Crop Water Information in Farmers’ Hand — Using Earth Observation data for Precision Water Management
Gabriel Eze
Plants absorb different amounts of water throughout their life cycle. Typically, they absorb most of their water requirement in the morning on a regular day, while during a cropping season, the absorption is at its peak in the middle when the temperature is generally warmer. The rate of water use by the plant is influenced by various factors such as rainfall, hours of sunshine, temperature, wind, and humidity. However, if the water is inadequate or unavailable in the soil, the plant will not take up its water requirement. This uncertainty in crop production can occur at any point resulting in problems like germination failure or reduction in yield potential, exacerbating the already concerning issue of food insecurity worldwide.
The number of food-insecure people worldwide is on the rise, leading to hunger and malnutrition (FAO, IFAD, WFP, 2014; Croft et al., 2016), worsened by the effects of climate change. While food and nutritional security are major global concerns, they are particularly prevalent in developing countries (Kisaka-Lwayo & Obi, 2016). Water is a fundamental requirement for crop production. It is responsible for several vital functions at the tissue level.
Agriculture is the largest water-consuming activity, accounting for about one-third of all freshwater withdrawals globally. It requires even more water to sustain the continuous demand for food fostered by population growth and changes in rainfall and temperature patterns.
To achieve sustainable on-farm water management, a responsive mechanism for measuring the water available in the soil and how much water is added & removed is necessary since we can only manage what we cannot measure. The proposed methodology combines Earth observation data and other multi-variables to evaluate soil moisture availability, differing from traditional approaches that measure water losses through soil evaporation and crop transpiration to determine available water resources. A persistent measurement of soil moisture at the pixel level presents a unique window for an efficient, precise timing of irrigation schedule and to do so at scale accurately and affordably on a crop-by-crop basis.
Improved monitoring of water availability can minimise crop vulnerabilities and maximise yield potential, thereby improving farmers’ resilience to climate change and other related uncertainties. Inevitably, expanding the capacity of farmers to produce more food for increased income at low cost, using easily accessible technologies, without further damage to the environment should be prioritised.
Theoretical Background
There are several methods deployed in calculating soil moisture content, the most common involves using moisture sensors or direct measurement (gravimetry). Similarly, remote sensing of soil moisture content is fancied by researchers and increasingly adopted by farmers. It offers some unique advantages, such as greater spatial and temporal accuracy at a lower cost and unmatched scalability. It can measure soil moisture over a broader coverage area than any conventional method efficiently and cost-effectively.
In order to take advantage of remote sensing for crop water management, it is essential to understand the crop’s water needs in terms of amount and timing. For example, most grain crops will uptake water from the soil at a slower rate in the early phenophase; this rate rapidly changes as it approaches the end of the vegetative stage and begins to decline as the reproduction phase proceeds to completion.
An interesting fact is a direct correlation between the rate of Nitrogen uptake and the rate of water uptake. Where there is inadequate soil moisture, it will impact nutrient uptake commensurately. While this can provide the needed head-up for farmers across seasons, it is particularly relevant in irrigation farming scenarios. This underscores the need for monitoring the available soil moisture at a spatio-temporal scale. Earth observation, EO (via remote sensing) makes it possible to create soil moisture maps at a high resolution; this empowers farmers with information on where a deficiency exists and when, thereby protecting yield potential and improving production efficiency.
There are up to half a dozen algorithms for generating soil moisture (SM) maps using EO data, the resulting common indices used include (but are not limited to): Leaf Area Index (LAI), Normalised Difference Water Index (NDWI), and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). Other moisture-related indices such as the Normalised Difference Moisture Index (NDMI), Enhanced Vegetation Index (EVI), Atmospherically Resistant Vegetation Index (ARVI), and Structure Insensitive Pigment Index (SIPI) have been used to establish relationships with soil moisture (SM).
The Approach
For this presentation, we will demonstrate with LAI-based soil moisture index maps and its relationship to crop health. The full details of the algorithm is not disclosed; however, the design diagram below gives an idea of the approach adopted.
It is general knowledge that the finer the soil texture is, the higher its moisture retention is (due to more pores). Besides soil texture, other soil information determines its moisture condition, particularly the total available water and the wilting point. This means the soil moisture map from remote sensing is not enough information, and we also need to consider the soil information and climate pattern on the temporal scale. That way, we can provide the farmer with complete and balanced information on soil moisture in a comprehensive way. This is important as it tells the causative factor in addition to the effect; for example, when the soil moisture map shows high water stress, it could have been a consequence of soil texture. In this way, the decision support system does not advise the farmer to water the soil (which amounts to a waste of water).
Similarly, climatic conditions also play an essential role in soil moisture content. For example, at lower temperatures, soil moisture content is higher (and vice versa). Also, soil moisture content is critical before, during and after sowing; the crop type and geographic location determine the optimal amount required. A general rule of thumb is that the optimal planting window is such a time when the rate at which moisture is added to the soil is slightly higher than the rate at which it is removed. The scenario is quite different post-planting.
Case Study / Discussion
From the above side-by-side comparison of soil moisture map and normalised difference vegetation index, there is a direct correlation between soil moisture content and crop health. Drawing this conclusion does not suggest other factors would not be at play for this scenario, such as pests, diseases, and nutrient deficiency, would not be at play in this scenario. Knowing that water stress is not a causative factor removes one item from the checklist, enabling the farmer to narrow down further the list of what could be wrong when identified, timely and efficiently.
Implementation Checklist
- Language: Python
- Messaging: Redis
- Parallel Processing: Dask
- Image manipulation: Rasterio, Rioxarray
- Shape manipulations: Fiona
- Data visualisation: Matplotlib
References
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