Mapping irrigated areas from Surface Soil Moisture maps

Irrigation is a crucial practice for agriculture, especially in arid and semi-arid regions, because it considerably influences the crop production and quality. Remotely sensed data can be efficiently used to outline irrigated croplands, offering a synoptic overview of wide areas and of their extent. The most of the methods so far proposed to produce irrigated /not irrigated areas maps by remotely sensed data are based on optical images [1] More recently, the availability of operational SSM products derived from spaceborne radiometers and or scatterometers (e.g. SMOS, SMAP, ASCAT) has stimulated the investigation of Surface Soil Moisture(SSM) potential in the detection of irrigated areas [2]. One limitation of these studies is related to the coarse spatial resolution of the aforementioned operational products (typically on the order of 25 km). Here, a methodology using high resolution SSM data from Sentinel 1 images is presented for the identification of binary masks of irrigated/not-irrigated fields. It is based on the exploitation of local statistics computed, at different scales. The basic idea is that the SSM contrast between irrigated and not-irrigated areas reduces as the timespan between the irrigation event and the SAR acquisition increases. Nevertheless, a significant SSM local contrast after the water supply will last for a timespan that mainly depends on the soil type, on the amount of water received by the field and by the evapotranspiration rate. Clearly, the shorter the SAR system revisit, the higher is the potential to accurately discriminate irrigated vs not-irrigated fields. For this reason, the S1 constellation that has an exact revisit of 6 days represents an important improvement with respect to past SAR systems. In particular, local statistics are computed on the SSM maps by considering respectively n1 X n1 and n2 X n2 moving windows, surrounding each pixel, with n1 < n2. Then, a pixel X is selected as corresponding to an irrigated area, if and only if the following condition holds p_(n_1 ) (x<=X)>=p_(n_2 ) (x>X)+TH where p_(n_1 ) (x<=X) is the probability to find, in a patch surrounding X, pixels exhibiting a SSM value minor than the X one (i.e. it is the local cumulative distribution function computed on a patch of n1 dimension), p_(n_2 ) (x>X)=1- p_(n_2 ) (x<=X), where p_(n_2 ) (x<=X) is the local cumulative distribution function, computed on the patch of dimension n2, TH is a threshold value opportunely set by the user. A detailed analysis of the proposed algorithm has been carried out on two SSM maps, corresponding to the S1 images acquired respectively on 9th April and 25th August 2017, on the Riaza irrigation district (Spain). Several observations can be desumed from the experimental test. First of all, the algorithm performances are several biased by the contrast in the SSM maps between irrigated and not irrigated areas. Consequently, in very dry period, characterized by high rate of evapotranspiration, the algorithm performances are low (Overall Accuracy=64%), as can be seen on August 25th. On the contrary, in April, when the evapotranspiration rate is lower, better results have been obtained (Overall Accuracy =79%). It is also clear that there is a strong correlation between algorithm performances and the gap between irrigation time and S1 image acquisition. Also in this case, the evapotranspiration plays a key role. Other factors, such as the crop, seem to are unrelevant because different crops exhibited the same behaviour of the True Positive Rate respect to the gap between S1 acquisition and irrigation practice time. [1] Ozdogan, M. et al. Remote Sensing of Irrigated Agricolture: Opportunities and Challenges. Remote Sensing 2010, 2, 2388-2412. [2] Qiua et al. Comparison of temporal trends from multiple soil moisture data sets and precipitation: The implication of irrigation on regional soil moisture trend, International Journal of Applied Earth Observation and Geoinformation 2016, 48, 17-27.2016.