Development and implementation of a multiscale biomass model using hyperspectral vegetation indices for winter wheat in the North China Plain

Crop monitoring during the growing season is important for regional management decisions and biomassprediction. The objectives of this study were to develop, improve and validate a scale independentbiomass model. Field studies were conducted in Huimin County, Shandong Province of China, duringthe 2006-2007 growing season of winter wheat (Triticum aestivum L.). The field design had a multiscaleset-up with four levels which differed in their management, such as nitrogen fertilizer inputs and cul-tivars, to create different biomass conditions: small experimental fields (L1), large experimental fields(L2), small farm fields (L3), and large farm fields (L4). L4, planted with different winter wheat varieties,was managed according to farmers' practice while L1 through L3 represented controlled field experi-ments. Multitemporal spectral measurements were taken in the fields, and biomass was sampled foreach spectral campaign. In addition, multitemporal Hyperion data were obtained in 2006 and 2007. L1field data were used to develop biomass models based on the relation between the winter wheat spectraand biomass: several published vegetation indices, including NRI, REP, OSAVI, TCI, and NDVI, were inves-tigated. A new hyperspectral vegetation index, which uses a four-band combination in the NIR and SWIRdomains, named GnyLi, was developed. Following the multiscale concept, the data of higher levels (L2through L4) were used stepwise to validate and improve the models of the lower levels, and to transferthe improved models to the next level. Lastly, the models were transferred and validated at the regionalscale using Hyperion images of 2006 and 2007. The results showed that the GnyLi and NRI models, whichwere based on the NIR and SWIR domains, performed best with R²> 0.74. All the other indices explainedless than 60% model variability. Using the Hyperion data for regionalization, GnyLi and NRI explained81-89% of the biomass variability. These results highlighted that GnyLi and NRI can be used togetherwith hyperspectral images for both plot and regional level biomass estimation. Nevertheless, additionalstudies and analyses are needed to test its replicability in other environmental conditions.