Abstract
Abstract
Crop statistics are crucial for developing a demand-based export and import strategy to ensure a country’s sustainable food security. Remote sensing efficiently generates essential crop statistics, while ground-based supplementary sensor data offers sufficient information for crop delineation. This study explored the multispectral satellite imagery using in-situ ground-based hyperspectral reflectance phenology information as training data to delineate wheat from other competitive winter crops in Northwestern Bangladesh as a case study. Wheat spectral signatures were primarily obtained through a hand-held Spectroradiometer at various phenological stages, aligned with Sentinel-2 data availability. Five vegetation indices (VIs), namely Normalized Difference Vegetation Index (NDVI), Red-edge NDVI (RENDVI), Enhanced Vegetation Index (EVI), Greenness Chromatic Coordinate (GCC) and Soil-Adjusted Vegetation Index (SAVI), were derived from Spectroradiometer-data across six wheat growth stages: seedling, tillering, booting, flowering, grain development, and maturity. Maximum and minimum threshold values for the VIs at those six growth stages were determined from regression analysis of the values collected from Spectroradiometer and Sentinel-2. A rule-based classification technique was then used to categorize Sentinel-2 for wheat crop delineation based on those threshold values. The results revealed that maps based on NDVI, EVI, and SAVI showed overall accuracies of 83.33%, 85.18%, and 81.48%, respectively. These accuracies were found to be statistically acceptable (p < 0.05) outcomes. A positive agreement was observed when comparing the remotely sensed area at the union (4th tier administrative level) with the officially reported data of Bangladesh. This innovative method has the potential to be extended for developing phenology and area delineation for other major crops locally and globally.