Estimating Soil Parameters from DESIS Images using Deep Learning

Kurzfassung

There are several soil parameters that play a significant role in soil health and thus, crop production. Compared with traditional fieldwork by collecting soil samples, digital soil mapping by remote sensing data has many advantages, such as estimating soil properties efficiently in large areas. Multispectral and hyperspectral data have been used already widely for soil parameter retrieval using spectral soil models. Mostly, hyperspectral data have largely outperformed the model and prediction of soil parameters. However, the accuracy and uncertainty of both model results depend largely on the density of calibration points, which is especially problematic for large areas such as regions and countries. Therefore, new methods are needed that take into account the sparsity of calibration data during the training of the model. This work focuses on the SOC content for the whole Bavarian region in Germany (~70.000 km²). The soil data source is LFU (Bayerisches Landesamt fur Umwelt) and LUCAS 2018 (Land Use and Coverage Area Frame Survey). After data selection, we use 1171 soil samples. As for the hyperspectral images, we use DESIS data in Bavaria, whose spectral range is 400-1000 nm. We use 603 hyperspectral images in experiments. To get spectral reflectance for bare soils, we build temporal reflectance composites surrounding each soil sample from the original images. Specifically, We compute the NDVI value for each pixel and then filter pixels by NDVI threshold to filter out vegetated pixels. Temporal composites are then generated by the pixel-based averaging of the filtered images. These composites are fed into the deep learning model. The model would output the SOC value. Regarding the model framework, it consists of CNN layers followed by fully connected layers. To solve the sparsity of data availability, data augmentation, and transfer learning methodology are investigated in this work. During our experiments, we use cross-validation to evaluate the performance. Root Mean Square Error and R Square are used as the evaluation metrics.