A Review of Hybrid Approaches for Quantitative Assessment of Crop Traits Using Optical Remote Sensing: Research Trends and Future Directions

Monitoring canopy SPAD based on UAV and multispectral imaging over fruit tree growth stages and species

Chlorophyll monitoring is an important topic in phenotypic research. For fruit trees, chlorophyll content can reflect the real-time photosynthetic capacity, which is a great reference for nutrient status assessment. Traditional in situ estimation methods are labor- and time-consuming. Remote sensing spectral imagery has been widely applied in agricultural research. This study aims to explore a transferable model to estimate canopy SPAD across growth stages and tree species. Unmanned aerial vehicle (UAV) system was applied for multispectral images acquisition. The results showed that the univariate model yielded with Green Normalized Difference Vegetation Index (GNDVI) gave valuable prediction results, providing a simple and effective method for chlorophyll monitoring for single species. Reflection features (RF) and texture features (TF) were extracted for multivariate modeling. Gaussian Process Regression (GPR) models yielded better performance for mixed species research than other algorithm models, and the R 2 of the RF+TF+GPR model was approximately 0.7 in both single and mixed species. In addition, this method can also be used to predict canopy SPAD over various growth stages, especially in the third and fourth stages with R 2 higher than 0.6. This paper highlights the importance of using RF+TF for canopy feature expression and deep connection exploration between canopy features with GPR algorithm. This research provides a universal model for canopy SPAD inversion which can promote the growth status monitoring and management of fruit trees.

Accurate estimation of fractional vegetation cover for winter wheat by integrated unmanned aerial systems and satellite images

Accurate estimation of fractional vegetation cover (FVC) is essential for crop growth monitoring. Currently, satellite remote sensing monitoring remains one of the most effective methods for the estimation of crop FVC. However, due to the significant difference in scale between the coarse resolution of satellite images and the scale of measurable data on the ground, there are significant uncertainties and errors in estimating crop FVC. Here, we adopt a Strategy of Upscaling-Downscaling operations for unmanned aerial systems (UAS) and satellite data collected during 2 growing seasons of winter wheat, respectively, using backpropagation neural networks (BPNN) as support to fully bridge this scale gap using highly accurate the UAS-derived FVC (FVCUAS) to obtain wheat accurate FVC. Through validation with an independent dataset, the BPNN model predicted FVC with an RMSE of 0.059, which is 11.9% to 25.3% lower than commonly used Long Short-Term Memory (LSTM), Random Forest Regression (RFR), and traditional Normalized Difference Vegetation Index-based method (NDVI-based) models. Moreover, all those models achieved improved estimation accuracy with the Strategy of Upscaling-Downscaling, as compared to only upscaling UAS data. Our results demonstrate that: (1) establishing a nonlinear relationship between FVCUAS and satellite data enables accurate estimation of FVC over larger regions, with the strong support of machine learning capabilities. (2) Employing the Strategy of Upscaling-Downscaling is an effective strategy that can improve the accuracy of FVC estimation, in the collaborative use of UAS and satellite data, especially in the boundary area of the wheat field. This has significant implications for accurate FVC estimation for winter wheat, providing a reference for the estimation of other surface parameters and the collaborative application of multisource data.

Seasonal Mapping of Irrigated Winter Wheat Traits in Argentina with a Hybrid Retrieval Workflow Using Sentinel-2 Imagery

Earth observation offers an unprecedented opportunity to monitor intensively cultivated areas providing key support to assess fertilizer needs and crop water uptake. Routinely, vegetation traits mapping can help farmers to monitor plant development along the crop's phenological cycle, which is particularly relevant for irrigated agricultural areas. The high spatial and temporal resolution of the Sentinel-2 (S2) multispectral instrument leverages the possibility to estimate leaf area index (LAI), canopy chlorophyll content (CCC), and vegetation water content (VWC) from space. Therefore, our study presents a hybrid retrieval workflow combining a physically-based strategy with a machine learning regression algorithm, i.e., Gaussian processes regression, and an active learning technique to estimate LAI, CCC and VWC of irrigated winter wheat. The established hybrid models of the three traits were validated against in-situ data of a wheat campaign in the Bonaerense valley, South of the Buenos Aires Province, Argentina, in the year 2020. We obtained good to highly accurate validation results with LAI: R2 = 0.92, RMSE = 0.43 m2 m-2, CCC: R2 = 0.80, RMSE = 0.27 g m-2 and VWC: R2 = 0.75, RMSE = 416 g m-2. The retrieval models were also applied to a series of S2 images, producing time series along the seasonal cycle, which reflected the effects of fertilizer and irrigation on crop growth. The associated uncertainties along with the obtained maps underlined the robustness of the hybrid retrieval workflow. We conclude that processing S2 imagery with optimised hybrid models allows accurate space-based crop traits mapping over large irrigated areas and thus can support agricultural management decisions.