## 3 Results
### Crop Yield Prediction
#### 3.1.2 RTM Integration with CGMs
\begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Paper** & **Summary** \\ \hline
[43] & Crop traits LAI, FAPAR, Fcover and Cab were estimated through applying neural network inversion of the PROSAIL model to observe SPOT data acquisition time and resolution (10 and 20 m) influence on wheat yield. Spatial resolution of 10 m and data acquisition between the stem elongation and booting stages of wheat were most suitable to obtain an RMSE of about 1 t ha\({}^{-1}\). \\ \hline
[23] & \
\begin{tabular}{l}
\begin{tabular}{l} \multicolumn{1}{c}{Crop Yield Prediction based on RTM and CGM integration} \\ \end{tabular} \\ \end{tabular} \\ \hline
[23] &
\begin{tabular}{l} Within the framework of the ESA-MOST Dragon 4 program, findings indicated that LAI, obtained through the inversion of RTM and subsequently integrated into a crop growth model (e.g., SAFY), offered a more precise means of assessing yields. RMSE was substantially lower for assimilation, at 1.14 t ha\({}^{-1}\), compared to 4.42 t ha\({}^{-1}\) for the open-loop model. \\ \end{tabular} \\ \hline
[19] &
\begin{tabular}{l} A novel prototype system for forecasting wheat yields with minimal data inputs was created via integrating remote sensing data to retrieve LAI using PROSAIL model inversion, as well as weather forecasts, into the InfoCrop-Wheat crop simulation model (CSM). The PROSAIL inversion yielded an RMSE of 0.56 m\({}^{2}\)/m\({}^{2}\) in LAI retrievals. A normalized error (NE) of 6–8% in grain yield was observed with measured inputs during model validation. The suggested framework demonstrated a higher NE of only 3% in dry matter and 1% in grain yield as compared to measured inputs. \\ \hline
[46] &
\begin{tabular}{l} A spatial modeling framework known as Geo-CropSim was developed, having three distinct features of pixel level model initialization, PROSAIL and EPIC growth model integration based on LAI and stress adjustment. The results indicated that the RMSE of Geo-CropSim’s yield estimates, based on USDA-NASS reported yields, was notably lower, with values of 1.22 Mg ha\({}^{-1}\) for corn and 0.46 Mg ha\({}^{-1}\) for soybeans, as compared to original EPIC estimates with RMSE values of 2.18 Mg ha\({}^{-1}\) for corn and 0.98 Mg ha\({}^{-1}\) for soybeans. \\ \hline
[51,52] &
\begin{tabular}{l} The radiation effect of aerosols on maize production was investigated from 2001 to 2014 using AErosol Robotic NETwork (AERONET) data, a radiative transfer model (68) and the Agricultural Production Systems sIMulator (APSIM) model. Results revealed that the aerosols reduced direct solar radiation, causing maize yield reduction in the range of 1.55% for the Taihu station to 21.12% in the Nanjing AERONET station. \\ \hline
[47] &
\begin{tabular}{l} A hybrid approach to retrieving FVC was calibrated on 2017–18 data through the integration of PROSAIL and the Aquacrop-OS model. The retrieval accuracy of FVC during the validation process on 2018–19 data, based on VEN\(\mu\)S images, yielded R\({}^{2}\) values ranging from 0.69 to 0.86 and RMSE values between 0.15 and 0.44. Pixelwise predictions of durum wheat yield exhibited relatively low errors, with an RMSE of 1.51 t ha\({}^{-1}\) and R\({}^{2}\) of 0.11 with a tendency of overestimation. \\ \hline
[48] &
\begin{tabular}{l} Estimates of winter wheat (Triticum aestivum) yield at the country level in China were derived using three different datasets: 30 m Landsat reflectance, 8-day 1 km MODIS surface reflectance and 8-day 30 m synthetic KS reflectance. Data was fed into the coupled WOFOST–PROSAIL model using LAI. The results showed that synthetic KS reflectance data provided the most accurate yield estimates, with R\({}^{2}\) values of 0.44, 0.39 and 0.30, and RMSE values of 598, 1288 and 595 kg ha\({}^{-1}\) for the years 2009, 2013 and 2014, respectively. \\ \hline
[54] &
\begin{tabular}{l} The Breathing Earth System Simulator (BESS) was developed via incorporating an assimilate allocation module based on satellite remote sensing data and included FLiES, a one-dimensional atmospheric radiative transfer model. \\ BESS-Rice simulated Gross Primary Productivity (GPP) and the dynamics of dry matter partitioning, and estimated rice yields with an average RMSE of 534.8 kg ha\({}^{-1}\) (7.7%) for annual yield. \\ \end{tabular} \\ \hline
[49] &
\begin{tabular}{l} Multitemporal LAI maps were generated using MODIS reflectance data through the application of an inverted RTM. \\ These LAI map products were masked out for non-rice areas identified via SAR data using MAPscape-RICE. The ORYZA crop growth model, assisted by the Rice Yield Estimation System (Rice-YES), effectively and accurately estimated rice yields with RMSE values of 0.30 and 0.46 t ha\({}^{-1}\) for the spring and summer, respectively, in 2016. \\ \end{tabular} \\ \hline
[50] &
\begin{tabular}{l} The PROSAIL model was integrated with remote sensing data and a growth model (WheatGrow) to create a LUT that correlates Vegetation Indices (VIs) with parameters (sowing date, sowing rate and nitrogen rate) based on the coupling model. Accurate predictions were achieved with three to four high-resolution images acquired during the late jointing to initial filling stages. RRMSE values of 17.8%, 20.3% and less than 10% for LAI, leaf nitrogen accumulation and grain yield were observed, respectively. \\ \end{tabular}
\end{table}