Deep Learning-Based Estimation of Crop Biophysical Parameters Using Multi-Source and Multi-Temporal Remote Sensing Observations

Abstract

Remote sensing data are considered as one of the primary data sources for precise agriculture. Several studies have demonstrated the excellent capability of radar and optical imagery for crop mapping and biophysical parameter estimation. This paper aims at modeling the crop biophysical parameters, e.g., Leaf Area Index (LAI) and biomass, using a combination of radar and optical Earth observations. We extracted several radar features from polarimetric Synthetic Aperture Radar (SAR) data and Vegetation Indices (VIs) from optical images to model crops’ LAI and dry biomass. Then, the mutual correlations between these features and Random Forest feature importance were calculated. We considered two scenarios to estimate crop parameters. First, Machine Learning (ML) algorithms, e.g., Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB), were utilized to estimate two crop biophysical parameters. To this end, crops’ dry biomass and LAI were estimated using three input data; (1) SAR polarimetric features; (2) spectral VIs; (3) integrating both SAR and optical features. Second, a deep artificial neural network was created. These input data were fed to the mentioned algorithms and evaluated using the in-situ measurements. These observations of three cash crops, including soybean, corn, and canola, have been collected over Manitoba, Canada, during the Soil Moisture Active Validation Experimental 2012 (SMAPVEX-12) campaign. The results showed that GB and XGB have great potential in parameter estimation and remarkably improved accuracy. Our results also demonstrated a significant improvement in the dry biomass and LAI estimation compared to the previous studies. For LAI, the validation Root Mean Square Error (RMSE) was reported as 0.557 m2/m2 for canola using GB, and 0.298 m2/m2 for corn using GB, 0.233 m2/m2 for soybean using XGB. RMSE was reported for dry biomass as 26.29 g/m2 for canola utilizing SVR, 57.97 g/m2 for corn using RF, and 5.00 g/m2 for soybean using GB. The results revealed that the deep artificial neural network had a better potential to estimate crop parameters than the ML algorithms.

A Comparison of Tree-Based Regression Models for Soil Moisture Estimation Using Sar Data

“…Gradient Boosted Regression Trees (GBRT) takes advantage of boosting as a statistical approach to aggregate weak learners and convert them to a single strong learner model. This model minimizes the residuals, diminishes the loss function, and optimizes the prediction by generating new decision trees and adding them in sequential steps (Bahrami et al 2021). Furthermore, the GBRT algorithm is sensitive to its parameters; hence, appropriate parameter selection, including the number of estimators, maximum depth, learning rate, loss function, and so forth assists in reaching the best algorithm implementation and final result.…”

Toward Multi-Stage Phenotyping of Soybean with Multimodal UAV Sensor Data: A Comparison of Machine Learning Approaches for Leaf Area Index Estimation

“…Some researchers have tried to improve the prediction ability of the models of LAI by fusing LiDAR data with spectral data [83]. In addition, Bahrami et al [84] constructed prediction models of LAI and biomass by combining Li-DAR and optical earth observations. Luo et al [85] estimated the biomass of short, wetland reed using a combination of LiDAR and hyperspectral data, and found that combining the LiDAR with hyperspectral data could improve the estimation accuracy of reed biomass.…”

Gaussian Process Regression Model for Crop Biophysical Parameter Retrieval from Multi-Polarized C-Band SAR Data

“…In this regard, several machine learning regression algorithms (MLRAs) have been utilized to retrieve bio and geophysical parameters from both optical [32][33][34][35][36][37] and SAR [38][39][40][41] data. In particular, machine learning algorithms attempt to find a linear or a non-linear relationship among the features (e.g., linear polarizations) and the target (e.g., PAI, WB, etc.).…”