Empowering Data-Driven Farming
|Estimating Crop Water Consumption Using a Time Series of Satellite Imagery|
|Offer Rozenstein email@example.com and Josef Tanny*|
|November 22, 2018|
We are using the latest innovations in satellite remote sensing to estimate crop water use and provide timely and consistent feedback to farmers. This information is crucial for efficient irrigation management and can inform practices that increase agricultural productivity and sustainability from small to large scale farms.
The crop coefficient, which represents the crop water demand, is a useful and widely used approach for irrigation management. The crop coefficients have been shown to vary between sites and between seasons. Additionally, in cases of atypical crop development and water-use patterns caused by weather anomalies, adopting recommended crop coefficient values often results in imprecise crop water use estimations. As a result, local adaptations to recommended crop coefficients are implemented to form local coefficient tables, but even these sometimes fail to capture deviations from standard conditions due to specific fertilization, variations in crop planting density, and stress factors such as pests. In addition, the spatial variation in crop water use due to spatial heterogeneity in soil characteristics such as water holding capacity and nutrients availability is not reflected in standard coefficient tables. Accordingly, in the absence of reliable, real-time information about crop water use, there is a need for better crop coefficient estimates.
One approach to address this need is by using satellite remote sensing imagery. This technology is attractive for modeling crop water use since it provides a synoptic coverage at fixed time intervals, and can therefore monitor changes over time. Moreover, spectral indicators derived from remote sensing imagery are highly correlated with crop characteristics including biomass, Leaf Area Index (LAI), plant height, and yield. Similarly, these spectral indicators can serve as near-real-time surrogates for crop water use since they depict a similar temporal pattern. The reason for that is that both plant transpiration and light absorption increase roughly at the same rate throughout the growing season. In order to model the crop water consumption with satellite observations, we are performing evapotranspiration measurements in the field on the same days as the satellite overpass occurs.
The basic limitation of satellite remote sensing application for irrigation management is the compromise between the sensor’s revisit time and spatial resolution. Sensors with a short revisit time such as the moderate-resolution imaging spectroradiometer (MODIS) that provides daily coverage are characterized by a coarse spatial resolution (>250 m), while sensors with medium spatial resolution such as the Landsat series are characterized by longer revisit times (16 days). Cloudy conditions further reduce the temporal resolution for all optical sensors, thus posing another limitation on operational applications. Irrigation management decisions for field crops should ideally be based on a dense time series of imagery that are fine grained enough to distinguish between field plots. Commercial high spatial resolution satellite sensors are usually not employed for crop monitoring because their imagery are not public domain and come at a significant cost, rendering them too expensive for most operational agricultural applications. Therefore, in spite of remote sensing models for crop water consumption, the limited availability of imagery with suitable temporal and spatial resolutions at no or low cost hindered the development of worldwide remote sensing application for near-real-time irrigation decisions.
The successful recent deployment of the two Sentinel-2 satellites creates a unique opportunity for operational crop water consumption estimates. Sentinel-2 multispectral spaceborne imagery with a 5-day revisit time (obtained by the combination of Sentinel-2A and Sentinel-2B data) can potentially create a dense observation time series at 10-20 m spatial resolution, which would allow the application of this technique even for small fields. Sentinel-2 imagery offers an acceptable compromise between the revisit time and spatial resolution, with increased spectral abilities for vegetation monitoring compared to previous public domain spaceborne imagery. Hence, the overarching aim of this research was to develop a methodology to estimate cotton water consumption based on Sentinel-2 imagery. The key objectives of this study were to (1) estimate daily crop water consumption experimentally in the field, and (2) develop empirical models that link crop water consumption with remotely sensed spectral indicators from Sentinel-2.
Fig. 2: Field measured water consumption for cotton in this study, and the standard recommendation by the Israeli Extension Service (IES) for cotton growers in this region compared to estimates according to three models: (a) The model developed in this study; (b) Kamble et al. (2013); (c) Montgomery et al. (2015).
*Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, Volcani Center