Improving the Rice Yield Estimation Using SMOS and CYGNSS GNSS-R Data
2020
Zhan, Qian | Vall-llossera, Mercè | Pablos, Miriam | Camps, Adriano | Portal, Gerard | Chaparro, David
2020 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2020), 26 September - 2 October 2020
Show more [+] Less [-]Unaffected by the atmospheric conditions and solar illumination, L-band emission and scattering are sensitive to vegetation water content and can be used to estimate crop yield. However, for rice which has an inundated period during its growing cycle, the current methods do not work due to the water under the crops. In this paper, we propose to use Global Navigation Satellite System Reflectometry (GNSS-R) signals to find how the water in rice field influence vegetation optical depth (VOD) which had been recently used to estimate the crop yield. Soil moisture (SM) and VOD in Thailand rice fields are compared to signal to noise ratio (SNR) from CYGNSS. Good correlation among them has been found. Results indicate that GNSS-R signals can be used to flag the presence of water and develop an adapted VOD algorithm that can be used to improve the estimation of rice yields
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