Abstract
In order to improve the simulation accuracy of directional brightness temperature (DBT) and the retrieval accuracy of component temperature, a model considering intra-row heterogeneity to simulate the DBT angular distribution over crop canopy is proposed. At individual scale, the probability of leaf appearance is inversely proportional to the distance from central stem. On the basis of this assumption, we formulated leaf area volume density (LAVD) spatial distribution at three hierarchical scales: individual scale, row scale, and scene scale. The equations for directional gap probability and bi-directional gap probability were modified to adapt the heterogeneity of row structure. Afterwards, a straightforward radiative transfer model was built based on the gap probabilities. A set of simulated data was generated by the thermal radiosity-graphics combined model (TRGM) as the benchmark to evaluate both forward simulation and inversion ability of the new model; we compared the new DBT model against an existing model assuming row as homogeneous box. With the growth of crops, the canopy structure of row crops will gradually change from row structure to continuous canopy. The new DBT model agreed with the TRGM model much better than the homogeneous row model at the middle stage of the crop growth season. The new model and the homogeneous row model achieve similar accuracy at early stage and end stage. At the middle growth stage, the new model can improve the accuracy of soil temperature retrieval. We recommend the new DBT model as an option to improve the DBT simulation and component temperature retrieval for row-planted crop canopy. In particular, the more accurate component temperatures during the middle growth stage are fundamentally important in characterizing crop water status, evapotranspiration, and soil moisture, which are subsequently critical for predicting crop productivity.
Highlights
Thermal infrared (TIR) remote sensing has wide applications for crops
A directional brightness temperature (DBT) model considering the intra-row heterogeneity is proposed. This new model formulated the trend that the probability of leaf appearance decreases gradually from center to edge, and simulates leaf area volume density (LAVD) spatial distribution at three scales, including the individual scale, row scale, and scene scale
The equations for directional gap probability and bi-directional gap probability were modified to adapt the heterogeneity of row structure
Summary
Thermal infrared (TIR) remote sensing has wide applications for crops. TIR data are widely used in land surface temperature (LST) and energy balance and evapotranspiration (ET) estimation at regional and global scales [10,11,12,13]. The deviation of observation from different angles degrades the reliability of TIR data and obstructs applications. TIR directional anisotropy provides a possibility to separate component temperatures (CTs) from the multi-angle data [20,21,22,23,24]. To well understand the underlying mechanism of the TIR directional anisotropy, directional brightness temperature (DBT) models are required to link the directional radiance and the illumination viewing geometry, vegetation architecture, and functioning. A high-precision DBT model is a prerequisite for both angular correction and CT inversion
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