VH/VV, VV) time series and NDVI time series were 0.870 (0.915, 0.986) and 4.715 (1.896, 5.506) for single cropping rice, as well as 0.597 (0.783, 0.673) and 2.396 (1.839, 3.441) for double cropping rice, respectively. Higher R² and lower DTW distance suggested that VH/VV time series, like NDVI, was more sensitive to the rice growth cycle. Furthermore, the flooding signals in rice transplanting phase could be well captured by VH time series. Additionally, the overall accuracy of single and double cropping rice classification based on optical and SAR features (S-2: NDVI, EVI, LSWI; S-1: VH, VH/VV) in six time windows was 91.25% and 90.00%, respectively, and their consistency was high, with the area correlation of 95.70%.

There was high consistency of classification results for single and double cropping rice based on optical and SAR imagery. Thus, Sentinel-1 imagery held great potentials to identify rice area in cloudy and rainy regions. Specifically, VH and VH/VV backscatter coefficient were optimal features for mapping rice. This study provided vital technical supports for feature optimization by using SAR imagery in cloudy and rainy regions to identify single and double cropping rice accurately.

The decametric-resolution (≤30 m) image is an important data source to identify crop types in South China dominated by fragmented croplands and complex cropping patterns. Due to the relative long revisit frequency of decametric-resolution sensors and persistent rainy/cloud weather in South China, it is critical to integrate multi-source decametric-resolution images using the temporal composition method for the generation of spatiotemporal continuous crop type map. Due to the different temporal resolutions of different satellites, and the significant differences in phenological quaternal rhythms of various crop types, selecting the optimal temporal composition window for integrating multi-source images is vital to map crop type distribution accurately.

This study aims to explore the impact of image temporal composition windows on crop type identification, and to provide significant references for large-scale crop type mapping in regions with complex terrain.

In this study, Landat-8 and Sentinel-2 data were integrated to extract the crop type distribution in the Jianghan Plain, Hubei Province, characterized by the various crop types and cloudy and rainy weather. Then, seven scenarios (15, 20, 25, 30, 40, 50, and 60 d) were established to analyze the effect of different temporal composition windows on crop type identification. Specifically, three aspects, including image coverage rate, spectral-temporal feature curves for different crops and classification accuracies, were combined to understand the performances of different scenarios comprehensively.

The crop type mapping using 20-day composition window performed the best, with the overall accuracy (OA) of 93.13%. In contrast, the scenarios that used narrower temporal composition window derived lower accuracy of crop type identification (e.g., OA=90.91% for the 15-day composition window), which can be primarily attributed to the low coverage rate of good observations in the study area. Meanwhile, since time series images composited in the wide window blurred the key phenological information for different crops, the classification accuracy of crop type mapping scenarios using wide temporal interval was also lower (e.g., OA=86.06% for the 60-day composition window). Additionally, the effect of temporal interval on different crops classification was ranked as following: other crops>rapeseed>rice>wheat>rice-crayfish. In detail, the reason why the classification performance of other crops was the most sensitive to the temporal composition window can be due to the high intra-class phenological variance of this type. Flowering period is the key phenology window to identify rapeseed, therefore, the classification accuracy of rapeseed decreased while the temporal composition window exceeds 30-day, and rapeseed was easily confused with wheat. Furthermore, because the key phenology window to distinguish rice-crayfish from single-cropping rice (i.e., the flooding stage of rice-crayfish fields) lasted a long period (e.g., from October 2020 to June 2021), the classification accuracy of rice-crayfish was less sensitive to the temporal composition window.

In general, the 20-day impact of the temporal composition window can take into account the high-quality image coverage and capture of key phenological characteristics of crop identification, but the optimal temporal composition window of different crops identification is affected by the key phenological period of crops. This study provides theoretical reference and method support for selecting the optimal temporal composition window to generate multi-source image time series, which is promising to improve the efficiency and accuracy of large-scale crop type mapping.