Abstract
In this study, Sentinel-2 data were used for the retrieval of three key biophysical parameters of crops: leaf area index (LAI), leaf chlorophyll content (LCC), and leaf water content (LWC) for dominant crop types in the Czech Republic, including winter wheat (Triticum aestivum), spring barley (Hordeum vulgare), winter rapeseed (Brassica napus subsp. napus), alfalfa (Medicago sativa), sugar beet (Beta vulgaris), and corn (Zea mays subsp. Mays) in different stages of crop development. Artificial neural networks were applied in combination with an approach using look-up tables that is based on PROSAIL simulations to retrieve the biophysical properties tailored for each crop type. Crop-specific PROSAIL model optimization and validation were based upon a large dataset of in situ measurements collected in 2017 and 2018 in lowland of Central Bohemia region. For LCC and LAI, respectively, low relative root mean square error (rRMSE; 25%, 37%) was achieved. Additionally, a relatively strong correlation with in situ measurements (r = 0.80) was obtained for LAI. On the contrary, the results of the LWC parameter retrieval proved to be unsatisfactory. We have developed a generic tool for biophysical monitoring of agricultural crops based on the interpretation of Sentinel-2 satellite data by inversion of the radiation transfer model. The resulting crop condition maps can serve as precision agriculture inputs for selective fertilizer and irrigation application as well as for yield potential assessment.
Highlights
IntroductionA wide range of remote-sensing applications have been developed to monitor the status and development of agricultural crops [2]
The results show that PROSAIL simulations are in accordance with observed Sentinel-2 data in the vast majority of spectral bands
A database of in situ crop biophysical characteristics was used for representative parametrization of radiative transfer model and robust validation of its inverted parameters
Summary
A wide range of remote-sensing applications have been developed to monitor the status and development of agricultural crops [2]. This has revolutionized the agriculture sector by introducing the concepts of so-called precision farming. The availability of detailed spatial information relating to, for example, crop vigor, crop heterogeneity, nitrogen fertilization needs, or crop water deficiency have enabled more effective crop management on the level of individual parcels. This can bring benefits in such forms as substantially reduced costs, higher yields, and diminished environmental pressures (e.g., by avoiding overfertilization that leads to nitrogen leaching). A review on using remote sensing for precision farming can be found for example in [3]
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