# First Vegetation Optical Depth Mapping from Sentinel-1 C-band SAR Data over Crop Fields
## 1 Introduction  
According to United Nations, the world population will reach 9.7 billion by 2050. As global air temperature is rising and water scarcity is pushing, maintaining food security for the next generations is certainly a challenge. Tools and solutions should then be developed to adapt agricultural practices to future climate and water conditions, which are likely to not be favorable for having enough food production and to precisely predict the impact of agricultural practices and weather changes on cropyield. Currently, several agronomic models have been developed in order predict the yield, based on several parameters including, but not limited to, weather conditions and agricultural practices. Such models can predict potential stress and thus can aid decision makers to take actions. For reliable predictions, the calibration of these models, by ground truth data, is a must. As in-situ sensors are limited to crop or field scale, remote sensing, on the other hand, is an effective tool in providing observations on larger scales, with high temporal resolution (6 days of revisit time for Sentinel). In addition to feeding crop models, remote sensing observations can save yield by early detecting the presence of crop diseases and thus could assist farmers for better interventions. Monitoring crop growth and the state of vegetation is needed for better irrigation management and for optimizing yield. Optical observations at visible and near-infrared wavelengths are commonly used for crop monitoring and crop parameter estimation [1; 2; 3; 4; 5]. However, optical observations from space cannot ensure continuous crop monitoring since they are useless in the presence of clouds. Microwave remote sensing at low frequencies is able to operate in all weather conditions and can complement optical observations by monitoring the temporal change in the Vegetation Water Content (VWC). Because a large fraction of the crop biomass consists of water, VWC is related to the amount of fresh biomass and to the vegetation water status. Several studies have shown that the Vegetation Optical Depth (VOD) derived from microwave data can be used to monitor crops [6; 7; 8; 9; 10; 11; 12; 13]. The optical depth of crops is a measure of how opaque a canopy is to microwave radiation passing through it and it is directly related to VWC as well as to instrumental parameters (incidence angle, polarization, and frequency) [6; 14; 15; 16]. Currently, several VOD products at low spatial resolution are provided from the C-band ASCAT (Advanced SCATerometer), AMSR-E & -2 (Advanced Microwave Scanning Radiometer), and from L-band radiometers such as, SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil moisture Active and Passive) [6; 7; 8; 9; 10; 11; 12; 13]. These VOD products were evaluated by several authors. For instance, [PERSON] et al. [12] and [PERSON] [10] found that changes in SMOS VOD over croplands in the USA are consistent with the crop cycle: VOD increases during the growing season and decreases during the senescence. Moreover, [PERSON] [10] assessed the relationship between SMOS VOD and crops (wheat hay and corn) optical indices including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Normalized difference Water Index (NDWI). They found that VOD is correlated with theses optical indices with a coefficient of determination (\(R^{2}\)) of about 0.33. [PERSON] et al. [18] assessed the relationship between SMAP VOD and crops yield (barley, bean, canola, corn, flaxseed, lentils, oat, pea, sorghum, soybean, and wheat) in north-central USA using principal component regressions. For the part of the study site dominated by corn and soybean (cover fraction \(>95\%\)), the first principal component (PC1) explained 78% of yield variance, whereas the part of the study site containing wheat and mixed crops PC1 explained 43% of yield variance. [PERSON] et al.