J.G.P.W. Clevers

Combined use of optical and microwave remote sensing data for crop growth monitoring

Abstract In this study, optical and microwave remote sensing data were used in combination for crop growth monitoring. A simple reflectance model was used for estimating leaf area index (LAI) from optical data, and a simple backscatter model was used for estimating LAI from radar data. Subsequently, the synergistic effect of using both optical and radar data for estimating LAI was analyzed by studying different data acquisition scenarios. Finally, the remote sensing models were inverted to obtain LAI estimates during the growing season for use in calibrating the crop growth model to actual growing conditions. This concept for crop growth monitoring is elucidated and illustrated with examples using ground-based and airborne data obtained during the MAC Europe 1991 campaign. Results showed that simultaneous optical and radar observations did not improve estimates of LAI over optical data alone. However, for operational applications the assumption of nonsimultaneous observations is more realistic. Results for sugar beet indicated that when periodic (about every ten days) optical recordings are available throughout most of the growing season, LAI can be monitored well and a good estimate of sugar beet yield at the end of the season is possible by using a calibrated crop growth model. When only a few recording dates with an optical sensor are available, radar recordings at L-band HH polarization or C-band W polarization gave a slight improvement of the results of crop monitoring and yield estimation compared with the optical data alone. In the absence of optical remote sensing data, radar data yielded a significant improvement in yield estimation compared with the case of no remotely observed information. This confirmed that the main advantage of radar lies in acquiring information on crop growth when other techniques (in particular optical techniques) fail.

A Framework for Monitoring Crop Growth by Combining Directional and Spectral Remote Sensing Information

Abstract For monitoring agricultural crop production, growth of crops is modeled, for example, by using simulation models. Estimates of crop growth often are inaccurate for practical field conditions. Therefore, model simulations must be improved by incorporating information on the actual growth and development of field crops, for example, by using remote sensing data. Such data can be used to initialize, calibrate, or update crop growth models, and it can yield parameter estimates to be used as direct input into growth models: 1) leaf area index (LAI), 2) leaf angle distribution (LAD), and 3) leaf colour (optical properties in the PAR region). LAI and LAD determine the amount of light interception. Leaf (or crop) color influences the fraction of absorbed photosynthetically active radiation (APAR) and the maximum (potential) rate of photosynthesis of the leaves. A framework is described for integrating optical remote sensing data from various sources in order to estimate the mentioned parameters. In this article, the above concepts for crop growth estimation are elucidated and illustrated using groundbased and airborne data obtained during the MAC Europe 1991 campaign. Quantitative information concerning both LAI and LAD was obtained by measurements at two viewing angles (using data from the CAESAR scanner in dual-look mode). The red edge index was used for estimating the leaf optical properties (using AVIRIS data). Finally, a crop growth model (SUCROS) was calibrated on time series of optical reflectance measurements to improve the estimation of crop yield.

Synergetic use of optical and microwave remote sensing data using models and specific features with respect to the sugar beet crop

Analyses radar backscatter and optical reflectance from sugar beet crops and examine the relations with the growth and development stages of sugar beet. The authors final goal is then to answer the question whether radar can monitor sugar beet growth throughout the whole growing season and whether radar measurements can contribute to synergism with optical data in predicting sugar beet yield. The information from radar remote sensing is used in a twofold manner. Firstly, biomass is estimated by inversion of the Cloud model and, secondly, the use of structure changes of the sugar beet crop on the backscatter are discussed. The Flevopolder dataset of Mac Europe 1991 is used.<<ETX>>

Estimating the fraction APAR by means of vegetation indices: a sensitivity analysis with a combined PROSPECT-SAIL model.

Abstract A sensitivity analysis is performed for both crop parameters and external factors, using the SAIL canopy reflectance model and the PROSPECT leaf reflectance model, towards the possibilities of using vegetation indices (VIs) in estimating the fraction of absorbed photosynthetically active radiation (FPAR). Results from this theoretical study show that a linear relationship may be assumed between WDVI (Weighted Difference VI) or NDVI (Normalized Difference VI) and FPAR as an approximation. External factors (soil background, ratio diffuse/total irradiation, solar zenith angle) do not have a large influence on the relationship between FPAR and the WDVI. Moreover, leaf parameters (such as leaf chlorophyll content, leaf mesophyll structure and hot spot size‐parameter) also have quite a small influence for green leaves, as concluded from simulations with the combined PROSPECT‐SAIL model. The main crop parameter influencing the relationship between WDVI and FPAR is the leaf angle distribution (LAD). So, ...