Challenges and opportunities in remote sensing-based crop monitoring: a review

Validation and refinement of cropland map in southwestern China by harnessing ten contemporary datasets

Accurate cropland map serves as the cornerstone of effective agricultural monitoring. Despite the continuous enrichment of remotely sensed cropland maps, pervasive inconsistencies have impeded their further application. This issue is particularly evident in areas with limited valid observations, such as southwestern China, which is characterized by its complex topography and fragmented parcels. In this study, we constructed multi-sourced samples independent of the data producers, taking advantage of open-source validation datasets and sampling to rectify the accuracy of ten contemporary cropland maps in southwestern China, decoded their inconsistencies, and generated a refined cropland map (CroplandSyn) by leveraging ten state-of-the-art remotely sensed cropland maps released from 2021 onwards using the self-adaptive threshold method. Validations, conducted at both prefecture and county scales, underscored the superiority of the refined cropland map, aligning more closely with national land survey data. The refined cropland map and samples are publicly available to users. Our study offers valuable insights for improving agricultural practices and land management in under-monitored areas by providing high-quality cropland maps and validation datasets.

Impacts of mining on vegetation phenology and sensitivity assessment of spectral vegetation indices to mining activities in arid/semi-arid areas

Measuring the impact of mining activities on vegetation phenology and assessing the sensitivity of vegetation indices (VIs) to it are crucial for understanding land degradation in mining areas and enhancing the carbon sink capacity following the ecological restoration of mines. To this end, we have developed a novel technical framework to quantify the impact of mining activities on vegetation, and applied it to the Bainaimiao copper mining area in Inner Mongolia. Phenological indices are extracted based on the VI time series data of Sentinel-2, and changes in phenological differences in various directions are used to quantify the impact of mining activities on vegetation. Finally, indicators such as mean difference, standard deviation, index value distribution interval, and concentration of index value distribution were selected to assess the sensitivity of the Enhanced Vegetation Index (EVI), Green Chlorophyll Index (GCI), Global Environmental Monitoring Index (GEMI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), Red-Edge Chlorophyll Index (RECI), and Soil-Adjusted Vegetation Index (SAVI) to mining activities. The results of the study show that the impact of mining activities on surrounding vegetation extends to an area three times larger than the actual mining activity area. When compared with the reference and unaffected areas, the affected area experienced a delay of approximately 10 days in seasonal vegetation development. Environmental pollution caused by the tailings pond was identified as the primary factor influencing this delay. Significant variations in the sensitivity of each VI to assess mining activities in arid/semi-arid areas were observed. Notably, GCI, GNDVI and RDVI displayed relatively high sensitivity to discrepancies in the spectral attributes of vegetation within the affected area, while SAVI reflected the overall spectral stability of the vegetation in the affected area. The research findings have the potential to provide valuable technical guidance for holistic environmental management in mining areas and hold great significance in preventing further land degradation and supporting ecological restoration in mining areas.

Utilizing Spectral, Structural and Textural Features for Estimating Oat Above-Ground Biomass Using UAV-Based Multispectral Data and Machine Learning

Accurate and timely monitoring of biomass in breeding nurseries is essential for evaluating plant performance and selecting superior genotypes. Traditional methods for phenotyping above-ground biomass in field conditions requires significant time, cost, and labor. Unmanned Aerial Vehicles (UAVs) offer a rapid and non-destructive approach for phenotyping multiple field plots at a low cost. While Vegetation Indices (VIs) extracted from remote sensing imagery have been widely employed for biomass estimation, they mainly capture spectral information and disregard the 3D canopy structure and spatial pixel relationships. Addressing these limitations, this study, conducted in 2020 and 2021, aimed to explore the potential of integrating UAV multispectral imagery-derived canopy spectral, structural, and textural features with machine learning algorithms for accurate oat biomass estimation. Six oat genotypes planted at two seeding rates were evaluated in two South Dakota locations at multiple growth stages. Plot-level canopy spectral, structural, and textural features were extracted from the multispectral imagery and used as input variables for three machine learning models: Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR). The results showed that (1) in addition to canopy spectral features, canopy structural and textural features are also important indicators for oat biomass estimation; (2) combining spectral, structural, and textural features significantly improved biomass estimation accuracy over using a single feature type; (3) machine learning algorithms showed good predictive ability with slightly better estimation accuracy shown by RFR (R2 = 0.926 and relative root mean square error (RMSE%) = 15.97%). This study demonstrated the benefits of UAV imagery-based multi-feature fusion using machine learning for above-ground biomass estimation in oat breeding nurseries, holding promise for enhancing the efficiency of oat breeding through UAV-based phenotyping and crop management practices.