Mapping crop leaf area index at the parcel level via inverting a radiative transfer model under spatiotemporal constraints: A case study on sugarcane

Abstract

Crop leaf area index (LAI) mapping from remote sensing observations is highly demanded for regional agricultural applications, such as crop health monitoring and crop yield prediction. However, the popularly used model inversion method inevitably presents an ill-posed problem, which leads to unstable and inaccurate retrieval results. An agricultural parcel is a relatively homogeneous object due to uniform agricultural practices and similar environmental conditions. Crops inside a parcel generally present the same growth stage and similar growth status. In this study, a new method was proposed by adopting spatiotemporal constraints at the parcel level in the model inversion process for LAI retrieval. Firstly, phenology information of parcels was utilized to constrain the LAI ranges based on the established prior knowledge, which described the temporal variation of LAI during the life cycle of crop. Subsequently, spatial constraint was adopted in the model inversion through a proposed novel cost function, which assumed the spatial autocorrelation of parameters inside a parcel according to the first law of geography. Sugarcane was taken as an example to evaluate the proposed method. The method was applied to Sentinel-2 data and validated using ground-measured LAI data. The retrieved model parameters exhibited smoother spatial patterns and lower intra-parcel spatial variations through the proposed parcel-level inversion method, compared to the conventional pixel-level inversion method. Evaluations of LAI retrieval accuracy showed that the parcel-level inversion method yielded more accurate results (root mean square error (RMSE): 0.34 m2/m2; relative root mean square error (RRMSE): 20.89%), compared to the pixel-level inversion method (RMSE: 0.56 m2/m2; RRMSE: 34.53%). The spatiotemporal constraint strategy presented the ability to prevent severe overestimation and underestimation. Among the validation data set, the accuracy of the severely overestimated samples of the pixel-level method (RMSE: 0.93 m2/m2) was improved via the new method (RMSE: 0.43 m2/m2); the accuracy of the severely underestimated samples of the pixel-level method (RMSE: 0.66 m2/m2) was improved via the new method (RMSE: 0.26 m2/m2). Finally, the proposed method was applied to obtain sugarcane LAI mapping. The study demonstrates that the proposed parcel-level spatiotemporal constraint strategy improves the accuracy of LAI retrieval, has the capability of producing a highly reliable crop LAI mapping, and shows good potential for agricultural applications at regional scales.